BiocParallel/DESCRIPTION0000644000175200017520000000512214154342106015633 0ustar00biocbuildbiocbuildPackage: BiocParallel Type: Package Title: Bioconductor facilities for parallel evaluation Version: 1.28.3 Authors@R: c( person("Bioconductor Package Maintainer", email="maintainer@bioconductor.org", role="cre"), person("Martin", "Morgan", role="aut"), person("Jiefei", "Wang", role = "aut"), person("Valerie", "Obenchain", role="aut"), person("Michel", "Lang", email="michellang@gmail.com", role="aut"), person("Ryan", "Thompson", email="rct@thompsonclan.org", role="aut"), person("Nitesh", "Turaga", role="aut"), person("Aaron", "Lun", role = "ctb"), person("Henrik", "Bengtsson", role = "ctb")) Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. URL: https://github.com/Bioconductor/BiocParallel BugReports: https://github.com/Bioconductor/BiocParallel/issues biocViews: Infrastructure License: GPL-2 | GPL-3 SystemRequirements: C++11 Depends: methods, R (>= 3.5.0) Imports: stats, utils, futile.logger, parallel, snow Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr, batchtools, data.table Collate: AllGenerics.R DeveloperInterface.R prototype.R bploop.R ErrorHandling.R log.R bpbackend-methods.R bpisup-methods.R bplapply-methods.R bpiterate-methods.R bpstart-methods.R bpstop-methods.R BiocParallelParam-class.R bpmapply-methods.R bpschedule-methods.R bpvec-methods.R bpvectorize-methods.R bpworkers-methods.R bpaggregate-methods.R bpvalidate.R SnowParam-class.R MulticoreParam-class.R TransientMulticoreParam-class.R register.R SerialParam-class.R DoparParam-class.R SnowParam-utils.R BatchJobsParam-class.R BatchtoolsParam-class.R progress.R ipcmutex.R utilities.R rng.R bpinit.R LinkingTo: BH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_14 git_last_commit: 2f9d88a git_last_commit_date: 2021-12-07 Date/Publication: 2021-12-09 NeedsCompilation: yes Packaged: 2021-12-09 08:54:30 UTC; biocbuild Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Jiefei Wang [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb] Maintainer: Bioconductor Package Maintainer BiocParallel/NAMESPACE0000644000175200017520000000635614136050065015356 0ustar00biocbuildbiocbuilduseDynLib("BiocParallel", .registration = TRUE) import(methods) importFrom(stats, setNames, terms, runif) importFrom(utils, capture.output, find, head, relist, setTxtProgressBar, txtProgressBar) importFrom(parallel, nextRNGStream, nextRNGSubStream) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 classes ### exportClass( BiocParallelParam, MulticoreParam, SnowParam, DoparParam, SerialParam, BatchJobsParam, BatchtoolsParam, BPValidate ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export non-generic functions ### export( MulticoreParam, SnowParam, DoparParam, SerialParam, BatchJobsParam, BatchtoolsParam, ## register register, registered, bpparam, ## accessor bpnworkers, ## error handlers bptry, ## helpers bploop, # worker, manager loops multicoreWorkers, snowWorkers, batchtoolsWorkers, batchtoolsCluster, batchtoolsRegistryargs, batchtoolsTemplate, bpvalidate, bpok, bperrorTypes, bprunMPIslave, bprunMPIworker, ## ipcmutex ipcid, ipcremove, ipclock, ipctrylock, ipcunlock, ipclocked, ipcyield, ipcvalue, ipcreset ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 generics and methods defined in BiocParallel ### export( bplapply, bpvec, bpvectorize, bpmapply, bpaggregate, bpiterate, ## bp-controls bpworkers, "bpworkers<-", bpbackend, "bpbackend<-", bptasks, "bptasks<-", bpjobname, "bpjobname<-", bpstart, bpstop, bpisup, bpstopOnError, "bpstopOnError<-", bpprogressbar, "bpprogressbar<-", bpRNGseed, "bpRNGseed<-", bptimeout, "bptimeout<-", bpexportglobals, "bpexportglobals<-", bpforceGC, "bpforceGC<-", bplog, "bplog<-", bplogdir, "bplogdir<-", bpthreshold, "bpthreshold<-", bpresultdir, "bpresultdir<-", ## schedule bpschedule ) ### Same list as above. exportMethods( bplapply, bpvec, bpvectorize, bpmapply, bpaggregate, bpiterate, ## bp-controls bpworkers, "bpworkers<-", bpbackend, "bpbackend<-", bptasks, "bptasks<-", bpjobname, "bpjobname<-", bpstart, bpstop, bpisup, bpstopOnError, "bpstopOnError<-", bpprogressbar, "bpprogressbar<-", bpRNGseed, "bpRNGseed<-", bptimeout, "bptimeout<-", bplog, "bplog<-", bplogdir, "bplogdir<-", bpthreshold, "bpthreshold<-", bpresultdir, "bpresultdir<-", ## schedule bpschedule ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 methods for generics not defined in BiocParallel ### exportMethods( show ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S3 methods ### S3method(print, remote_error) S3method(print, bplist_error) S3method(bploop, lapply) S3method(bploop, iterate) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export 'developer' API for extending BiocParallelParam ### export( .BiocParallelParam_prototype, .prototype_update, .prettyPath, .send_to, .recv_any, .send, .recv, .close, .send_all, .recv_all, .bpstart_impl, .bpstop_impl, .bpworker_impl, .bplapply_impl, .bpiterate_impl, .error_worker_comm ) BiocParallel/NEWS0000644000175200017520000002517714154324272014644 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.28 ----------------------- USER VISIBLE CHANGES o (v 1.27.3) Setting `progressbar = TRUE` for SnowParam() or MulticoreParam() changes the default value of `tasks` from 0 to `.Machine$integer.max`, so that progress on each element of `X` is reported. o (v 1.27.3) `tasks` greater than `length(X)` are set to `length(X)`. Thus `.Machine$integer.max`, for instance, assures that each element of `X` is a separate task. o (v 1.27.5) Use of random numbers is robust to the distribution of jobs across tasks for SerialParam(), SnowParam(), and MulticoreParam(), for both bplapply() and bpiterate(), using the RNGseed= argument to each *Param(). The change is NOT backward compatible -- users wishing to exactly reproduce earlier results should use a previous version of the package. o (v 1.27.8) Standardize SerialParam() construct to enable setting additional fields. Standardize coercion of other BiocParallelParam types (e.g., SnowParam(), MulticoreParam()) to SerialParam() with as(., "SerialParam"). o (v. 1.27.9) By defualt, do _not_ only run garbage collection after every call to FUN(), except under MulticoreParam(). R's garbage collection algorithm only fails to do well when forked processes (i.e., MulticoreParam) assume that they are the only consumers of process memory. o (v 1.27.11) Developer-oriented functions bploop.*() arguments changed. o (v 1.27.12) Ignore set.seed() and never increment the global random number stream. This reverts a side-effect of behavior introduced in v. 1.27.5 to behavior more consistent with version 1.26. o (v 1.27.16) Better BPREDO support for previously started BPPARAM, and 'transient' BPPARAM without RNGseed. BUG FIXES o (v 1.27.10) Typo in coercion to SerialParam when only a single worker specified. https://github.com/Bioconductor/BiocParallel/issues/151 o (v 1.28.1) Fix regression in use of debug() with Serialparam. https://github.com/Bioconductor/BiocParallel/issues/128 o (v 1.28.2) Fix regression in progress bar display with bplapply(). https://github.com/Bioconductor/BiocParallel/issues/172 o (v 1.28.3) Fix default seed generation when user has non-defaultg generator. https://github.com/Bioconductor/BiocParallel/pull/176 CHANGES IN VERSION 1.26 ----------------------- USER VISIBLE CHANGES o (v 1.25.2) bpvalidate() gains an argument to control warning / error / silent signaling, and returns a 'BPValidate' object. BUG FIXES o (v 1.26.1) bptry(bplapply(X, ...)) returns a list of length X, appropriately annotated, when SerialParam(stop.on.error = TRUE). See https://github.com/Bioconductor/BiocParallel/issues/142 CHANGES IN VERSION 1.24 ----------------------- BUG FIXES o (v.1.23.1) bpvalidate() detects variables defined in parent environments; warns on use of global variables. o (v.1.23.2) bplapply() runs gc() after each evaluation of `FUN()`, so that workers do not accumulate excessive memory allocations (memory on a per-process basis is not excessive, but cluster-wise could be). See https://github.com/Bioconductor/BiocParallel/pull/124 o (v.1.24.1) Add 'topLevelEnvironment' to list of blocked global variable exports to address performance regression introduced by testthat 3.0. See https://github.com/Bioconductor/BiocParallel/issues/127 CHANGES IN VERSION 1.22 ----------------------- USER VISIBLE CHANGES o (v 1.20.2) don't advance random number stream when used 'internally'. This behavior alters reproducibility of existing scripts relying (probably implicitly) on the advancing stream. https://github.com/Bioconductor/BiocParallel/issues/110 BUG FIXES o (v 1.20.1) bplapply(), bpmapply(), bpvec() propagate names on arguments more correctly, https://github.com/Bioconductor/BiocParallel/issues/108 CHANGES IN VERSION 1.20 ----------------------- BUG FIXES o (v 1.19.2) Improve efficiency of MulticoreParam() when state does not persist across calls to bplapply(). CHANGES IN VERSION 1.18 ----------------------- USER VISIBLE CHANGES o (v 1.17.6) Initial use of registered BPPARAM does not advance random number seed, see https://stat.ethz.ch/pipermail/bioc-devel/2019-January/014526.html o (v 1.17.7) Loading package does not advance random number seed, see https://stat.ethz.ch/pipermail/bioc-devel/2019-January/014535.html o (v. 1.17.7) removed deprecated functions bplasterror(), bpresume(), bpcatchError() and field catch.error. o (v. 1.17.7) Make logdir, resultdir fields of BiocParallelParam. o (v. 1.17.7) replaced internal use of BatchJobs:::checkDir() (testing existence and read / write ability of log and other directories) with BiocParallelParam validity check. o (v. 1.17.7) expose 'developer' interface, `?DeveloperInterface` o (v. 1.17.11) on Windows, coerce `MulticoreParam(n)` to `MulticoreParam(1)` == SerialParam()` BUG FIXES o (v 1.17.4) port 1.16.3 (no '>' on SnowParam() worker end) and 1.16.4 (bpRNGseed<-() accepts NULL) o (v 1.17.5) port 1.16.4 (bpRNGseed() can reset seed to NULL), 1.16.5 (number of available cores defaults to 1 if cannot be determined). CHANGES IN VERSION 1.16 ----------------------- NEW FEATURES o (v 1.15.9) BatchtoolsParam() gains resources=list() for template file substitution. o (v 1.15.12) bpexportglobals() for all BPPARAM exports global options (i.e., base::options()) to workers. Default TRUE. BUG FIXES o (v 1.15.6) bpiterate,serial-method does not return a list() when REDUCE present (https://github.com/Bioconductor/BiocParallel/issues/77) o (v 1.15.7) bpaggregate,formula-method failed to find BPREDO (https://support.bioconductor.org/p/110784) o (v 1.15.13) bplappy,BatchtoolsParam() coerces List to list (https://github.com/Bioconductor/BiocParallel/issues/82) o (v 1.15.14) implicit loading of BiocParallel when loading a third- party package failed because reference class `initialize()` methods are not installed correctly. This bug fix results in signficant revision in the implementation, so that valid objects must be constructed through the public constructors, e.g., `BatchtoolsParam()` o (v 1.16.3) do not print '>' for each terminating SnowParam() worker o (v 1.16.4) allow bpRNGseed() to reset seed to NULL o (v 1.16.5) number of available cores defaults to 1 on machines where number of cores available cannot be determined. See https://github.com/Bioconductor/BiocParallel/issues/91. CHANGES IN VERSION 1.14 ----------------------- BUG FIXES o (v 1.13.1) bpiterate,serial-method does not unlist the result of FUN before passing to REDUCE. CHANGES IN VERSION 1.12 ----------------------- BUG FIXES o (v. 1.11.1) Change registered backend initialization to first invocation, rather than on load. o (v 1.11.8) Ensure registry is initiailized before (public) use. Issue #65 NEW FEATURES o (v. 1.11.2) bpiterate() gains a progress counter. o (v. 1.11.5) ipclock(), etc: inter-process locks and counters CHANGES IN VERSION 1.10 ---------------------- BUG FIXES o (v. 1.9.6) use of logdir= no longer tries to double-close log file. CHANGES IN VERSION 1.8 ---------------------- BUG FIXES o (v. 1.7.4) Allow more than 125 MPI nodes, https://github.com/Bioconductor/BiocParallel/issues/55 NEW FEATURES o Throttle number of cores used on Bioconductor build systems (with environment variable BBS_HOME set) to 4 CHANGES IN VERSION 1.6 ---------------------- NEW FEATURES o stop.on.error returns catchable 'remote_error' o bplapply() signals a 'bplist_error' when any element is an error. o 'bplist_error' includes an attribute 'result' containing computed results; when stop.on.error = FALSE, the result vector is parallel to (has the same geometry as) the input vector. o bpvec() signals a 'bpvec_error' when length(FUN(X)) != length(X) USER-VISIBLE CHANGES o Rename bpslaveLoop to (S3 generic) bploop o bpiterate() returns values consistent with REDUCE, rather than wrapping in list() o BatchJobsParam() passes more arguments to BatchJobs' makeRegistry(), setConfig(), submitJobs() BUG FIXES o workers=1, tasks=0 assigns all elements of X in one chunk o SerialParam() respects stop.on.error o bpmapply,ANY,* methods did not honor all arguments, particularly MoreArgs. CHANGES IN VERSION 1.2.0 ------------------------ NEW FEATURES o Add support for iterative REDUCE in .bpiterate_serial() o Refactor BiocParallelParam class: - add 'log', 'tasks', 'threshold', 'logdir', 'resultdir' fields - 'tasks' is used by SnowParam and MulticoreParam only o MulticoreParam now uses SnowParam(..., type=FORK) o Add bpvalidate() MODIFICATIONS o Add check to bipiterate() for Windows o Invoke REDUCE without '...' in .bpiterate_serial() o Update README and bpvec() man page o Change default BPPARAM to SnowParam() for Windows o Update bpiterate() man pages for Windows o Add note to vignette re: module load in template file from Thomas Girke o SnowParam: - bpmapply() now dispatches to bplapply() - remove BPRESUME - logging, gc ouput on worker - write results or logs to file - new error handling with futile.logger o Lighten the NAMESPACE by importing only parallel, snow o Modify which params are registered at load time: - Windows: SnowParam(), SerialParam() - Non-Windows: MulticoreParam(), SnowParam(), SerialParam() o bpvalidate() looks for symbols in 'fun' environment, NAMESPACE of loaded libraries, and the search path BUG FIXES o Bug fix in bpiterate_multicore(); update doc examples o Bug fix in bpiterate() in ordering results from Martin o Bug fix in .bpiterate_serial() when REDUCE is given CHANGES IN VERSION 1.0.0 ------------------------ NEW FEATURES o Add vignette sections for cluster managers, AMI o Add bpiterate generic and methods o Add REDUCE to bpiterate() o Add 'reduce.in.order' to bpiterate() MODIFICATIONS o Update vignette examples, reorganize sections o Allow 'workers' in BiocParallelParam to be character or integer o Enhance register() man page; add examples o Improve default registration for SnowParam: - max 8 cores - use detectcores() / mc.cores if available o Modify .convertToSimpleError() to convert NULL to NA_character_ BUG FIXES o Fix recursion problem for BPPARAM as list o Modify bpaggregate() to run in parallel BiocParallel/R/0000755000175200017520000000000014154324272014332 5ustar00biocbuildbiocbuildBiocParallel/R/AllGenerics.R0000644000175200017520000000660114136050065016644 0ustar00biocbuildbiocbuildsetGeneric("bplapply", signature=c("X", "BPPARAM"), function(X, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) standardGeneric("bplapply")) setGeneric("bpmapply", signature=c("FUN", "BPPARAM"), function(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) standardGeneric("bpmapply")) setGeneric("bpiterate", signature=c("ITER", "FUN", "BPPARAM"), function(ITER, FUN, ..., BPPARAM=bpparam()) standardGeneric("bpiterate")) setGeneric("bpvec", signature=c("X", "BPPARAM"), function(X, FUN, ..., AGGREGATE=c, BPREDO=list(), BPPARAM=bpparam()) standardGeneric("bpvec")) setGeneric("bpvectorize", function(FUN, ..., BPREDO=list(), BPPARAM=bpparam()) standardGeneric("bpvectorize")) setGeneric("bpaggregate", signature=c("x", "BPPARAM"), function(x, ..., BPREDO=list(), BPPARAM=bpparam()) standardGeneric("bpaggregate")) ## ## accessors ## setGeneric("bpworkers", function(x) standardGeneric("bpworkers")) setGeneric("bpworkers<-", function(x, value) standardGeneric("bpworkers<-")) setGeneric("bptasks", function(x) standardGeneric("bptasks")) setGeneric("bptasks<-", function(x, value) standardGeneric("bptasks<-")) setGeneric("bpjobname", function(x) standardGeneric("bpjobname")) setGeneric("bpjobname<-", function(x, value) standardGeneric("bpjobname<-")) setGeneric("bpRNGseed", function(x) standardGeneric("bpRNGseed")) setGeneric("bpRNGseed<-", function(x, value) standardGeneric("bpRNGseed<-")) setGeneric("bpforceGC", function(x) standardGeneric("bpforceGC")) setGeneric("bpforceGC<-", function(x, value) standardGeneric("bpforceGC<-")) ## errors setGeneric("bpstopOnError", function(x) standardGeneric("bpstopOnError")) setGeneric("bpstopOnError<-", function(x, value) standardGeneric("bpstopOnError<-")) ## logging / progress setGeneric("bpprogressbar", function(x) standardGeneric("bpprogressbar")) setGeneric("bpprogressbar<-", function(x, value) standardGeneric("bpprogressbar<-")) setGeneric("bptimeout", function(x) standardGeneric("bptimeout")) setGeneric("bptimeout<-", function(x, value) standardGeneric("bptimeout<-")) setGeneric("bpexportglobals", function(x) standardGeneric("bpexportglobals")) setGeneric("bpexportglobals<-", function(x, value) standardGeneric("bpexportglobals<-")) setGeneric("bplog", function(x) standardGeneric("bplog")) setGeneric("bplog<-", function(x, value) standardGeneric("bplog<-")) setGeneric("bplogdir", function(x) standardGeneric("bplogdir")) setGeneric("bplogdir<-", function(x, value) standardGeneric("bplogdir<-")) setGeneric("bpthreshold", function(x) standardGeneric("bpthreshold")) setGeneric("bpthreshold<-", function(x, value) standardGeneric("bpthreshold<-")) setGeneric("bpresultdir", function(x) standardGeneric("bpresultdir")) setGeneric("bpresultdir<-", function(x, value) standardGeneric("bpresultdir<-")) ## control setGeneric("bpstart", function(x, ...) standardGeneric("bpstart")) setGeneric("bpstop", function(x) standardGeneric("bpstop")) setGeneric("bpisup", function(x) standardGeneric("bpisup")) setGeneric("bpbackend", function(x) standardGeneric("bpbackend")) setGeneric("bpbackend<-", function(x, value) standardGeneric("bpbackend<-")) ## scheduling setGeneric("bpschedule", function(x) standardGeneric("bpschedule")) BiocParallel/R/BatchJobsParam-class.R0000644000175200017520000001362014136050065020376 0ustar00biocbuildbiocbuild### ========================================================================= ### BatchJobsParam objects ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### .BatchJobsParam_prototype <- c( list( reg.pars = list(), submit.pars = list(), conf.pars = list(), cleanup = logical() ), .BiocParallelParam_prototype ) .BatchJobsParam <- setRefClass("BatchJobsParam", contains="BiocParallelParam", fields=list( reg.pars="list", submit.pars="list", conf.pars="list", cleanup="logical"), methods=list( show = function() { ## TODO more output callSuper() cat(" cleanup: ", .self$cleanup, "\n", sep="") }) ) BatchJobsParam <- function(workers=NA_integer_, cleanup=TRUE, work.dir=getwd(), stop.on.error=TRUE, seed=NULL, resources=NULL, conffile=NULL, cluster.functions=NULL, progressbar=TRUE, jobname = "BPJOB", reg.pars=list(seed=seed, work.dir=work.dir), conf.pars=list(conffile=conffile, cluster.functions=cluster.functions), submit.pars=list(resources=resources), ...) { if (!requireNamespace("BatchJobs", quietly=TRUE)) stop("BatchJobsParam() requires the 'BatchJobs' package") not_null <- Negate(is.null) reg.pars <- Filter(not_null, reg.pars) submit.pars <- Filter(not_null, submit.pars) conf.pars <- Filter(not_null, conf.pars) ## save user config and reset it on exit prev.config <- BatchJobs::getConfig() on.exit(do.call(BatchJobs::setConfig, prev.config)) if (!is.null(conf.pars$conffile)) BatchJobs::loadConfig(conf.pars$conffile) new.conf <- unclass(do.call( BatchJobs::setConfig, conf.pars[setdiff(names(conf.pars), "conffile")] )) workers <- if (is.na(workers)) { getNumberCPUs <- function(conf) { x <- environment(new.conf$cluster.functions$submitJob)$workers vapply(x, "[[", integer(1L), "ncpus") } cf.name <- new.conf$cluster.functions$name if (is.null(cf.name)) { NA_integer_ } else { switch(cf.name, Multicore=getNumberCPUs(new.conf), SSH=sum(getNumberCPUs(new.conf)), NA_integer_) } } else as.integer(workers) prototype <- .prototype_update( .BatchJobsParam_prototype, reg.pars=reg.pars, submit.pars=submit.pars, conf.pars=conf.pars, workers=workers, cleanup=cleanup, stop.on.error=stop.on.error, progressbar=progressbar, jobname=jobname ) do.call(.BatchJobsParam, prototype) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - control ### setMethod("bpschedule", "BatchJobsParam", function(x) { !getOption("BatchJobs.on.slave", FALSE) }) setMethod("bpisup", "BatchJobsParam", function(x) TRUE) setMethod("bpbackend", "BatchJobsParam", function(x) BatchJobs::getConfig()) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - evaluation ### setMethod("bplapply", c("ANY", "BatchJobsParam"), function(X, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) if (!length(X)) return(.rename(list(), X)) if (!bpschedule(BPPARAM)) return(bplapply(X, FUN, ..., BPPARAM=SerialParam())) idx <- .redo_index(X, BPREDO) if (any(idx)) X <- X[idx] nms <- names(X) ## restore current settings prev.bp <- getOption("BBmisc.ProgressBar.style") on.exit(options(BBmisc.ProgressBar.style=prev.pb)) prev.config <- BatchJobs::getConfig() on.exit(BatchJobs::setConfig(conf=prev.config), add=TRUE) pb <- c("off", "text")[bpprogressbar(BPPARAM)+1L] prev.pb <- options(BBmisc.ProgressBar.style=pb) ## switch config BatchJobs::setConfig(conf=BPPARAM$conf.pars) ## reg.pars reg.pars <- c(list(id=bpjobname(BPPARAM), skip=FALSE), BPPARAM$reg.pars) if (is.null(reg.pars$file.dir)) reg.pars$file.dir <- file.path(reg.pars$work.dir, tempfile("BiocParallel_tmp_", "")) if (BPPARAM$cleanup) on.exit(unlink(reg.pars$file.dir, recursive=TRUE), add=TRUE) ## FUN FUN <- .composeTry(FUN, bplog(BPPARAM), bpstopOnError(BPPARAM), as.error=FALSE, timeout=bptimeout(BPPARAM), exportglobals=bpexportglobals(BPPARAM)) WRAP <- function(.x, .FUN, .MoreArgs) do.call(.FUN, c(list(.x), .MoreArgs)) res <- suppressMessages({ ## make / map / submit / wait/ load reg <- do.call(BatchJobs::makeRegistry, reg.pars) ids <- BatchJobs::batchMap( reg, WRAP, X, more.args=list(.FUN=FUN, .MoreArgs=list(...))) submit.pars <- c(list(reg=reg), BPPARAM$submit.pars) submit.pars$ids <- if (is.na(bpnworkers(BPPARAM))) { ids } else BBmisc::chunk(ids, n.chunks=bpnworkers(BPPARAM), shuffle=TRUE) do.call(BatchJobs::submitJobs, submit.pars) BatchJobs::waitForJobs(reg, ids, timeout=30L * 24L * 60L * 60L, stop.on.error=bpstopOnError(BPPARAM)) BatchJobs::loadResults(reg, ids, use.names="none") }) ## post-process names(res) <- nms if (any(idx)) { BPREDO[idx] <- res res <- BPREDO } ok <- bpok(res) if (!all(ok)) { ## HACK: promote conditions to errors res[!ok] <- lapply(res[!ok], function(x) { class(x) <- c(class(x)[-length(class(x))], c("error", "condition")) x }) stop(.error_bplist(res)) } res }) setMethod("bpiterate", c("ANY", "ANY", "BatchJobsParam"), function(ITER, FUN, ..., BPPARAM=bpparam()) { stop("bpiterate not supported for BatchJobsParam") }) BiocParallel/R/BatchtoolsParam-class.R0000644000175200017520000003161314136050065020643 0ustar00biocbuildbiocbuild### ================================================================ ### BatchtoolsParam objects ### ---------------------------------------------------------------- .BATCHTOOLS_CLUSTERS <- c( "socket", "multicore", "interactive", "sge", "slurm", "lsf", "openlava", "torque" ) ### ------------------------------------------------- ### Helper functions ### batchtoolsWorkers <- function(cluster = batchtoolsCluster()) { switch( match.arg(cluster, .BATCHTOOLS_CLUSTERS), interactive = 1L, socket = snowWorkers("SOCK"), multicore = multicoreWorkers(), stop("specify number of workers for '", cluster, "'") ) } batchtoolsCluster <- function(cluster) { if (missing(cluster)) { if (.Platform$OS.type == "windows") { cluster <- "socket" } else { cluster <- "multicore" } } else { cluster <- match.arg(cluster, .BATCHTOOLS_CLUSTERS) } cluster } .batchtoolsClusterAvailable <- function(cluster) { switch( cluster, socket = TRUE, multicore = .Platform$OS.type != "windows", interactive = TRUE, sge = suppressWarnings(system2("qstat", stderr=NULL, stdout=NULL) != 127L), slurm = suppressWarnings(system2("squeue", stderr=NULL, stdout=NULL) != 127L), lsf = suppressWarnings(system2("bjobs", stderr=NULL, stdout=NULL) != 127L), openlava = suppressWarnings(system2("bjobs", stderr=NULL, stdout=NULL) != 127L), torque = suppressWarnings(system2("qselect", stderr=NULL, stdout=NULL) != 127L), .stop( "unsupported cluster type '", cluster, "'; ", "supported types (when available):\n", " ", paste0("'", .BATCHTOOLS_CLUSTERS, "'", collapse = ", ") ) ) } batchtoolsTemplate <- function(cluster) { if (!cluster %in% .BATCHTOOLS_CLUSTERS) stop("unsupported cluster type '", cluster, "'") if (cluster %in% c("socket", "multicore", "interactive")) return(NA_character_) message("using default '", cluster, "' template in batchtools.") if (cluster == "torque") tmpl <- "torque-lido.tmpl" else tmpl <- sprintf("%s-simple.tmpl", tolower(cluster)) ## return template system.file("templates", tmpl, package="batchtools") } batchtoolsRegistryargs <- function(...) { args <- list(...) ## our defaults... registryargs <- as.list(formals(batchtools::makeRegistry)) registryargs$file.dir <- tempfile(tmpdir=getwd()) registryargs$conf.file <- registryargs$seed <- NULL registryargs$make.default <- FALSE ## ...modified by user registryargs[names(args)] <- args registryargs } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### setOldClass("Registry") setOldClass(c("NULLRegistry", "Registry")) .NULLRegistry <- function() { structure(list(), class=c("NULLRegistry", "Registry")) } print.NULLRegistry <- function(x, ...) { cat("NULL Job Registry\n") } setOldClass("ClusterFunctions") .BatchtoolsParam_prototype <- c( list( cluster = NA_character_, template = NA_character_, registry = .NULLRegistry(), registryargs = list(), saveregistry = FALSE, resources = list() ), .BiocParallelParam_prototype ) .BatchtoolsParam <- setRefClass( "BatchtoolsParam", contains="BiocParallelParam", fields = list( cluster = "character", template = "character", registry = "Registry", registryargs = "list", saveregistry = "logical", resources = "list" ), methods = list( show = function() { callSuper() .registryargs <- .bpregistryargs(.self) .resources <- .bpresources(.self) cat(" cluster type: ", bpbackend(.self), "\n", .prettyPath(" template", .bptemplate(.self)), "\n registryargs:", paste0("\n ", names(.registryargs), ": ", .registryargs), "\n saveregistry: ", .bpsaveregistry(.self), "\n resources:", if (length(.resources)) paste0("\n ", names(.resources), ": ", .resources), "\n", sep="") } ) ) BatchtoolsParam <- function( workers = batchtoolsWorkers(cluster), ## Provide either cluster or template cluster = batchtoolsCluster(), registryargs = batchtoolsRegistryargs(), saveregistry = FALSE, resources = list(), template = batchtoolsTemplate(cluster), stop.on.error = TRUE, progressbar=FALSE, RNGseed = NA_integer_, timeout= 30L * 24L * 60L * 60L, exportglobals=TRUE, log=FALSE, logdir=NA_character_, resultdir=NA_character_, jobname = "BPJOB" ) { if (!requireNamespace("batchtools", quietly=TRUE)) stop("BatchtoolsParam() requires 'batchtools' package") if (!.batchtoolsClusterAvailable(cluster)) stop("'", cluster, "' supported but not available on this machine") if (length(resources) && is.null(names(resources))) stop("'resources' must be a named list") prototype <- .prototype_update( .BatchtoolsParam_prototype, workers = as.integer(workers), cluster = cluster, registry = .NULLRegistry(), registryargs = registryargs, saveregistry = saveregistry, resources = resources, jobname = jobname, progressbar = progressbar, log = log, logdir = logdir, resultdir = resultdir, stop.on.error = stop.on.error, timeout = as.integer(timeout), exportglobals = exportglobals, RNGseed = as.integer(RNGseed), template = template ) x <- do.call(.BatchtoolsParam, prototype) validObject(x) x } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity ### setValidity("BatchtoolsParam", function(object) { msg <- NULL if (!bpbackend(object) %in% .BATCHTOOLS_CLUSTERS) { types <- paste(.BATCHTOOLS_CLUSTERS, collape = ", ") msg <- c(msg, paste("'cluster' must be one of", types)) } if (is.null(msg)) TRUE else msg }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - control ### setMethod("bpisup", "BatchtoolsParam", function(x) { !is(x$registry, "NULLRegistry") }) .bpregistryargs <- function(x) { x$registryargs } .bpsaveregistry <- function(x) { x$saveregistry } .bpsaveregistry_path <- function(x) { ## update registry location pattern <- "\\-[0-9]+$" file.dir <- .bpregistryargs(x)$file.dir dirname <- dirname(file.dir) basename <- basename(file.dir) dirs <- dir(dirname, paste0(basename, pattern)) n <- 0L if (length(dirs)) n <- max(as.integer(sub(".*\\-", "", dirs))) file.path(dirname, paste0(basename, "-", n + 1L)) } .bpresources <- function(x) { x$resources } .bptemplate <- function(x) { x$template } .composeBatchtools <- function(FUN) { force(FUN) function(fl, ...) { x <- readRDS(fl) FUN(x, ...) } } setMethod("bpbackend", "BatchtoolsParam", function(x) { x$cluster }) setMethod("bpstart", "BatchtoolsParam", function(x) { if (bpisup(x)) return(invisible(x)) cluster <- bpbackend(x) registryargs <- .bpregistryargs(x) oopt <- options(batchtools.verbose = FALSE) on.exit(options(oopt)) seed <- bpRNGseed(x) if (!is.na(seed)) registryargs$seed <- seed if (.bpsaveregistry(x)) { ## the registry$file.dir gets -0, -1, -2... for each bpstart on the ## same parameter registryargs$file.dir <- .bpsaveregistry_path(x) } registry <- do.call(batchtools::makeRegistry, registryargs) registry$cluster.functions <- switch( cluster, interactive = batchtools::makeClusterFunctionsInteractive(), socket = batchtools::makeClusterFunctionsSocket(bpnworkers(x)), multicore = batchtools::makeClusterFunctionsMulticore(bpnworkers(x)), sge = batchtools::makeClusterFunctionsSGE(template = .bptemplate(x)), ## Add mutliple cluster support slurm = batchtools::makeClusterFunctionsSlurm(template=.bptemplate(x)), lsf = batchtools::makeClusterFunctionsLSF(template=.bptemplate(x)), openlava = batchtools::makeClusterFunctionsOpenLava( template=.bptemplate(x) ), torque = batchtools::makeClusterFunctionsTORQUE( template=.bptemplate(x) ), default = stop("unsupported cluster type '", cluster, "'") ) x$registry <- registry # toggles bpisup() invisible(x) }) setMethod("bpstop", "BatchtoolsParam", function(x) { wait <- getOption("BIOCPARALLEL_BATCHTOOLS_REMOVE_REGISTRY_WAIT", 5) if (!.bpsaveregistry(x)) suppressMessages({ batchtools::removeRegistry(wait = wait, reg = x$registry) }) x$registry <- .NULLRegistry() # toggles bpisup() invisible(x) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - evaluation ### setMethod("bplapply", c("ANY", "BatchtoolsParam"), function(X, FUN, ..., BPREDO = list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) if (!length(X)) return(.rename(list(), X)) if (is(X, "List")) ## hack; issue 82 X <- as.list(X) idx <- .redo_index(X, BPREDO) if (any(idx)) X <- X[idx] nms <- names(X) ## start / stop cluster if (!bpisup(BPPARAM)) { BPPARAM <- bpstart(BPPARAM) on.exit(bpstop(BPPARAM), TRUE) } ## progressbar / verbose if (bpprogressbar(BPPARAM)) { opts <- options( BBmisc.ProgressBar.style="text", batchtools.verbose = TRUE ) on.exit({ ## message("") # clear progress bar options(opts) }, TRUE) } else { opts <- options( BBmisc.ProgressBar.style="off", batchtools.verbose = FALSE ) on.exit(options(opts), TRUE) } registry <- BPPARAM$registry FUN <- .composeTry( FUN, bplog(BPPARAM), bpstopOnError(BPPARAM), timeout=bptimeout(BPPARAM), exportglobals=bpexportglobals(BPPARAM) ) ## Make registry / map / submit / wait / load ids = batchtools::batchMap( fun=FUN, X, more.args = list(...), reg = registry ) ids$chunk = batchtools::chunk(ids$job.id, n.chunks = bpnworkers(BPPARAM)) batchtools::submitJobs( ids = ids, resources = .bpresources(BPPARAM), reg = registry ) batchtools::waitForJobs( ids = ids, reg = registry, timeout = bptimeout(BPPARAM), stop.on.error = bpstopOnError(BPPARAM) ) res <- batchtools::reduceResultsList(ids = ids, reg = registry) ## Copy logs from log dir to bplogdir before clearing registry if (bplog(BPPARAM) && !is.na(bplogdir(BPPARAM))) { logs <- file.path(.bpregistryargs(BPPARAM)$file.dir, "logs") ## Create log dir dir.create(bplogdir(BPPARAM)) ## Recursive copy logs file.copy(logs, bplogdir(BPPARAM) , recursive=TRUE, overwrite=TRUE) } ## Clear registry if (bpprogressbar(BPPARAM)) message("Clearing registry ...") if (!.bpsaveregistry(BPPARAM)) ## WARNING Save a registry in a folder with extension, ## _saved_registry. BatchtoolsParam('saveregistry=TRUE') option ## should be set only when debugging. This can be extremely ## time and space intensive. suppressMessages({ batchtools::clearRegistry(reg=registry) }) if (!is.null(res)) names(res) <- nms if (any(idx)) { BPREDO[idx] <- res res <- BPREDO } if (!all(bpok(res))) stop(.error_bplist(res)) res }) setMethod("bpiterate", c("ANY", "ANY", "BatchtoolsParam"), function(ITER, FUN, ..., REDUCE, init, reduce.in.order=FALSE, BPPARAM=bpparam()) { ITER <- match.fun(ITER) FUN <- match.fun(FUN) if (missing(REDUCE)) { if (reduce.in.order) stop("REDUCE must be provided when 'reduce.in.order = TRUE'") if (!missing(init)) stop("REDUCE must be provided when 'init' is given") } if (!bpschedule(BPPARAM) || bpnworkers(BPPARAM) == 1L) { param <- as(BPPARAM, "SerialParam") return( bpiterate(ITER, FUN, ..., REDUCE=REDUCE, init=init, BPPARAM=param) ) } ## start / stop cluster if (!bpisup(BPPARAM)) { bpstart(BPPARAM) on.exit(bpstop(BPPARAM)) } ## composeTry FUN <- .composeTry( FUN, bplog(BPPARAM), bpstopOnError(BPPARAM), timeout=bptimeout(BPPARAM), exportglobals=bpexportglobals(BPPARAM) ) FUN <- .composeBatchtools(FUN) ## Call batchtoolsIterate with arguments bploop(structure(list(), class="iterate_batchtools"), ITER, FUN, BPPARAM, REDUCE, init, reduce.in.order, ...) }) BiocParallel/R/BiocParallelParam-class.R0000644000175200017520000002076114136050065021074 0ustar00biocbuildbiocbuild### ========================================================================= ### BiocParallelParam objects ### ------------------------------------------------------------------------- .BiocParallelParam_prototype <- list( workers=0, tasks=0L, jobname="BPJOB", log=FALSE, logdir = NA_character_, threshold="INFO", resultdir = NA_character_, stop.on.error=TRUE, timeout=30L * 24L * 60L * 60L, # 30 days exportglobals=TRUE, progressbar=FALSE, RNGseed=NULL, RNGstream = NULL, force.GC = FALSE ) .BiocParallelParam <- setRefClass("BiocParallelParam", contains="VIRTUAL", fields=list( workers="ANY", tasks="integer", jobname="character", progressbar="logical", ## required for composeTry log="logical", logdir = "character", threshold="character", resultdir = "character", stop.on.error="logical", timeout="integer", exportglobals="logical", RNGseed = "ANY", # NULL or integer(1) RNGstream = "ANY", # NULL or integer(); internal use only force.GC = "logical", ## cluster management .finalizer_env = "environment", .uid = "character" ), methods=list( show = function() { cat("class: ", class(.self), "\n", " bpisup: ", bpisup(.self), "; bpnworkers: ", bpnworkers(.self), "; bptasks: ", bptasks(.self), "; bpjobname: ", bpjobname(.self), "\n", " bplog: ", bplog(.self), "; bpthreshold: ", bpthreshold(.self), "; bpstopOnError: ", bpstopOnError(.self), "\n", " bpRNGseed: ", bpRNGseed(.self), "; bptimeout: ", bptimeout(.self), "; bpprogressbar: ", bpprogressbar(.self), "\n", " bpexportglobals: ", bpexportglobals(.self), "; bpforceGC: ", bpforceGC(.self), "\n", .prettyPath(" bplogdir", bplogdir(.self)), "\n", .prettyPath(" bpresultdir", bpresultdir(.self)), "\n", sep="") }) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity ### setValidity("BiocParallelParam", function(object) { msg <- NULL ## workers and tasks workers <- bpworkers(object) if (is.numeric(workers)) if (length(workers) != 1L || workers < 0) msg <- c(msg, "'workers' must be integer(1) and >= 0") tasks <- bptasks(object) if (!is.numeric(tasks)) msg <- c(msg, "bptasks(BPPARAM) must be an integer") if (length(tasks) > 1L) msg <- c(msg, "length(bptasks(BPPARAM)) must be == 1") if (is.character(workers)) { if (length(workers) < 1L) msg <- c(msg, "length(bpworkers(BPPARAM)) must be > 0") if (tasks > 0L && tasks < workers) msg <- c(msg, "number of tasks is less than number of workers") } if (!.isTRUEorFALSE(bpexportglobals(object))) msg <- c(msg, "'bpexportglobals' must be TRUE or FALSE") if (!.isTRUEorFALSE(bplog(object))) msg <- c(msg, "'bplog' must be logical(1)") ## log / logdir dir <- bplogdir(object) if (length(dir) != 1L || !is(dir, "character")) { msg <- c(msg, "'logdir' must be character(1)") } else if (!is.na(dir)) { if (!bplog(object)) msg <- c(msg, "'log' must be TRUE when 'logdir' is given") if (!.dir_valid_rw(dir)) msg <- c(msg, "'logdir' must exist with read / write permission") } ## resultdir dir <- bpresultdir(object) if (length(dir) != 1L || !is(dir, "character")) { msg <- c(msg, "'resultdir' must be character(1)") } else if (!is.na(dir) && !.dir_valid_rw(dir)) { msg <- c(msg, "'resultdir' must exist with read / write permissions") } levels <- c("TRACE", "DEBUG", "INFO", "WARN", "ERROR", "FATAL") threshold <- bpthreshold(object) if (length(threshold) > 1L) { msg <- c(msg, "'bpthreshold' must be character(0) or character(1)") } else if ((length(threshold) == 1L) && (!threshold %in% levels)) { txt <- sprintf("'bpthreshold' must be one of %s", paste(sQuote(levels), collapse=", ")) msg <- c(msg, paste(strwrap(txt, indent=2, exdent=2), collapse="\n")) } if (!.isTRUEorFALSE(bpstopOnError(object))) msg <- c(msg, "'bpstopOnError' must be TRUE or FALSE") if (!.isTRUEorFALSE(bpforceGC(object))) msg <- c(msg, "'force.GC' must be TRUE or FALSE") if (is.null(msg)) TRUE else msg }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters / Setters ### setMethod("bpworkers", "BiocParallelParam", function(x) { x$workers }) setMethod("bptasks", "BiocParallelParam", function(x) { x$tasks }) setReplaceMethod("bptasks", c("BiocParallelParam", "numeric"), function(x, value) { x$tasks <- as.integer(value) x }) setMethod("bpjobname", "BiocParallelParam", function(x) { x$jobname }) setReplaceMethod("bpjobname", c("BiocParallelParam", "character"), function(x, value) { x$jobname <- value x }) setMethod("bplog", "BiocParallelParam", function(x) { x$log }) setMethod("bplogdir", "BiocParallelParam", function(x) { x$logdir }) setReplaceMethod("bplogdir", c("BiocParallelParam", "character"), function(x, value) { if (bpisup(x)) stop("use 'bpstop()' before setting 'bplogdir()'") x$logdir <- value validObject(x) x }) setMethod("bpthreshold", "BiocParallelParam", function(x) { x$threshold }) setMethod("bpresultdir", "BiocParallelParam", function(x) { x$resultdir }) setReplaceMethod("bpresultdir", c("BiocParallelParam", "character"), function(x, value) { if (bpisup(x)) stop("use 'bpstop()' before setting 'bpresultdir()'") x$resultdir <- value validObject(x) x }) setMethod("bptimeout", "BiocParallelParam", function(x) { x$timeout }) setReplaceMethod("bptimeout", c("BiocParallelParam", "numeric"), function(x, value) { x$timeout <- as.integer(value) x }) setMethod("bpexportglobals", "BiocParallelParam", function(x) { x$exportglobals }) setReplaceMethod("bpexportglobals", c("BiocParallelParam", "logical"), function(x, value) { x$exportglobals <- value validObject(x) x }) setMethod("bpstopOnError", "BiocParallelParam", function(x) { x$stop.on.error }) setReplaceMethod("bpstopOnError", c("BiocParallelParam", "logical"), function(x, value) { x$stop.on.error <- value validObject(x) x }) setMethod("bpprogressbar", "BiocParallelParam", function(x) { x$progressbar }) setReplaceMethod("bpprogressbar", c("BiocParallelParam", "logical"), function(x, value) { x$progressbar <- value validObject(x) x }) setMethod("bpRNGseed", "BiocParallelParam", function(x) { x$RNGseed }) setReplaceMethod("bpRNGseed", c("BiocParallelParam", "NULL"), function(x, value) { x$RNGseed <- NULL validObject(x) x }) setReplaceMethod("bpRNGseed", c("BiocParallelParam", "numeric"), function(x, value) { x$RNGseed <- as.integer(value) validObject(x) x }) .RNGstream <- function(x) { x$RNGstream } `.RNGstream<-` <- function(x, value) { value <- as.integer(value) if (anyNA(value)) stop("[internal] RNGstream value could not be coerced to integer") x$RNGstream <- value x } .bpnextRNGstream <- function(x) { ## initialize or get the next random number stream; increment the ## stream only in bpstart_impl .RNGstream(x) <- .rng_next_stream(.RNGstream(x)) } setMethod("bpforceGC", "BiocParallelParam", function(x) { x$force.GC }) setReplaceMethod("bpforceGC", c("BiocParallelParam", "numeric"), function(x, value) { x$force.GC <- as.logical(value) validObject(x) x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - evaluation ### setMethod("bpstart", "BiocParallelParam", .bpstart_impl) setMethod("bpstop", "BiocParallelParam", .bpstop_impl) setMethod("bplapply", c("ANY", "BiocParallelParam"), .bplapply_impl) setMethod("bpiterate", c("ANY", "ANY", "BiocParallelParam"), .bpiterate_impl) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Helpers ### ## taken from S4Vectors .isTRUEorFALSE <- function (x) { is.logical(x) && length(x) == 1L && !is.na(x) } BiocParallel/R/DeveloperInterface.R0000644000175200017520000000370614136050065020225 0ustar00biocbuildbiocbuild## ## see NAMESPACE section for definitive exports ## ## server setGeneric( ".send_to", function(backend, node, value) standardGeneric(".send_to"), signature = "backend" ) setGeneric( ".recv_any", function(backend) standardGeneric(".recv_any"), signature = "backend" ) setGeneric( ".send_all", function(backend, value) standardGeneric(".send_all"), signature = "backend" ) setGeneric( ".recv_all", function(backend) standardGeneric(".recv_all"), signature = "backend" ) ## client setGeneric( ".send", function(worker, value) standardGeneric(".send"), signature = "worker" ) setGeneric( ".recv", function(worker) standardGeneric(".recv"), signature = "worker" ) setGeneric( ".close", function(worker) standardGeneric(".close"), signature = "worker" ) ## default implementation -- SNOW backend setMethod( ".send_all", "ANY", function(backend, value) { for (node in seq_along(backend)) .send_to(backend, node, value) }) setMethod( ".recv_all", "ANY", function(backend) { replicate(length(backend), .recv_any(backend), simplify=FALSE) }) setMethod( ".send_to", "ANY", function(backend, node, value) { parallel:::sendData(backend[[node]], value) TRUE }) setMethod( ".recv_any", "ANY", function(backend) { tryCatch({ parallel:::recvOneData(backend) }, error = function(e) { ## indicate error, but do not stop .error_worker_comm(e, "'.recv_any()' data failed") }) }) setMethod( ".send", "ANY", function(worker, value) { parallel:::sendData(worker, value) }) setMethod( ".recv", "ANY", function(worker) { tryCatch({ parallel:::recvData(worker) }, error = function(e) { ## indicate error, but do not stop .error_worker_comm(e, "'.recv()' data failed") }) }) setMethod( ".close", "ANY", function(worker) { parallel:::closeNode(worker) }) BiocParallel/R/DoparParam-class.R0000644000175200017520000000562114136050065017606 0ustar00biocbuildbiocbuild### ========================================================================= ### DoparParam objects ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### .DoparParam_prototype <- .BiocParallelParam_prototype .DoparParam <- setRefClass("DoparParam", contains="BiocParallelParam", fields=list(), methods=list() ) DoparParam <- function(stop.on.error=TRUE) { if (!"package:foreach" %in% search()) { tryCatch({ loadNamespace("foreach") }, error=function(err) { stop(conditionMessage(err), "\n", " DoparParam() requires the 'foreach' package") }) } prototype <- .prototype_update( .DoparParam_prototype, stop.on.error=stop.on.error ) x <- do.call(.DoparParam, prototype) validObject(x) x } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - control ### setMethod("bpworkers", "DoparParam", function(x) { if (bpisup(x)) foreach::getDoParWorkers() else 0L }) setMethod("bpisup", "DoparParam", function(x) { isNamespaceLoaded("foreach") && foreach::getDoParRegistered() && (foreach::getDoParName() != "doSEQ") && (foreach::getDoParWorkers() > 1L) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - evaluation ### setMethod("bplapply", c("ANY", "DoparParam"), function(X, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { if (!length(X)) return(.rename(list(), X)) FUN <- match.fun(FUN) idx <- .redo_index(X, BPREDO) if (any(idx)) X <- X[idx] FUN <- .composeTry( FUN, bplog(BPPARAM), bpstopOnError(BPPARAM), timeout=bptimeout(BPPARAM), exportglobals=bpexportglobals(BPPARAM) ) i <- NULL handle <- ifelse(bpstopOnError(BPPARAM), "stop", "pass") `%dopar%` <- foreach::`%dopar%` res <- tryCatch({ foreach::foreach(X=X, .errorhandling=handle) %dopar% FUN(X, ...) }, error=function(e) { msg <- conditionMessage(e) pattern <- "task ([[:digit:]]+).*" if (!grepl(pattern, msg)) stop( "'DoparParam()' foreach() error occurred: ", msg, call. = FALSE ) txt <- "'DoparParam()' does not support partial results" updt <- rep(list(.error_not_available(txt)), length(X)) i <- sub(pattern, "\\1", msg) updt[[as.integer(i)]] <- .error(msg) updt }) names(res) <- names(X) if (any(idx)) { BPREDO[idx] <- res res <- BPREDO } if (!all(bpok(res))) stop(.error_bplist(res)) res }) setMethod("bpiterate", c("ANY", "ANY", "DoparParam"), function(ITER, FUN, ..., BPPARAM=bpparam()) { stop("'bpiterate' not supported for DoparParam") }) BiocParallel/R/ErrorHandling.R0000644000175200017520000001272614136050065017217 0ustar00biocbuildbiocbuild### ========================================================================= ### Error handling ### ------------------------------------------------------------------------- bpok <- function(x, type = bperrorTypes()) { type <- match.arg(type) !vapply(x, inherits, logical(1), type) } bptry <- function(expr, ..., bplist_error, bperror) { if (missing(bplist_error)) bplist_error <- function(err) attr(err, "result") if (missing(bperror)) bperror <- identity tryCatch(expr, ..., bplist_error=bplist_error, bperror=bperror) } .composeTry <- function(FUN, log, stop.on.error, stop.immediate = FALSE, # TRUE for SerialParam lapply as.error = TRUE, # FALSE for BatchJobs compatible timeout, exportglobals = TRUE, force.GC = FALSE) { if (!stop.on.error && stop.immediate) stop("[internal] 'stop.on.error == FALSE' && 'stop.immediate == TRUE'") FUN <- match.fun(FUN) force(log) force(stop.on.error) force(stop.immediate) force(as.error) force(timeout) force(force.GC) if (exportglobals) { blocklist <- c( "askpass", "asksecret", "buildtools.check", "buildtools.with", "pager", "plumber.swagger.url", "profvis.print", "restart", "reticulate.repl.hook", "reticulate.repl.initialize", "reticulate.repl.teardown", "shiny.launch.browser", "terminal.manager", "error", "topLevelEnvironment" ) global_options <- base::options() global_options <- global_options[!names(global_options) %in% blocklist] } ERROR_OCCURRED <- FALSE UNEVALUATED <- .error_unevaluated() # singleton handle_warning <- function(w) { .log_warn(log, "%s", w) w # FIXME: muffleWarning; don't rely on capture.output() } handle_error <- function(e) { ERROR_OCCURRED <<- TRUE .log_error(log, "%s", e) call <- sapply(sys.calls(), deparse, nlines=3) e <- if (as.error) { .error_remote(e, call) } else { .condition_remote(e, call) # BatchJobs } if (stop.immediate) stop(e) else e } function(...) { setTimeLimit(timeout, timeout, TRUE) on.exit(setTimeLimit(Inf, Inf, FALSE)) if (exportglobals) base::options(global_options) if (stop.on.error && ERROR_OCCURRED) { UNEVALUATED } else { output <- withCallingHandlers({ tryCatch({ FUN(...) }, error=handle_error) }, warning=handle_warning) ## Trigger garbage collection to cut down on memory usage within ## each worker in shared memory contexts. Otherwise, each worker is ## liable to think the entire heap is available (leading to each ## worker trying to fill said heap, causing R to exhaust memory). if (force.GC) gc(verbose=FALSE, full=FALSE) output } } } .condition_remote <- function(x, call) { ## BatchJobs does not return errors structure(x, class = c("remote_error", "bperror", "condition"), traceback = capture.output(traceback(call))) } .error <- function(msg, class=NULL) { structure(list(message=msg), class = c(class, "bperror", "error", "condition")) } .error_remote <- function(x, call) { structure(x, class = c("remote_error", "bperror", "error", "condition"), traceback = capture.output(traceback(call))) } .error_unevaluated <- function() { structure(list(message="not evaluated due to previous error"), class=c("unevaluated_error", "bperror", "error", "condition")) } .error_not_available <- function(msg) { structure(list(message=msg), class=c("not_available_error", "bperror", "error", "condition")) } .error_worker_comm <- function(error, msg) { msg <- sprintf("%s:\n %s", msg, conditionMessage(error)) structure(list(message=msg, original_error_class=class(error)), class=c("worker_comm_error", "bperror", "error", "condition")) } bperrorTypes <- function() { subclasses <- paste0( c("remote", "unevaluated", "not_available", "worker_comm"), "_error" ) c("bperror", subclasses) } .error_bplist <- function(result) { remote_error <- !bpok(result, "remote_error") | !bpok(result, "worker_comm_error") idx <- which(remote_error) n_remote_error <- sum(remote_error) n_other_error <- sum(!bpok(result)) - n_remote_error fmt = paste( "BiocParallel errors", "%d remote errors, element index: %s%s", "%d unevaluated and other errors", "first remote error: %s", sep = "\n " ) message <- sprintf( fmt, n_remote_error, paste(head(idx), collapse = ", "), ifelse(length(idx) > 6, ", ...", ""), n_other_error, conditionMessage(result[[idx[[1]]]]) ) err <- structure( list(message=message), result=result, class = c("bplist_error", "bperror", "error", "condition")) } print.remote_error <- function(x, ...) { NextMethod(x) cat("traceback() available as 'attr(x, \"traceback\")'\n") } `print.bplist_error` <- function(x, ...) { NextMethod(x) cat("results and errors available as 'attr(x, \"result\")'\n") } BiocParallel/R/MulticoreParam-class.R0000644000175200017520000000553214136050065020505 0ustar00biocbuildbiocbuild### ========================================================================= ### MulticoreParam objects ### ------------------------------------------------------------------------- multicoreWorkers <- function() .snowCores("multicore") ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### .MulticoreParam_prototype <- .SnowParam_prototype .MulticoreParam <- setRefClass("MulticoreParam", contains="SnowParam", fields=list(), methods=list() ) MulticoreParam <- function(workers=multicoreWorkers(), tasks=0L, stop.on.error=TRUE, progressbar=FALSE, RNGseed=NULL, timeout= 30L * 24L * 60L * 60L, exportglobals=TRUE, log=FALSE, threshold="INFO", logdir=NA_character_, resultdir=NA_character_, jobname = "BPJOB", force.GC = TRUE, manager.hostname=NA_character_, manager.port=NA_integer_, ...) { if (.Platform$OS.type == "windows") { warning("MulticoreParam() not supported on Windows, use SnowParam()") workers = 1L } if (progressbar && missing(tasks)) tasks <- .Machine$integer.max clusterargs <- c(list(spec=workers, type="FORK"), list(...)) manager.hostname <- if (is.na(manager.hostname)) { local <- (clusterargs$type == "FORK") || is.numeric(clusterargs$spec) manager.hostname <- .snowHost(local) } else as.character(manager.hostname) manager.port <- if (is.na(manager.port)) { .snowPort() } else as.integer(manager.port) if (!is.null(RNGseed)) RNGseed <- as.integer(RNGseed) prototype <- .prototype_update( .MulticoreParam_prototype, .clusterargs=clusterargs, cluster=.NULLcluster(), .controlled=TRUE, workers=as.integer(workers), tasks=as.integer(tasks), stop.on.error=stop.on.error, progressbar=progressbar, RNGseed=RNGseed, timeout=as.integer(timeout), exportglobals=exportglobals, log=log, threshold=threshold, logdir=logdir, resultdir=resultdir, jobname=jobname, force.GC = force.GC, hostname=manager.hostname, port=manager.port, ... ) x <- do.call(.MulticoreParam, prototype) validObject(x) x } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - control ### setReplaceMethod("bpworkers", c("MulticoreParam", "numeric"), function(x, value) { value <- as.integer(value) max <- .snowCoresMax("multicore") if (value > max) stop( "'value' exceeds ", max, " available workers; see ?multicoreWorkers" ) x$workers <- value x$.clusterargs$spec <- value x }) setMethod("bpschedule", "MulticoreParam", function(x) { if (.Platform$OS.type == "windows") FALSE else TRUE }) BiocParallel/R/SerialParam-class.R0000644000175200017520000000652314145415024017762 0ustar00biocbuildbiocbuild### ========================================================================= ### SerialParam objects ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### .SerialParam_prototype <- c( list( workers = 1L, backend = NULL ), .BiocParallelParam_prototype ) .SerialParam <- setRefClass( "SerialParam", fields=list(backend = "ANY"), contains="BiocParallelParam", ) SerialParam <- function(stop.on.error = TRUE, progressbar=FALSE, RNGseed = NULL, timeout = 30L * 24L * 60L * 60L, log=FALSE, threshold="INFO", logdir=NA_character_, resultdir = NA_character_, jobname = "BPJOB", force.GC = FALSE) { if (!is.null(RNGseed)) RNGseed <- as.integer(RNGseed) if (progressbar) { tasks <- .Machine$integer.max } else { tasks <- 0L } prototype <- .prototype_update( .SerialParam_prototype, tasks = tasks, stop.on.error=stop.on.error, progressbar=progressbar, RNGseed = RNGseed, timeout = as.integer(timeout), log=log, threshold=threshold, logdir=logdir, resultdir = resultdir, jobname = jobname, force.GC = force.GC ) x <- do.call(.SerialParam, prototype) validObject(x) x } setAs("BiocParallelParam", "SerialParam", function(from) { SerialParam( stop.on.error = bpstopOnError(from), progressbar = bpprogressbar(from), RNGseed = bpRNGseed(from), timeout = bptimeout(from), log = bplog(from), threshold = bpthreshold(from), logdir = bplogdir(from), resultdir = bpresultdir(from), jobname = bpjobname(from), force.GC = bpforceGC(from) ) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - control ### setMethod( "bpbackend", "SerialParam", function(x) { x$backend }) setMethod( "bpstart", "SerialParam", function(x, ...) { x$backend <- .SerialBackend() x$backend$BPPARAM <- x .bpstart_impl(x) }) setMethod( "bpstop", "SerialParam", function(x) { x$backend <- NULL .bpstop_impl(x) }) setMethod( "bpisup", "SerialParam", function(x) { is.environment(bpbackend(x)) }) setReplaceMethod("bplog", c("SerialParam", "logical"), function(x, value) { x$log <- value validObject(x) x }) setReplaceMethod( "bpthreshold", c("SerialParam", "character"), function(x, value) { x$threshold <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Backend method ### .SerialBackend <- setClass("SerialBackend", contains = "environment") setMethod(".send_to", "SerialBackend", function(backend, node, value){ backend$value <- value TRUE }) setMethod( ".recv_any", "SerialBackend", function(backend) { on.exit(backend$value <- NULL) msg <- backend$value if (inherits(msg, "error")) stop(msg) if (msg$type == "EXEC") { value <- .bpworker_EXEC(msg, bplog(backend$BPPARAM)) list(node = 1L, value = value) } }) setMethod("length", "SerialBackend", function(x){ 1L }) BiocParallel/R/SnowParam-class.R0000644000175200017520000002271714136050065017474 0ustar00biocbuildbiocbuild### ========================================================================= ### SnowParam objects ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Helpers ### .snowHost <- function(local=TRUE) { host <- if (local) { "localhost" } else Sys.info()[["nodename"]] host <- Sys.getenv("MASTER", host) host <- getOption("bphost", host) host } .snowPort <- function() { port <- Sys.getenv("R_PARALLEL_PORT", NA_integer_) port <- Sys.getenv("PORT", port) port <- getOption("ports", port) if (identical(tolower(port), "random") || is.na(port)) { .rng_internal_stream$set() on.exit(.rng_internal_stream$reset()) port <- as.integer( 11000 + 1000 * ((stats::runif(1L) + unclass(Sys.time()) / 300) %% 1L) ) } else { port <- as.integer(port) } port } .snowCoresMax <- function(type) { if (type == "MPI") { .Machine$integer.max } else { 128L - nrow(showConnections(all=TRUE)) } } .snowCores <- function(type) { if (type == "multicore" && .Platform$OS.type == "windows") return(1L) min(.detectCores(), .snowCoresMax(type)) } snowWorkers <- function(type = c("SOCK", "MPI", "FORK")) { type <- match.arg(type) .snowCores(type) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### setOldClass(c("NULLcluster", "cluster")) .NULLcluster <- function() { cl <- list() class(cl) <- c("NULLcluster", "cluster") cl } .SnowParam_prototype <- c( list( cluster = .NULLcluster(), .clusterargs = list(spec=0, type="SOCK"), .controlled = TRUE, hostname = NA_character_, port = NA_integer_ ), .BiocParallelParam_prototype ) .SnowParam <- setRefClass("SnowParam", contains="BiocParallelParam", fields=list( cluster = "cluster", .clusterargs = "list", .controlled = "logical", hostname = "character", port = "integer" ), methods=list( show = function() { callSuper() cat(" cluster type: ", .clusterargs$type, "\n", sep="") }) ) SnowParam <- function(workers=snowWorkers(type), type=c("SOCK", "MPI", "FORK"), tasks=0L, stop.on.error=TRUE, progressbar=FALSE, RNGseed=NULL, timeout=30L * 24L * 60L * 60L, exportglobals=TRUE, log=FALSE, threshold="INFO", logdir=NA_character_, resultdir=NA_character_, jobname = "BPJOB", force.GC = FALSE, manager.hostname=NA_character_, manager.port=NA_integer_, ...) { type <- tryCatch(match.arg(type), error=function(...) { stop("'type' must be one of ", paste(sQuote(formals("SnowParam")$type), collapse=", ")) }) if (type %in% c("MPI", "FORK") && is(workers, "character")) stop("'workers' must be integer(1) when 'type' is MPI or FORK") if (progressbar && missing(tasks)) tasks <- .Machine$integer.max clusterargs <- c(list(spec=workers, type=type), list(...)) manager.hostname <- if (is.na(manager.hostname)) { local <- (clusterargs$type == "FORK") || is.numeric(clusterargs$spec) manager.hostname <- .snowHost(local) } else as.character(manager.hostname) manager.port <- if (is.na(manager.port)) { .snowPort() } else as.integer(manager.port) if (!is.null(RNGseed)) RNGseed <- as.integer(RNGseed) prototype <- .prototype_update( .SnowParam_prototype, .clusterargs=clusterargs, .controlled=TRUE, workers=workers, tasks=as.integer(tasks), stop.on.error=stop.on.error, progressbar=progressbar, RNGseed=RNGseed, timeout=as.integer(timeout), exportglobals=exportglobals, log=log, threshold=threshold, logdir=logdir, resultdir=resultdir, jobname=jobname, force.GC = force.GC, hostname=manager.hostname, port=manager.port, ... ) do.call(.SnowParam, prototype) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity ### setValidity("SnowParam", function(object) { msg <- NULL if (!.isTRUEorFALSE(.controlled(object))) msg <- c(msg, "'.controlled' must be TRUE or FALSE") if (.controlled(object)) { if (!all(bpworkers(object) == object$.clusterargs$spec)) msg <- c(msg, "'bpworkers(BPPARAM)' must equal BPPARAM$.clusterargs$spec") } if (is.null(msg)) TRUE else msg }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters / Setters ### .hostname <- function(x) x$hostname .port <- function(x) x$port setReplaceMethod("bpworkers", c("SnowParam", "numeric"), function(x, value) { value <- as.integer(value) max <- .snowCoresMax(x$.clusterargs$type) if (value > max) stop("'value' exceeds ", max, " available workers; see ?snowWorkers") x$workers <- value x$.clusterargs$spec <- value x }) setReplaceMethod("bpworkers", c("SnowParam", "character"), function(x, value) { x$workers <- x$.clusterargs$spec <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Methods - control ### .bpstart_makeCluster <- function(cargs) { ## set internal stream to avoid iterating global random number ## stream in `parallel::makeCluster()`. Use the internal stream so ## that the random number generator advances on each call. state <- .rng_internal_stream$set() on.exit(.rng_internal_stream$reset()) do.call(parallel::makeCluster, cargs) } setMethod("bpstart", "SnowParam", function(x, lenX = bpnworkers(x)) { if (!.controlled(x)) stop("'bpstart' not available; instance from outside BiocParallel?") if (bpisup(x)) stop("cluster already started") if (bpnworkers(x) == 0 && lenX <= 0) stop("cluster not started; no workers specified") nnodes <- min(bpnworkers(x), lenX) if (x$.clusterargs$type != "MPI" && (nnodes > 128L - nrow(showConnections(all=TRUE)))) stop("cannot create ", nnodes, " workers; ", 128L - nrow(showConnections(all=TRUE)), " connections available in this session") ## FORK (useRscript not relevant) if (x$.clusterargs$type == "FORK") { bpbackend(x) <- .bpfork(nnodes, bptimeout(x), .hostname(x), .port(x)) ## SOCK, MPI } else { cargs <- x$.clusterargs cargs$spec <- if (is.numeric(cargs$spec)) { nnodes } else cargs$spec[seq_len(nnodes)] if (is.null(cargs$snowlib)) cargs$snowlib <- find.package("BiocParallel") if (!is.null(cargs$useRscript) && !cargs$useRscript) cargs$scriptdir <- find.package("BiocParallel") if (x$.clusterargs$type == "SOCK") { cargs$master <- .hostname(x) cargs$port <- .port(x) } bpbackend(x) <- .bpstart_makeCluster(cargs) } .bpstart_impl(x) }) setMethod("bpstop", "SnowParam", function(x) { if (!.controlled(x)) { warning("'bpstop' not available; instance from outside BiocParallel?") return(invisible(x)) } if (!bpisup(x)) return(invisible(x)) x <- .bpstop_impl(x) cluster <- bpbackend(x) for (i in seq_along(cluster)) .close(cluster[[i]]) bpbackend(x) <- .NULLcluster() invisible(x) }) setMethod("bpisup", "SnowParam", function(x) { length(bpbackend(x)) != 0L }) setMethod("bpbackend", "SnowParam", function(x) { x$cluster }) setReplaceMethod("bpbackend", c("SnowParam", "cluster"), function(x, value) { x$cluster <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters / Setters ### .controlled <- function(x) { x$.controlled } setReplaceMethod("bplog", c("SnowParam", "logical"), function(x, value) { if (x$.controlled) { x$log <- value if (bpisup(x)) { bpstop(x) bpstart(x) } x } else { stop("'bplog' not available; instance from outside BiocParallel?") } }) setReplaceMethod("bpthreshold", c("SnowParam", "character"), function(x, value) { x$threshold <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion methods for SOCK and MPI clusters ### ### parallel::SOCKcluster types setOldClass(c("SOCKcluster", "cluster")) stopCluster.SOCKcluster <- parallel:::stopCluster.default setAs("SOCKcluster", "SnowParam", function(from) { .clusterargs <- list(spec=length(from), type=sub("cluster$", "", class(from)[1L])) prototype <- .prototype_update( .SnowParam_prototype, .clusterargs = .clusterargs, cluster = from, .controlled = FALSE, workers = length(from) ) do.call(.SnowParam, prototype) }) ### MPIcluster setOldClass(c("spawnedMPIcluster", "MPIcluster", "cluster")) setAs("spawnedMPIcluster", "SnowParam", function(from) { .clusterargs <- list(spec=length(from), type=sub("cluster", "", class(from)[1L])) .SnowParam(.clusterargs=.clusterargs, cluster=from, .controlled=FALSE, workers=length(from)) }) BiocParallel/R/SnowParam-utils.R0000644000175200017520000000602514147652225017531 0ustar00biocbuildbiocbuild ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### snow::MPI ### bprunMPIslave <- function() { .Deprecated("bprunMPIworker") bprunMPIworker() } bprunMPIworker <- function() { comm <- 1 intercomm <- 2 Rmpi::mpi.comm.get.parent(intercomm) Rmpi::mpi.intercomm.merge(intercomm,1,comm) Rmpi::mpi.comm.set.errhandler(comm) Rmpi::mpi.comm.disconnect(intercomm) .bpworker_impl(snow::makeMPImaster(comm)) Rmpi::mpi.comm.disconnect(comm) Rmpi::mpi.quit() } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### parallel::FORK ### .bpfork <- function (nnodes, timeout, host, port) { nnodes <- as.integer(nnodes) if (is.na(nnodes) || nnodes < 1L) stop("'nnodes' must be >= 1") if (length(host) != 1L || is.na(host) || !is.character(host)) stop("'host' must be character(1)") if (length(port) != 1L || is.na(port) || !is.integer(port)) stop("'port' must be integer(1)") cl <- vector("list", nnodes) for (rank in seq_along(cl)) { .bpforkChild(host, port, rank, timeout) cl[[rank]] <- .bpforkConnect(host, port, rank, timeout) } class(cl) <- c("SOCKcluster", "cluster") cl } .bpforkChild <- function(host, port, rank, timeout) { parallel::mcparallel({ con <- NULL suppressWarnings({ while (is.null(con)) { con <- tryCatch({ socketConnection(host, port, FALSE, TRUE, "a+b", timeout = timeout) }, error=function(e) {}) } }) node <- structure(list(con = con), class = "SOCK0node") .bpworker_impl(node) }, detached=TRUE) } .bpforkConnect <- function(host, port, rank, timeout) { con <- socketConnection(host, port, TRUE, TRUE, "a+b", timeout = timeout) structure(list(con = con, host = host, rank = rank), class = c("forknode", "SOCK0node")) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### logs and results ### .bpwriteLog <- function(con, d) { .log_internal <- function() { message( "############### LOG OUTPUT ###############\n", "Task: ", d$value$tag, "\nNode: ", d$node, "\nTimestamp: ", Sys.time(), "\nSuccess: ", d$value$success, "\n\nTask duration:\n", paste(capture.output(d$value$time), collapse="\n"), "\n\nMemory used:\n", paste(capture.output(gc()), collapse="\n"), "\n\nLog messages:\n", paste(trimws(d$value$log), collapse="\n"), "\n\nstderr and stdout:\n", if (!is.null(d$value$sout)) paste(noquote(d$value$sout), collapse="\n") ) } if (!is.null(con)) { on.exit({ sink(NULL, type = "message") sink(NULL, type = "output") }) sink(con, type = "message") sink(con, type = "output") .log_internal() } else .log_internal() } BiocParallel/R/TransientMulticoreParam-class.R0000644000175200017520000000512714136050065022375 0ustar00biocbuildbiocbuild.TransientMulticoreParam <- setRefClass( "TransientMulticoreParam", contains = "MulticoreParam" ) TransientMulticoreParam <- function(param) { as(param, "TransientMulticoreParam") } .TRANSIENTMULTICOREPARAM_JOBNODE <- new.env(parent=emptyenv()) .TRANSIENTMULTICOREPARAM_RESULT <- new.env(parent=emptyenv()) setMethod( "bpstart", "TransientMulticoreParam", function(x, ...) { parallel::mccollect(wait=TRUE) rm( list=ls(envir = .TRANSIENTMULTICOREPARAM_JOBNODE), envir = .TRANSIENTMULTICOREPARAM_JOBNODE ) rm( list = ls(envir = .TRANSIENTMULTICOREPARAM_RESULT), envir = .TRANSIENTMULTICOREPARAM_RESULT ) .bpstart_impl(x) }) setMethod( "bpstop", "TransientMulticoreParam", function(x) { .bpstop_impl(x) }) setMethod( "bpbackend", "TransientMulticoreParam", function(x) { x }) setMethod( "length", "TransientMulticoreParam", function(x) { bpnworkers(x) }) ## ## send / recv ## setMethod( ".recv_all", "TransientMulticoreParam", function(backend) { replicate(length(backend), .recv_any(backend), simplify=FALSE) }) setMethod( ".send_to", "TransientMulticoreParam", function(backend, node, value) { if (value$type == "EXEC") { job <- parallel::mcparallel(.bpworker_EXEC(value)) id <- as.character(job$pid) .TRANSIENTMULTICOREPARAM_JOBNODE[[id]] <- node } TRUE }) setMethod( ".recv_any", "TransientMulticoreParam", function(backend) { .BUFF <- .TRANSIENTMULTICOREPARAM_RESULT # alias tryCatch({ while (!length(.BUFF)) { result <- parallel::mccollect(wait = FALSE, timeout = 1) for (id in names(result)) .BUFF[[id]] <- result[[id]] } id <- head(names(.BUFF), 1L) value <- .BUFF[[id]] rm(list = id, envir = .BUFF) node <- .TRANSIENTMULTICOREPARAM_JOBNODE[[id]] rm(list = id, envir = .TRANSIENTMULTICOREPARAM_JOBNODE) list(node = node, value = value) }, error = function(e) { ## indicate error, but do not stop .error_worker_comm(e, "'.recv_any()' data failed") }) }) setMethod( ".send", "TransientMulticoreParam", function(worker, value) { stop("'.send,TransientMulticoreParam-method' not implemented") }) setMethod( ".recv", "TransientMulticoreParam", function(worker) { stop("'.recv,TransientMulticoreParam-method' not implemented") }) setMethod( ".close", "TransientMulticoreParam", function(worker) { stop("'.close,TransientMulticoreParam-method' not implemented") }) BiocParallel/R/bpaggregate-methods.R0000644000175200017520000000604614136050065020370 0ustar00biocbuildbiocbuild### ========================================================================= ### bpaggregate methods ### ------------------------------------------------------------------------- ## All params use bpaggregate,data.frame,BiocParallelParam. ## bpaggretate() dispatches to bplapply() where errors and ## logging are handled. setMethod("bpaggregate", c("ANY", "missing"), function(x, ..., BPREDO=list(), BPPARAM=bpparam()) { bpaggregate(x, ..., BPREDO=BPREDO, BPPARAM=BPPARAM) }) setMethod("bpaggregate", c("matrix", "BiocParallelParam"), function(x, by, FUN, ..., simplify=TRUE, BPREDO=list(), BPPARAM=bpparam()) { if (!is.data.frame(x)) x <- as.data.frame(x) bpaggregate(x, by, FUN, ..., simplify=simplify, BPREDO=BPREDO, BPPARAM=BPPARAM) }) setMethod("bpaggregate", c("data.frame", "BiocParallelParam"), function(x, by, FUN, ..., simplify=TRUE, BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) if (!is.data.frame(x)) x <- as.data.frame(x) if (!is.list(by)) stop("'by' must be a list") by <- lapply(by, as.factor) wrapper <- function(.ind, .x, .AGGRFUN, ..., .simplify) { sapply(.x[.ind,, drop=FALSE], .AGGRFUN, ..., simplify=.simplify) } ind <- Filter(length, split(seq_len(nrow(x)), by)) grp <- rep(seq_along(ind), lengths(ind)) grp <- grp[match(seq_len(nrow(x)), unlist(ind))] res <- bplapply(ind, wrapper, .x=x, .AGGRFUN=FUN, .simplify=simplify, BPREDO=BPREDO, BPPARAM=BPPARAM) res <- do.call(rbind, lapply(res, rbind)) if (is.null(names(by)) && length(by)) { names(by) <- sprintf("Group.%i", seq_along(by)) } else { ind <- which(!nzchar(names(by))) names(by)[ind] <- sprintf("Group.", ind) } tab <- as.data.frame(lapply(by, as.character), stringsAsFactors=FALSE) tab <- tab[match(sort(unique(grp)), grp),, drop=FALSE] rownames(tab) <- rownames(res) <- NULL tab <- cbind(tab, res) names(tab) <- c(names(by), names(x)) tab }) setMethod("bpaggregate", c("formula", "BiocParallelParam"), function (x, data, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { if (length(x) != 3L) stop("Formula 'x' must have both left and right hand sides") m <- match.call(expand.dots=FALSE) if (is.matrix(eval(m$data, parent.frame()))) m$data <- as.data.frame(data) m$... <- m$FUN <- m$BPPARAM <- m$BPREDO <- NULL m[[1L]] <- quote(stats::model.frame) names(m)[[2]] <- "formula" if (x[[2L]] == ".") { rhs <- as.list(attr(terms(x[-2L]), "variables")[-1]) lhs <- as.call(c(quote(cbind), setdiff(lapply(names(data), as.name), rhs))) x[[2L]] <- lhs m[[2L]] <- x } mf <- eval(m, parent.frame()) if (is.matrix(mf[[1L]])) { lhs <- as.data.frame(mf[[1L]]) bpaggregate(lhs, mf[-1L], FUN=FUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM) } else bpaggregate(mf[1L], mf[-1L], FUN=FUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM) }) BiocParallel/R/bpbackend-methods.R0000644000175200017520000000035714136050065020030 0ustar00biocbuildbiocbuildsetMethod("bpbackend", "missing", function(x) { x <- registered()[[1]] bpbackend(x) }) setReplaceMethod("bpbackend", c("missing", "ANY"), function(x, value) { x <- registered()[[1]] bpbackend(x) <- value x }) BiocParallel/R/bpinit.R0000644000175200017520000000553214136050065015743 0ustar00biocbuildbiocbuild.bpinit <- function(manager, FUN, BPPARAM, BPREDO = list(), ...) { ## Conditions for starting a cluster, or falling back to (and ## starting) a SerialParam nworkers <- bpnworkers(BPPARAM) # cache in case this requires a netowrk call fallback_condition <- !inherits(BPPARAM, "SerialParam") && nworkers == 0L || # e.g., in dynamic cluster like RedisParam !bpschedule(BPPARAM) # e.g., in nested parallel call if (!bpisup(BPPARAM) || fallback_condition) { if (fallback_condition || nworkers == 1L) { ## use SerialParam when zero workers (e.g., in a dynaamic ## cluster like RedisParam, where there are no registered ## workers), or when bpschedule() is FALSE (e.g,. because ## we are already in a parallel job), or when there is ## only one worker (when the cost of serialization to a ## remote worker can be avoided). oldParam <- BPPARAM BPPARAM <- as(BPPARAM, "SerialParam") on.exit({ .RNGstream(oldParam) <- .RNGstream(BPPARAM) }, TRUE, FALSE) # add = TRUE, last = FALSE --> last in, # first out order } else if (is(BPPARAM, "MulticoreParam")) { ## use TransientMulticoreParam when MulticoreParam has not ## started oldParam <- BPPARAM BPPARAM <- as(BPPARAM, "TransientMulticoreParam") on.exit({ .RNGstream(oldParam) <- .RNGstream(BPPARAM) }, TRUE, FALSE) } ## start / stop cluster BPPARAM <- bpstart(BPPARAM) on.exit(bpstop(BPPARAM), TRUE, FALSE) } ## record the initial seed used by the BPPARAM BPREDOSEED <- .RNGstream(BPPARAM) ## If BPREDO present and contains a seed, we need to ## 1. use the seed from BPREDO ## 2. recover the old seed after computing the result if (length(BPREDO) && !is.null(attr(BPREDO, "BPREDOSEED"))) { .RNGstream(BPPARAM) <- attr(BPREDO, "BPREDOSEED") on.exit({ .RNGstream(BPPARAM) <- BPREDOSEED }, TRUE, after = FALSE) } ## FUN FUN <- .composeTry( FUN, bplog(BPPARAM), bpstopOnError(BPPARAM), timeout=bptimeout(BPPARAM), exportglobals=bpexportglobals(BPPARAM), force.GC = bpforceGC(BPPARAM) ) ## iteration res <- bploop( manager, # dispatch FUN = FUN, BPPARAM = BPPARAM, ... ) if (length(BPREDO)) { redo_idx <- which(!bpok(BPREDO)) if (length(redo_idx)) BPREDO[redo_idx] <- res res <- BPREDO } if (!all(bpok(res))) { ## attach the seed only when no BPREDO presents if (!length(BPREDO)) attr(res, "BPREDOSEED") <- BPREDOSEED } else { attr(res, "BPREDOSEED") <- NULL } res } BiocParallel/R/bpisup-methods.R0000644000175200017520000000021514136050065017412 0ustar00biocbuildbiocbuildsetMethod("bpisup", "ANY", function(x) FALSE) setMethod("bpisup", "missing", function(x) { x <- registered()[[1]] bpisup(x) }) BiocParallel/R/bpiterate-methods.R0000644000175200017520000000263514136050065020077 0ustar00biocbuildbiocbuild### ========================================================================= ### bpiterate methods ### ------------------------------------------------------------------------- ## All params have dedicated bpiterate() methods. setMethod("bpiterate", c("ANY", "ANY", "missing"), function(ITER, FUN, ..., BPPARAM=bpparam()) { ITER <- match.fun(ITER) FUN <- match.fun(FUN) bpiterate(ITER, FUN, ..., BPPARAM=BPPARAM) }) ## TODO: support BPREDO .bpiterate_impl <- function(ITER, FUN, ..., REDUCE, init, reduce.in.order = FALSE, BPPARAM = bpparam()) { ## Required API ## ## - BiocParallelParam() ## - bpschedule(), bpisup(), bpstart(), bpstop() ## - .sendto, .recvfrom, .recv, .close ITER <- match.fun(ITER) FUN <- match.fun(FUN) ITER_ <- function() { list(ITER()) } if (missing(REDUCE)) { if (reduce.in.order) stop("REDUCE must be provided when 'reduce.in.order = TRUE'") if (!missing(init)) stop("REDUCE must be provided when 'init' is given") } ARGS <- list(...) manager <- structure(list(), class="iterate") # dispatch res <- .bpinit( manager = manager, ITER = ITER_, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM, init = init, REDUCE = REDUCE, reduce.in.order = reduce.in.order ) ## TODO: handle the error in res res } BiocParallel/R/bplapply-methods.R0000644000175200017520000000423314136050065017737 0ustar00biocbuildbiocbuild### ========================================================================= ### bplapply methods ### ------------------------------------------------------------------------- ## All params have dedicated bplapply methods. setMethod("bplapply", c("ANY", "missing"), function(X, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) bplapply(X, FUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM) }) setMethod("bplapply", c("ANY", "list"), function(X, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) if (!all(vapply(BPPARAM, inherits, logical(1), "BiocParallelParam"))) stop("All elements in 'BPPARAM' must be BiocParallelParam objects") if (length(BPPARAM) == 0L) stop("'length(BPPARAM)' must be > 0") myFUN <- if (length(BPPARAM) > 1L) { if (length(param <- BPPARAM[-1]) == 1L) function(...) FUN(..., BPPARAM=param[[1]]) else function(...) FUN(..., BPPARAM=param) } else FUN bplapply(X, myFUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM[[1]]) }) .bplapply_impl <- function(X, FUN, ..., BPREDO = list(), BPPARAM = bpparam()) { ## abstract 'common' implementation using accessors only ## ## Required API: ## ## - BiocParallelParam() ## - bpschedule(), bpisup(), bpstart(), bpstop() ## - .send_to, .recv_any, .send, .recv, .close FUN <- match.fun(FUN) if (!length(X)) return(.rename(list(), X)) ## which need to be redone? redo_index <- .redo_index(X, BPREDO) if (any(redo_index)) { compute_element <- which(redo_index) X <- X[compute_element] } else { compute_element <- seq_along(X) } nms <- names(X) ## split into tasks X <- .splitX(X, bpnworkers(BPPARAM), bptasks(BPPARAM), redo_index) ARGS <- list(...) manager <- structure(list(), class="lapply") # dispatch res <- .bpinit( manager = manager, X = X, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM, BPREDO = BPREDO ) if (!is.null(res)) names(res)[compute_element] <- nms if (!all(bpok(res))) stop(.error_bplist(res)) res } BiocParallel/R/bploop.R0000644000175200017520000003045014147652225015756 0ustar00biocbuildbiocbuild### ========================================================================= ### Low-level cluster utilities ### ------------------------------------------------------------------------- ### Support for SOCK, MPI and FORK connections. ### Derived from snow version 0.3-13 by Luke Tierney ### Derived from parallel version 2.16.0 by R Core Team .EXEC <- function(tag, fun, args) { list(type="EXEC", data=list(fun=fun, args=args, tag=tag)) } .VALUE <- function(tag, value, success, time, log, sout) { list(type = "VALUE", tag = tag, value = value, success = success, time = time, log = log, sout = sout) } .DONE <- function() { list(type = "DONE") } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Worker loop used by SOCK, MPI and FORK. Error handling is done in ### .composeTry. .bpworker_EXEC <- function(msg, sink.sout = TRUE) { ## need local handler for worker read/send errors if (sink.sout) { on.exit({ sink(NULL, type="message") sink(NULL, type="output") close(file) }) file <- rawConnection(raw(), "r+") sink(file, type="message") sink(file, type="output") } t1 <- proc.time() value <- tryCatch({ do.call(msg$data$fun, msg$data$args) }, error=function(e) { ## return as 'list()' because msg$data$fun has lapply semantics list(.error_worker_comm(e, "worker evaluation failed")) }) t2 <- proc.time() if (sink.sout) { sout <- rawToChar(rawConnectionValue(file)) if (!nchar(sout)) sout <- NULL } else { sout <- NULL } success <- !(inherits(value, "bperror") || !all(bpok(value))) log <- .log_buffer_get() value <- .VALUE( msg$data$tag, value, success, t2 - t1, log, sout ) } .bpworker_impl <- function(worker) { repeat { tryCatch({ msg <- .recv(worker) if (inherits(msg, "error")) ## FIXME: try to return error to manager break # lost socket connection? if (msg$type == "DONE") { .close(worker) break } else if (msg$type == "EXEC") { value <- .bpworker_EXEC(msg) .send(worker, value) } }, interrupt = function(e) { NULL }) } } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Manager loop used by SOCK, MPI and FORK ## These functions are used by all cluster types (SOCK, MPI, FORK) and ## run on the master. Both enable logging, writing logs/results to ## files and 'stop on error'. The 'cl' argument is an active cluster; ## starting and stopping of 'cl' is done outside these functions, eg, ## bplapply, bpmapply etc.. .clear_cluster <- function(cl, running, reducer) { tryCatch({ setTimeLimit(30, 30, TRUE) on.exit(setTimeLimit(Inf, Inf, FALSE)) while (any(running)) { d <- .recv_any(cl) njob <- d$value$tag value <- d$value$value reducer$add(njob, value) running[d$node] <- FALSE } }, error=function(e) { warning(.error_worker_comm(e, "stop worker failed")) }) reducer } .manager_log <- function(BPPARAM, njob, d) { if (bplog(BPPARAM)) { con <- NULL if (!is.na(bplogdir(BPPARAM))) { fname <- paste0(bpjobname(BPPARAM), ".task", njob, ".log") lfile <- file.path(bplogdir(BPPARAM), fname) con <- file(lfile, open="a") on.exit(close(con)) } .bpwriteLog(con, d) } else if (length(d$value$sout)) { message(paste(d$value$sout, collapse="\n")) } } .manager_result_save <- function(BPPARAM, njob, value) { if (is.na(bpresultdir(BPPARAM))) return(NULL) fname <- paste0(bpjobname(BPPARAM), ".task", njob, ".Rda") rfile <- file.path(bpresultdir(BPPARAM), fname) save(value, file=rfile) } .reducer <- function(REDUCE, init, reduce.in.order=FALSE) { env <- new.env(parent=emptyenv()) env[["reduce"]] <- !missing(REDUCE) env[["init"]] <- !missing(init) if (env[["reduce"]]) env[["value"]] <- if (env[["init"]]) init else list() env[["index"]] <- 1L reduce_one <- function(idx) { if (env[["reduce"]]) { if (!env[["init"]] && (env[["index"]] == 1L)) { env[["value"]] <- env[[idx]][[1]] skip_idx <- 1L } else { skip_idx <- 0L } for (i in seq_along(env[[idx]])) { if (i == skip_idx) next env[["value"]] <- REDUCE(env[["value"]], env[[idx]][[i]]) } rm(list=idx, envir=env) } env[["index"]] <- env[["index"]] + 1L 1L } add_immediate <- function(i, value) { env[[as.character(i)]] <- value reduce_one(as.character(i)) } add_inorder <- function(i, value) { status <- 0L env[[as.character(i)]] <- value while (exists(as.character(env[["index"]]), envir=env)) status <- status + reduce_one(as.character(env[["index"]])) status } list(add=function(i, value) { if (reduce.in.order) add_inorder(i, value) else add_immediate(i, value) }, isComplete=function() { length(setdiff(ls(env), c("reduce", "init", "value", "index"))) == 0L }, value=function() { if (env[["reduce"]]) env[["value"]] # NULL if no values else { lst <- as.list(env) idx <- setdiff(names(lst), c("reduce", "init", "value", "index")) if (length(idx)) { lst <- unname(lst[idx[order(as.integer(idx))]]) unlist(lst, recursive=FALSE) } else { list() } } }) } ## A dummy iterator for bploop.lapply .bploop_lapply_iter <- function(X) { i <- 0L n <- length(X) function() { if (i < n) { i <<- i + 1L X[[i]] } else { list(NULL) } } } ## This class object can force bploop.iterator to iterate ## the seed stream n times .bploop_rng_iter <- function(n) { structure(as.integer(n), class = c(".bploop_rng_iter")) } ## ## bploop.lapply(): derived from snow::dynamicClusterApply. ## bploop <- function(manager, ...) UseMethod("bploop") ## X: the loop value after division ## ARGS: The function arguments for `FUN` bploop.lapply <- function(manager, X, FUN, ARGS, BPPARAM, ...) { ITER <- .bploop_lapply_iter(X) manager <- structure(list(), class="iterate") # dispatch bploop( manager = manager, ITER = ITER, FUN = FUN, ARGS = ARGS, BPPARAM =BPPARAM, reduce.in.order = TRUE, progress.length = length(X) ) } ## ## bploop.iterate(): ## ## Derived from snow::dynamicClusterApply and parallel::mclapply. ## ## - length of 'X' is unknown (defined by ITER()) ## - results not pre-allocated; list grows each iteration if no REDUCE ## - args wrapped in arglist with different chunks from ITER() ## Arguments ## - ITER: Return a list where each list element will be passed to FUN ## 1. if nothing to proceed, it should return list(NULL) ## 2. if the task is iterate the seed stream only, it should return ## an object from .bploop_rng_iter() ## - FUN: A function that will be evaluated in the worker ## - ARGS: the arguments to FUN bploop.iterate <- function( manager, ITER, FUN, ARGS, BPPARAM, REDUCE, init, reduce.in.order, progress.length, ... ) { cl <- bpbackend(BPPARAM) seed <- .RNGstream(BPPARAM) on.exit(.RNGstream(BPPARAM) <- seed) workers <- length(cl) running <- logical(workers) reducer <- .reducer(REDUCE, init, reduce.in.order) progress <- .progress( active=bpprogressbar(BPPARAM), iterate=missing(progress.length) ) on.exit(progress$term(), TRUE) progress$init(progress.length) ARGFUN <- function(X, seed) c( list(X=X), list(FUN=FUN), ARGS, list(BPRNGSEED = seed) ) ## initial load for (i in seq_len(workers)) { value <- ITER() ## If the value is of the class .bploop_rng_iter, we merely iterate ## the seed stream `value` times and obtain the next value. There must ## be no two consecutive .bploop_rng_iter objects in the iterator, so ## we can proceed without checking the class again. if (inherits(value, ".bploop_rng_iter")) { seed <- .rng_iterate_substream(seed, value) value <- ITER() } if (identical(value, list(NULL))) { if (i == 1L) warning("first invocation of 'ITER()' returned NULL") break } value_ <- .EXEC(i, .rng_lapply, ARGFUN(value, seed)) running[i] <- .send_to(cl, i, value_) seed <- .rng_iterate_substream(seed, length(value)) } repeat { if (!any(running)) break ## collect d <- .recv_any(cl) progress$step() value <- d$value$value njob <- d$value$tag running[d$node] <- FALSE ## reduce ## FIXME: if any worker has an error - can't reduce reducer$add(njob, value) .manager_log(BPPARAM, njob, d) .manager_result_save(BPPARAM, njob, reducer$value()) if (bpstopOnError(BPPARAM) && !d$value$success) { ## stop on error; let running jobs finish, do not re-load ## FIXME: harvest assigned jobs reducer <- .clear_cluster(cl, running, reducer) break } ## re-load value <- ITER() if (inherits(value, "rng_iter")) { seed <- .rng_iterate_substream(seed, value) value <- ITER() } if (!identical(value, list(NULL))) { i <- i + 1L value_ <- .EXEC(i, .rng_lapply, ARGFUN(value, seed)) running[d$node] <- .send_to(cl, d$node, value_) seed <- .rng_iterate_substream(seed, length(value)) } } ## return results if (!is.na(bpresultdir(BPPARAM))) { NULL } else { reducer$value() } } bploop.iterate_batchtools <- function(manager, ITER, FUN, BPPARAM, REDUCE, init, reduce.in.order, ...) { ## get number of workers workers <- bpnworkers(BPPARAM) ## reduce in order reducer <- .reducer(REDUCE, init, reduce.in.order) ## progress bar. progress <- .progress(active=bpprogressbar(BPPARAM), iterate=TRUE) on.exit(progress$term(), TRUE) progress$init() def.id <- job.id <- 1L repeat{ value <- ITER() if (is.null(value)) { if (job.id == 1L) warning("first invocation of 'ITER()' returned NULL") break } ## save 'value' to registry tempfile fl <- tempfile(tmpdir = BPPARAM$registry$file.dir) saveRDS(value, fl) if (job.id == 1L) { suppressMessages({ ids <- batchtools::batchMap( fun = FUN, fl, more.args = list(...), reg = BPPARAM$registry ) }) } else { job.pars <- list(fl) BPPARAM$registry$defs <- rbind(BPPARAM$registry$defs, list(def.id, list(job.pars))) entry <- c(list(job.id, def.id), rep(NA, 10)) BPPARAM$registry$status <- rbind(BPPARAM$registry$status, entry) } def.id <- def.id + 1L job.id <- job.id + 1L } ## finish updating tables ids <- data.table::data.table(job.id = seq_len(job.id - 1)) data.table::setkey(BPPARAM$registry$status, "job.id") ids$chunk = batchtools::chunk(ids$job.id, n.chunks = workers) ## submit and wait for jobs batchtools::submitJobs( ids = ids, resources = .bpresources(BPPARAM), reg = BPPARAM$registry ) batchtools::waitForJobs( ids = BPPARAM$registry$status$job.id, reg = BPPARAM$registry, timeout = bptimeout(BPPARAM), stop.on.error = bpstopOnError(BPPARAM) ) ## reduce in order for (job.id in ids$job.id) { value <- batchtools::loadResult(id = job.id, reg=BPPARAM$registry) reducer$add(job.id, list(value)) } ## return reducer value reducer$value() } BiocParallel/R/bpmapply-methods.R0000644000175200017520000000402014136050065017732 0ustar00biocbuildbiocbuild### ========================================================================= ### bpmapply methods ### ------------------------------------------------------------------------- ## bpmapply() dispatches to bplapply() where errors and logging are handled. setMethod("bpmapply", c("ANY", "BiocParallelParam"), function(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) { ## re-package for lapply ddd <- .getDotsForMapply(...) if (!length(ddd) || !length(ddd[[1L]])) return(.mrename(list(), ddd, USE.NAMES)) FUN <- match.fun(FUN) wrap <- function(.i, .FUN, .ddd, .MoreArgs) { dots <- lapply(.ddd, `[`, .i) .mapply(.FUN, dots, .MoreArgs)[[1L]] } res <- bplapply(X=seq_along(ddd[[1L]]), wrap, .FUN=FUN, .ddd=ddd, .MoreArgs=MoreArgs, BPREDO=BPREDO, BPPARAM=BPPARAM) .simplify(.mrename(res, ddd, USE.NAMES), SIMPLIFY) }) setMethod("bpmapply", c("ANY", "missing"), function(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) bpmapply(FUN, ..., MoreArgs=MoreArgs, SIMPLIFY=SIMPLIFY, USE.NAMES=USE.NAMES, BPREDO=BPREDO, BPPARAM=BPPARAM) }) setMethod("bpmapply", c("ANY", "list"), function(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) if (!all(vapply(BPPARAM, inherits, logical(1), "BiocParallelParam"))) stop("All elements in 'BPPARAM' must be BiocParallelParam objects") if (length(BPPARAM) == 0L) stop("'length(BPPARAM)' must be > 0") myFUN <- if (length(BPPARAM) > 1L) { if (length(param <- BPPARAM[-1]) == 1L) function(...) FUN(..., BPPARAM=param[[1]]) else function(...) FUN(..., BPPARAM=param) } else FUN bpmapply(myFUN, ..., MoreArgs=MoreArgs, SIMPLIFY=SIMPLIFY, USE.NAMES=USE.NAMES, BPREDO=BPREDO, BPPARAM=BPPARAM[[1L]]) }) BiocParallel/R/bpschedule-methods.R0000644000175200017520000000023014136050065020223 0ustar00biocbuildbiocbuildsetMethod("bpschedule", "ANY", function(x) TRUE) setMethod("bpschedule", "missing", function(x) { x <- registered()[[1]] bpschedule(x) }) BiocParallel/R/bpstart-methods.R0000644000175200017520000000541114136050065017572 0ustar00biocbuildbiocbuild### ========================================================================= ### ClusterManager object: ensures started clusters are stopped ### ------------------------------------------------------------------------- .ClusterManager <- local({ ## package-global registry of backends; use to avoid closing ## socket connections of unreferenced backends during garbage ## collection -- bpstart(MulticoreParam(1)); gc(); gc() uid <- 0 env <- environment() list(add = function(cluster) { uid <<- uid + 1L cuid <- as.character(uid) env[[cuid]] <- cluster # protection cuid }, drop = function(cuid) { if (length(cuid) && cuid %in% names(env)) rm(list=cuid, envir=env) invisible(NULL) }, get = function(cuid) { env[[cuid]] }, ls = function() { cuid <- setdiff(ls(env), c("uid", "env")) cuid[order(as.integer(cuid))] }) }) ### ========================================================================= ### bpstart() methods ### ------------------------------------------------------------------------- setMethod("bpstart", "ANY", function(x, ...) invisible(x)) setMethod("bpstart", "missing", function(x, ...) { x <- registered()[[1]] bpstart(x) }) ## ## .bpstart_impl: common functionality after bpisup() ## .bpstart_error_handler <- function(x, response, id) { value <- lapply(response, function(elt) elt[["value"]][["value"]]) if (!all(bpok(value))) { on.exit(try(bpstop(x))) stop( "\nbpstart() ", id, " error:\n", conditionMessage(.error_bplist(value)) ) } } .bpstart_set_rng_stream <- function(x) { ## initialize the random number stream; increment the stream only ## in bpstart_impl .RNGstream(x) <- .rng_init_stream(bpRNGseed(x)) invisible(.RNGstream(x)) } .bpstart_set_logging <- function(x) { cluster <- bpbackend(x) value <- .EXEC(NULL, .log_load, list(bplog(x), bpthreshold(x), TRUE)) .send_all(cluster, value) response <- .recv_all(cluster) .bpstart_error_handler(x, response, "set_logging") invisible(x) } .bpstart_set_finalizer <- function(x) { if (length(x$.uid) == 0L) { finalizer_env <- as.environment(list(self=x$.self)) reg.finalizer( finalizer_env, function(e) bpstop(e[["self"]]), onexit=TRUE ) x$.finalizer_env <- finalizer_env } x$.uid <- .ClusterManager$add(bpbackend(x)) invisible(x) } .bpstart_impl <- function(x) { ## common actions once bpisup(backend) ## initialize the random number stream .bpstart_set_rng_stream(x) ## logging if (bplog(x)) .bpstart_set_logging(x) ## clean up when x left open .bpstart_set_finalizer(x) } BiocParallel/R/bpstop-methods.R0000644000175200017520000000075614136050065017431 0ustar00biocbuildbiocbuildsetMethod("bpstop", "ANY", function(x) invisible(x)) setMethod("bpstop", "missing", function(x) { x <- registered()[[1]] bpstop(x) }) ## ## .bpstop_impl: common functionality after bpisup() is no longer TRUE ## .bpstop_nodes <- function(x) { cluster <- bpbackend(x) for (i in seq_along(cluster)) .send_to(cluster, i, .DONE()) TRUE } .bpstop_impl <- function(x) { bpisup(x) && .bpstop_nodes(x) .ClusterManager$drop(x$.uid) invisible(x) } BiocParallel/R/bpvalidate.R0000644000175200017520000001136414136050065016571 0ustar00biocbuildbiocbuild.BPValidate <- setClass( "BPValidate", slots = c( symbol = "character", environment = "character", unknown = "character" ) ) BPValidate <- function(symbol = character(), environment = character(), unknown = character()) { if (is.null(symbol)) symbol <- character() if (is.null(environment)) environment <- character() .BPValidate(symbol = symbol, environment = environment, unknown = unknown) } .bpvalidateSymbol <- function(x) x@symbol .bpvalidateEnvironment <- function(x) x@environment .bpvalidateUnknown <- function(x) x@unknown .show_bpvalidateSearch <- function(x) { search <- data.frame( symbol = .bpvalidateSymbol(x), environment = .bpvalidateEnvironment(x), row.names = NULL ) output <- capture.output(search) text <- ifelse(NROW(search), paste(output, collapse = "\n "), "none") c("symbol(s) in search() path:\n ", text) } .show_bpvalidateUnknown <- function(x) { unknown <- .bpvalidateUnknown(x) text <- ifelse(length(unknown), paste(unknown, collapse = "\n "), "none") c("unknown symbol(s):\n ", text) } setMethod("show", "BPValidate", function(object) { cat( "class: ", class(object), "\n", .show_bpvalidateSearch(object), "\n\n", .show_bpvalidateUnknown(object), "\n\n", sep = "" ) }) bpvalidate <- function(fun, signal = c("warning", "error", "silent")) { typeof <- suppressWarnings(typeof(fun)) if (!typeof %in% c("closure", "builtin")) stop("'fun' must be a closure or builtin") if (is.function(signal)) { ERROR_FUN <- signal } else { ERROR_FUN <- switch( match.arg(signal), warning = warning, error = stop, silent = capture.output ) } unknown <- codetools::findGlobals(fun) f_env <- environment(fun) ## 'fun' environment is NAMESPACE if (length(unknown) && isNamespace(f_env)) { f_ls <- c( getNamespaceImports(f_env), setNames(list(ls(f_env, all.names=TRUE)), getNamespaceName(f_env)) ) f_symbols <- unique(unlist(f_ls, use.names=FALSE)) unknown <- unknown[!unknown %in% f_symbols] } ## 'fun' body loads libraries warn <- err <- NULL if (any(c("require", "library") %in% unknown)) { xx <- withCallingHandlers(tryCatch({ dep <- deparse(fun) i <- grepl("(library|require)", dep) sapply(dep[i], function(elt) { pkg <- gsub(")", "", strsplit(elt, "(", fixed=TRUE)[[1]][2]) unknown %in% getNamespaceExports(pkg) }) }, error=function(e) { err <<- append(err, conditionMessage(e)) NULL }), warning=function(w) { warn <<- append(warn, conditionMessage(w)) invokeRestart("muffleWarning") }) if (!is.null(warn) || !is.null(err)) stop("\nattempt to load library failed:\n ", paste(c(warn, err), collapse="\n ")) if (!is.matrix(xx)) xx <- t(as.matrix(xx)) unknown <- unknown[rowSums(xx) == 0L] } ## look in search path inpath <- structure(list(), names=character()) if (length(unknown)) { ## exclude variables found in defining environment(s) env <- environment(fun) while (!identical(env, topenv(environment(fun)))) { inlocal <- ls(env, all.names = TRUE) unknown <- setdiff(unknown, inlocal) env <- parent.env(env) } inpath <- .foundInPath(unknown) unknown <- setdiff(unknown, names(inpath)) inpath <- .filterDefaultPackages(inpath) } result <- BPValidate( symbol = names(inpath), environment = unlist(inpath, use.names = FALSE), unknown = unknown ) ## error report msg <- character() test <- .bpvalidateEnvironment(result) %in% ".GlobalEnv" if (any(test)) msg <- c( msg, "symbol(s) in .GlobalEnv:\n ", paste(.bpvalidateSymbol(result)[test], collapse = "\n "), "\n" ) test <- .bpvalidateUnknown(result) if (length(test)) msg <- c( msg, "unknown symbol(s):\n ", paste(test, collapse = "\n "), "\n" ) if (length(msg)) ERROR_FUN("\n", paste(msg, collapse = ""), call. = FALSE) result } .foundInPath <- function(symbols) { loc <- Map(function(elt) head(find(elt), 1), symbols) loc[lengths(loc) == 1L] } .filterDefaultPackages <- function(symbols) { pkgs <- c( "stats", "graphics", "grDevices", "utils", "datasets", "methods", "Autoloads", "base" ) drop <- unlist(symbols, use.names = FALSE) %in% paste0("package:", pkgs) symbols[!drop] } BiocParallel/R/bpvec-methods.R0000644000175200017520000000452114136050065017213 0ustar00biocbuildbiocbuild### ========================================================================= ### bpvec methods ### ------------------------------------------------------------------------- ## bpvec() dispatches to bplapply() where errors and logging are ## handled. setMethod("bpvec", c("ANY", "BiocParallelParam"), function(X, FUN, ..., AGGREGATE=c, BPREDO=list(), BPPARAM=bpparam()) { if (!length(X)) return(.rename(list(), X)) FUN <- match.fun(FUN) AGGREGATE <- match.fun(AGGREGATE) if (!bpschedule(BPPARAM)) { param <- as(BPPARAM, "SerialParam") return( bpvec( X, FUN, ..., AGGREGATE=AGGREGATE, BPREDO=BPREDO, BPPARAM = param ) ) } si <- .splitX(seq_along(X), bpnworkers(BPPARAM), bptasks(BPPARAM)) otasks <- bptasks(BPPARAM) bptasks(BPPARAM) <- 0L on.exit(bptasks(BPPARAM) <- otasks) idx <- .redo_index(si, BPREDO) if (any(idx)) si <- si[idx] ## FIXME: 'X' sent to all workers, but ith worker only needs X[i] FUN1 <- function(i, ...) FUN(X[i], ...) res <- bptry(bplapply(si, FUN1, ..., BPREDO=BPREDO[idx], BPPARAM=BPPARAM)) if (any(idx)) { BPREDO[idx] <- res res <- BPREDO } if (is(res, "error") || !all(bpok(res))) stop(.error_bplist(res)) if (any(lengths(res) != lengths(si))) stop(.error("length(FUN(X)) not equal to length(X)", "bpvec_error")) do.call(AGGREGATE, res) }) setMethod("bpvec", c("ANY", "missing"), function(X, FUN, ..., AGGREGATE=c, BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) AGGREGATE <- match.fun(AGGREGATE) bpvec(X, FUN, ..., AGGREGATE=AGGREGATE, BPREDO=BPREDO, BPPARAM=BPPARAM) }) setMethod("bpvec", c("ANY", "list"), function(X, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) if (!all(vapply(BPPARAM, inherits, logical(1), "BiocParallelParam"))) stop("All elements in 'BPPARAM' must be BiocParallelParam objects") if (length(BPPARAM) == 0L) stop("'length(BPPARAM)' must be > 0") myFUN <- if (length(BPPARAM) > 1L) { param <- BPPARAM[-1] if (length(param) == 1L) function(...) FUN(..., BPPARAM=param[[1]]) else function(...) FUN(..., BPPARAM=param) } else FUN bpvec(X, myFUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM[[1]]) }) BiocParallel/R/bpvectorize-methods.R0000644000175200017520000000062114136050065020445 0ustar00biocbuildbiocbuildsetMethod("bpvectorize", c("ANY", "ANY"), function(FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) function(...) bpvec(FUN=FUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM) }) setMethod("bpvectorize", c("ANY", "missing"), function(FUN, ..., BPREDO=list(), BPPARAM=bpparam()) { FUN <- match.fun(FUN) bpvectorize(FUN, ..., BPREDO=BPREDO, BPPARAM=BPPARAM) }) BiocParallel/R/bpworkers-methods.R0000644000175200017520000000031514136050065020127 0ustar00biocbuildbiocbuildsetMethod("bpworkers", "missing", function(x) { x <- registered()[[1]] bpworkers(x) }) bpnworkers <- function(x) { n <- bpworkers(x) if (!is.numeric(n)) n <- length(n) n } BiocParallel/R/ipcmutex.R0000644000175200017520000000122714136050065016311 0ustar00biocbuildbiocbuild## Utilities ipcid <- function(id) { uuid <- .Call(.ipc_uuid) if (!missing(id)) uuid <- paste(as.character(id), uuid, sep="-") uuid } ipcremove <- function(id) { invisible(.Call(.ipc_remove, id)) } ## Locks ipclocked <- function(id) .Call(.ipc_locked, id) ipclock <- function(id) { .Call(.ipc_lock, id) } ipctrylock <- function(id) { .Call(.ipc_try_lock, id) } ipcunlock <- function(id) { .Call(.ipc_unlock, id) } ## Counters ipcyield <- function(id) { .Call(.ipc_yield, id) } ipcvalue <- function(id) { .Call(.ipc_value, id) } ipcreset <- function(id, n = 1) { invisible(.Call(.ipc_reset, id, n)) } BiocParallel/R/log.R0000644000175200017520000000214514136050065015234 0ustar00biocbuildbiocbuild.log_load <- function(log, threshold, appender = FALSE) { if (!log) return(invisible(NULL)) if (!isNamespaceLoaded("futile.logger")) tryCatch({ loadNamespace("futile.logger") }, error=function(err) { msg <- "logging requires the 'futile.logger' package" stop(conditionMessage(err), msg) }) if (appender) futile.logger::flog.appender(.log_buffer_append, 'ROOT') futile.logger::flog.threshold(threshold) futile.logger::flog.info("loading futile.logger package") } .log_warn <- function(log, fmt, ...) { if (log) futile.logger::flog.warn(fmt, ...) } .log_error <- function(log, fmt, ...) { if (log) futile.logger::flog.error(fmt, ...) } ## logging buffer .log_buffer <- local({ env <- new.env(parent=emptyenv()) env[["buffer"]] <- character() env }) .log_buffer_init <- function() { .log_buffer[["buffer"]] <- NULL } .log_buffer_append <- function(line) { .log_buffer[["buffer"]] <- c(.log_buffer[["buffer"]], line) } .log_buffer_get <- function() { .log_buffer[["buffer"]] } BiocParallel/R/progress.R0000644000175200017520000000257714136050065016330 0ustar00biocbuildbiocbuild### ========================================================================= ### progress bar ### ------------------------------------------------------------------------- .progress <- function(style = 3, active = TRUE, iterate = FALSE, ...) { if (active) { ntasks <- 0L if (iterate) { list(init = function(x) { message("iteration: ", appendLF=FALSE) }, step = function() { ntasks <<- ntasks + 1L erase <- paste(rep("\b", ceiling(log10(ntasks))), collapse="") message(erase, ntasks, appendLF = FALSE) }, term = function() { message() # new line }) } else { ## derived from plyr::progress_text() txt <- NULL max <- 0 list(init = function(x) { txt <<- txtProgressBar(max = x, style = style, ...) setTxtProgressBar(txt, 0) max <<- x }, step = function() { ntasks <<- ntasks + 1L setTxtProgressBar(txt, ntasks) if (ntasks == max) cat("\n") }, term = function() { close(txt) }) } } else { list( init = function(x) NULL, step = function() NULL, term = function() NULL ) } } BiocParallel/R/prototype.R0000644000175200017520000000031114136050065016511 0ustar00biocbuildbiocbuild.prototype_update <- function(prototype, ...) { args <- list(...) stopifnot( all(names(args) %in% names(prototype)) ) prototype[names(args)] <- unname(args) prototype } BiocParallel/R/register.R0000644000175200017520000000522614136050065016302 0ustar00biocbuildbiocbuild### ========================================================================= ### .registry object ### ------------------------------------------------------------------------- .registry <- setRefClass(".BiocParallelRegistry", fields=list( bpparams = "list"), methods=list( register = function(BPPARAM, default = TRUE) { BPPARAM <- eval(BPPARAM) if ((!length(BPPARAM) == 1) || !is(BPPARAM, "BiocParallelParam")) stop("'BPPARAM' must be a 'BiocParallelParam' instance") .self$bpparams[[class(BPPARAM)]] <- BPPARAM if (default) { idx <- match(class(BPPARAM), names(.self$bpparams)) .self$bpparams <- c(.self$bpparams[idx], .self$bpparams[-idx]) } invisible(registered()) }, registered = function(bpparamClass) { if (missing(bpparamClass)) .self$bpparams else .self$bpparams[[bpparamClass]] }) )$new() # Singleton .register <- .registry$register .registered <- .registry$registered .registry_init <- function() { multicore <- .detectCores() > 1L tryCatch({ if ((.Platform$OS.type == "windows") && multicore) { .register(getOption( "SnowParam", SnowParam() ), TRUE) .register(getOption("SerialParam", SerialParam()), FALSE) } else if (multicore) { ## linux / mac .register(getOption( "MulticoreParam", MulticoreParam() ), TRUE) .register(getOption( "SnowParam", SnowParam() ), FALSE) .register(getOption("SerialParam", SerialParam()), FALSE) } else { .register(getOption("SerialParam", SerialParam()), TRUE) } }, error=function(err) { message( "'BiocParallel' did not register default BiocParallelParam:\n", " ", conditionMessage(err) ) NULL }) } register <- function(BPPARAM, default = TRUE) { if (length(.registry$bpparams) == 0L) .registry_init() .register(BPPARAM, default = default) } registered <- function(bpparamClass) { if (length(.registry$bpparams) == 0L) .registry_init() .registered(bpparamClass) } bpparam <- function(bpparamClass) { if (missing(bpparamClass)) bpparamClass <- names(registered())[1] default <- registered()[[bpparamClass]] result <- getOption(bpparamClass, default) if (is.null(result)) stop("BPPARAM '", bpparamClass, "' not registered() or in names(options())") result } BiocParallel/R/rng.R0000644000175200017520000001005614154324272015245 0ustar00biocbuildbiocbuild## .rng_get_generator(): get the current generator kind and seed .rng_get_generator <- function() { seed <- if (exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) { get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } else NULL kind <- RNGkind() list(kind = kind, seed = seed) } ## .rng_reset_generator(): reset the generator to a state returned by ## .rng_get_generator() .rng_reset_generator <- function(kind, seed) { ## Setting RNGkind() changes the seed, so restore the original ## seed after restoring the kind RNGkind(kind[[1]]) if (is.null(seed)) { rm(.Random.seed, envir = .GlobalEnv) } else { assign(".Random.seed", seed, envir = .GlobalEnv) } list(kind = kind, seed = seed) } ## .rng_init_stream(): initialize the generator to a new kind, ## optionally using `set.seed()` to set the seed for the the ## generator. .rng_init_stream <- function(seed) { state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) ## coerces seed to appropriate format for RNGkind; NULL seed (from ## bpRNGseed()) uses the global random number stream. if (!is.null(seed)) { RNGkind("default", "default", "default") set.seed(seed) ## change kind kind <- "L'Ecuyer-CMRG" RNGkind(kind) ## .Random.seed always exists after RNGkind() seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) } else { .rng_internal_stream$set() seed <- get(".Random.seed", envir = .GlobalEnv, inherits = FALSE) ## advance internal stream by 1 runif(1) .rng_internal_stream$reset() } seed } ## .rng_next_stream(): return the next stream for a parallel job .rng_next_stream <- function(seed) { ## `nextRNGStream()` does not require that the current stream is ## L'Ecuyer-CMRG if (is.null(seed)) seed <- .rng_init_stream(seed) nextRNGStream(seed) } .rng_next_substream <- function(seed) { if (is.null(seed)) seed <- .rng_init_stream(seed) nextRNGSubStream(seed) } ## iterate the seed stream n times .rng_iterate_substream <- function(seed, n) { for (k in seq_len(n)) seed <- .rng_next_substream(seed) seed } ## a random number stream independent of the stream used by R. Use for ## port and other 'internal' assignments without changing the random ## number sequence of users. .rng_internal_stream <- local({ state <- .rng_get_generator() RNGkind("L'Ecuyer-CMRG") # sets .Random.seed to non-NULL value internal_seed <- .Random.seed .rng_reset_generator(state$kind, state$seed) list(set = function() { state <<- .rng_get_generator() internal_seed <<- .rng_reset_generator("L'Ecuyer-CMRG", internal_seed) }, reset = function() { internal_seed <<- .rng_get_generator()$seed .rng_reset_generator(state$kind, state$seed) }) }) ## .rng_job_fun_factory(): use in conjunction with ## .rng_lapply() to coordinate streams across tasks in a job, ## independent of how the job are organized into tasks. .rng_job_fun_factory <- function(FUN, SEED) { ## 'on.exit' is of .rng_job_fun_factory(), not when the anonymous ## function goes out of scope state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) force(FUN) SEED <- .rng_reset_generator("L'Ecuyer-CMRG", SEED)$seed function(...) { ## get / reset the stream each time the function is evaluated state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) .rng_reset_generator("L'Ecuyer-CMRG", SEED) result <- FUN(...) SEED <<- .rng_next_substream(SEED) result } } ## lapply, but with 'FUN()' wrapped so that each call uses a new ## random number stream .rng_lapply <- function(X, FUN, ..., BPRNGSEED) { state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) FUN <- .rng_job_fun_factory(FUN, BPRNGSEED) lapply(X, FUN, ...) } BiocParallel/R/utilities.R0000644000175200017520000001624314136050065016472 0ustar00biocbuildbiocbuild.getEnvironmentVariable <- function(variable, default = NA_integer_) { result <- withCallingHandlers({ value <- Sys.getenv(variable, default) as.integer(value) }, warning = function(w) { txt <- sprintf( "Trying to coercing the environment variable '%s' to an integer caused a warning. The value of the environment variable was '%s'. The warning was: %s", variable, value, conditionMessage(w) ) .warning(txt) invokeRestart("muffleWarning") }) if (!is.na(result) && (result <= 0L)) { txt <- sprintf( "The environment variable '%s' must be > 0. The value was '%d'.", variable, result ) .stop(txt) } result } .detectCores <- function() { ## environment variables; least to most compelling result <- .getEnvironmentVariable("R_PARALLELLY_AVAILABLECORES_FALLBACK") result <- .getEnvironmentVariable("BIOCPARALLEL_WORKER_NUMBER", result) ## fall back to detectCores() if necessary if (is.na(result)) { result <- parallel::detectCores() if (is.na(result)) result <- 1L result <- max(1L, result - 2L) } ## respect 'mc.cores', overriding env. variables an detectCores() result <- getOption("mc.cores", result) ## override user settings by build-system configurations if (nzchar(Sys.getenv("_R_CHECK_LIMIT_CORES_"))) result <- min(result, 2L) if (nzchar(Sys.getenv("BBS_HOME"))) result <- min(result, 4L) result } .splitIndices <- function (nx, tasks) { ## derived from parallel i <- seq_len(nx) if (nx == 0L) list() else if (tasks <= 1L || nx == 1L) # allow nx, nc == 0 list(i) else { fuzz <- min((nx - 1L)/1000, 0.4 * nx / tasks) breaks <- seq(1 - fuzz, nx + fuzz, length.out = tasks + 1L) si <- structure(split(i, cut(i, breaks)), names = NULL) si[sapply(si, length) != 0] } } .split_X_redo <- function(X, redo_index, elements_per_task, n) { ## Whether we need to calculate n? prealloc <- missing(n) splittedX <- vector("list", ifelse(prealloc, 0L, n)) last_redo_status <- redo_index[[1]] task_i <- 1L x_start <- 1L x_len <- 0L for (i in seq_along(redo_index)) { redo <- redo_index[[i]] ## The task division depends on two factors: ## 1. Whether the current redo is different from the previous one? ## 2. Whether the elements number in a task will exceed ## `elements_per_task` limit is_switch <- xor(last_redo_status, redo) is_switch <- ifelse( is_switch || !redo, is_switch, x_len >= elements_per_task ) ## If we want to split X at this position and it is not in the ## preallocation stage, we create a subset of X or ## .bploop_rng_iter based on the previous redo value if (!prealloc && is_switch) { if (last_redo_status) splittedX[[task_i]] <- X[seq.int(x_start, length.out = x_len)] else splittedX[[task_i]] <- .bploop_rng_iter(x_len) } ## if we split X, record the next redo status, start index, ## add task index by 1 and reset the length, otherwise we just ## add the length by 1 if (is_switch) { x_start <- ifelse(last_redo_status, x_start + x_len, x_start) last_redo_status <- redo task_i <- task_i + 1L x_len <- 1L } else { x_len <- x_len + 1 } if (!prealloc && task_i >= n) break } ## The last task does not have the "switch" so we need to manually add it if (!prealloc && last_redo_status) { splittedX[[task_i]] <- X[seq.int(x_start, length(X))] } if (prealloc) { task_i - !last_redo_status } else { splittedX } } .splitX <- function(X, workers, tasks, redo_index = NULL) { if (tasks == 0L) { tasks <- workers } else { tasks <- min(length(X), tasks) } ## If redo index presents, split X based on the index while ## preserving the correct rng stream. it will respect `tasks` ## value if (length(redo_index)) { ## Two-pass algorithm, the first pass calculate the required ## list size to allocate, then do the real allocation elements_per_task <- ceiling(length(X) / max(tasks, 1L)) n <- .split_X_redo(X, redo_index, elements_per_task) .split_X_redo(X, redo_index, elements_per_task, n) } else { idx <- .splitIndices(length(X), tasks) relist(X, idx) } } .redo_index <- function(X, BPREDO) { idx <- !bpok(BPREDO) if (length(idx)) { if (length(BPREDO) != length(X)) stop("'length(BPREDO)' must equal 'length(X)'") if (!any(idx)) stop("no previous error in 'BPREDO'") } idx } ## re-apply names on X of lapply(X, FUN) to the return value .rename <- function(results, X) { names(results) <- names(X) results } ## re-apply the names on, e.g., mapply(FUN, X) to the return value; ## see test_mrename for many edge cases .mrename <- function(results, dots, USE.NAMES=TRUE) { ## dots: a list() containing one element for each ... argument ## passed to mapply if (USE.NAMES) { ## extract the first argument; if there are no arguments, then ## dots is (unnamed) list(0) if (length(dots)) dots <- dots[[1L]] if (is.character(dots) && is.null(names(dots))) { names(results) <- dots } else { names(results) <- names(dots) } } else { results <- unname(results) } results } .simplify <- function(results, SIMPLIFY=FALSE) { if (SIMPLIFY && length(results)) results <- simplify2array(results) results } .prettyPath <- function(tag, filepath) { wd <- options('width')[[1]] - nchar(tag) - 6 if (length(filepath) == 0 || is.na(filepath)) return(sprintf("%s: %s", tag, NA_character_)) if (0L == length(filepath) || nchar(filepath) < wd) return(sprintf("%s: %s", tag, filepath)) bname <- basename(filepath) wd1 <- wd - nchar(bname) dname <- substr(dirname(filepath), 1, wd1) sprintf("%s: %s...%s%s", tag, dname, .Platform$file.sep, bname) } .getDotsForMapply <- function(...) { ddd <- list(...) if (!length(ddd)) return(list(list())) len <- vapply(ddd, length, integer(1L)) if (!all(len == len[1L])) { max.len <- max(len) if (max.len && any(len == 0L)) stop("zero-length and non-zero length inputs cannot be mixed") if (any(max.len %% len)) warning("longer argument not a multiple of length of vector") ddd <- lapply(ddd, rep_len, length.out=max.len) } ddd } .dir_valid_rw <- function(x) { all(file.access(x, 6L) == 0L) } .warning <- function(...) { msg <- paste( strwrap(paste0("\n", ...), indent = 2, exdent = 2), collapse="\n" ) warning(msg, call. = FALSE) } .stop <- function(...) { msg <- paste( strwrap(paste0("\n", ...), indent = 2, exdent = 2), collapse="\n" ) stop(msg, call. = FALSE) } BiocParallel/README.md0000644000175200017520000000134714136050065015411 0ustar00biocbuildbiocbuildBiocParallel ============ Bioconductor facilities for parallel evaluation (experimental) Possible TODO ------------- + map/reduce-like function + bpforeach? + Abstract scheduler + lazy DoparParam + SnowParam support for setSeed, recursive, cleanup + subset SnowParam DONE ---- + encapsulate arguments as ParallelParam() + Standardize signatures + Make functions generics + parLapply-like function + Short vignette + elaborate SnowParam for SnowSocketParam, SnowForkParam, SnowMpiParam, ... + MulticoreParam on Windows github notes ------------ + commit one-liners with names git log --pretty=format:"- %h %an: %s" TO FIX ------------- + DoparParam does not pass foreach args (specifically access to .options.nws for chunking) BiocParallel/build/0000755000175200017520000000000014154342106015224 5ustar00biocbuildbiocbuildBiocParallel/build/vignette.rds0000644000175200017520000000055314154342106017566 0ustar00biocbuildbiocbuild‹Í‘]KÃ0†³®N7P†»5z-?þ€£†"c컵Y-´ÉHR†wûáâ<]“­-RñNB¾Îyyó䜷BÈAnËANŽí,˜}˜.Œ.ìx‹`B%M–!ÕÁ»"Qy(õ¦|etçOR ©È‹ˆyä!ñÙ<‹¢˜G%s-E˜:œÌ)›—t§SÊC‘’×,3©¶™*ÖõW1ÃZàœ»÷pÁvƒs8 ÖxgD—·?Ø•ØŒìêÁÃ6h8æUá_ª· ¿Wo/j®Þ^W¯^ž©b]47Õ¨ÎZjšjTýz;!6ØâX¨a/ëãw´¬¶ådÌ’*-#“ì?©]¢-“=à4eÊXtLÐÅ ³‚Y¬w—öÄ™cËVòÐgKÆCeŸf+!á^}¨+Åʳç¼kX6›Íg(H¨²D6Ø ©¦ü#Í¿ûõ ÝÝÁ'BiocParallel/cleanup0000755000175200017520000000002314154342106015475 0ustar00biocbuildbiocbuildrm -f src/Makevars BiocParallel/configure0000755000175200017520000032711514154342106016045 0ustar00biocbuildbiocbuild#! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.69 for BiocParallel 1.17.11. # # # Copyright (C) 1992-1996, 1998-2012 Free Software Foundation, Inc. # # # This configure script is free software; the Free Software Foundation # gives unlimited permission to copy, distribute and modify it. ## -------------------- ## ## M4sh Initialization. ## ## -------------------- ## # Be more Bourne compatible DUALCASE=1; export DUALCASE # for MKS sh if test -n "${ZSH_VERSION+set}" && (emulate sh) >/dev/null 2>&1; then : emulate sh NULLCMD=: # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which # is contrary to our usage. Disable this feature. alias -g '${1+"$@"}'='"$@"' setopt NO_GLOB_SUBST else case `(set -o) 2>/dev/null` in #( *posix*) : set -o posix ;; #( *) : ;; esac fi as_nl=' ' export as_nl # Printing a long string crashes Solaris 7 /usr/bin/printf. as_echo='\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\' as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo as_echo=$as_echo$as_echo$as_echo$as_echo$as_echo$as_echo # Prefer a ksh shell builtin over an external printf program on Solaris, # but without wasting forks for bash or zsh. if test -z "$BASH_VERSION$ZSH_VERSION" \ && (test "X`print -r -- $as_echo`" = "X$as_echo") 2>/dev/null; then as_echo='print -r --' as_echo_n='print -rn --' elif (test "X`printf %s $as_echo`" = "X$as_echo") 2>/dev/null; then as_echo='printf %s\n' as_echo_n='printf %s' else if test "X`(/usr/ucb/echo -n -n $as_echo) 2>/dev/null`" = "X-n $as_echo"; then as_echo_body='eval /usr/ucb/echo -n "$1$as_nl"' as_echo_n='/usr/ucb/echo -n' else as_echo_body='eval expr "X$1" : "X\\(.*\\)"' as_echo_n_body='eval arg=$1; case $arg in #( *"$as_nl"*) expr "X$arg" : "X\\(.*\\)$as_nl"; arg=`expr "X$arg" : ".*$as_nl\\(.*\\)"`;; esac; expr "X$arg" : "X\\(.*\\)" | tr -d "$as_nl" ' export as_echo_n_body as_echo_n='sh -c $as_echo_n_body as_echo' fi export as_echo_body as_echo='sh -c $as_echo_body as_echo' fi # The user is always right. if test "${PATH_SEPARATOR+set}" != set; then PATH_SEPARATOR=: (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && { (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 || PATH_SEPARATOR=';' } fi # IFS # We need space, tab and new line, in precisely that order. Quoting is # there to prevent editors from complaining about space-tab. # (If _AS_PATH_WALK were called with IFS unset, it would disable word # splitting by setting IFS to empty value.) IFS=" "" $as_nl" # Find who we are. Look in the path if we contain no directory separator. as_myself= case $0 in #(( *[\\/]* ) as_myself=$0 ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS test -z "$as_dir" && as_dir=. test -r "$as_dir/$0" && as_myself=$as_dir/$0 && break done IFS=$as_save_IFS ;; esac # We did not find ourselves, most probably we were run as `sh COMMAND' # in which case we are not to be found in the path. if test "x$as_myself" = x; then as_myself=$0 fi if test ! -f "$as_myself"; then $as_echo "$as_myself: error: cannot find myself; rerun with an absolute file name" >&2 exit 1 fi # Unset variables that we do not need and which cause bugs (e.g. in # pre-3.0 UWIN ksh). 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Introduction to BatchtoolsParam} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ <>= suppressPackageStartupMessages({ library(BiocParallel) }) @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Introduction to \emph{BatchtoolsParam}} \author{ Nitesh Turaga\footnote{\url{Nitesh.Turaga@RoswellPark.org}}, Martin Morgan\footnote{\url{Martin.Morgan@RoswellPark.org}} } \date{Edited: March 22, 2018; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The \Rcode{BatchtoolsParam} class is an interface to the \CRANpkg{batchtools} package from within \BiocParallel{}. This aims to replace BatchjobsParam as \BiocParallel{}'s class for computing on a high performance cluster such as SGE, TORQUE, LSF, SLURM, OpenLava. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Quick start} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This example demonstrates the easiest way to launch a 100000 jobs using batchtools. The first step involves creating a \Rcode{BatchtoolsParam} class. You can compute using 'bplapply' and then the result is stored. <>= library(BiocParallel) ## Pi approximation piApprox <- function(n) { nums <- matrix(runif(2 * n), ncol = 2) d <- sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } piApprox(1000) ## Apply piApprox over param <- BatchtoolsParam() result <- bplapply(rep(10e5, 10), piApprox, BPPARAM=param) mean(unlist(result)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\emph{BatchtoolsParam} interface} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The \Rcode{BatchtoolsParam} interface allows you to replace the BatchJobsParam interface, and allow more intuitive usage of your high performance cluster with \BiocParallel{}. The \Rcode{BatchtoolsParam} class allows the user to specify many arguments to customize their jobs. Applicable to clusters with formal schedulers. \begin{itemize} \item{\Rcode{workers}} The number of workers used by the job. \item{\Rcode{cluster}} We currently support, SGE, SLURM, LSF, TORQUE and OpenLava. The 'cluster' argument is supported only if the R environment knows how to find the job scheduler. Each cluster type uses a template to pass the job to the scheduler. If the template is not given we use the default templates as given in the 'batchtools' package. The cluster can be accessed by 'bpbackend(param)'. \item{\Rcode{registryargs}} The 'registryargs' argument takes a list of arguments to create a new job registry for you \Rcode{BatchtoolsParam}. The job registry is a data.table which stores all the required information to process your jobs. The arguments we support for registryargs are: \begin{description} \item{\Rcode{file.dir}} Path where all files of the registry are saved. Note that some templates do not handle relative paths well. If nothing is given, a temporary directory will be used in your current working directory. \item{\Rcode{work.dir}} Working directory for R process for running jobs. \item{\Rcode{packages}} Packages that will be loaded on each node. \item{\Rcode{namespaces}} Namespaces that will be loaded on each node. \item{\Rcode{source}} Files that are sourced before executing a job. \item{\Rcode{load}} Files that are loaded before executing a job. \end{description} <<>>= registryargs <- batchtoolsRegistryargs( file.dir = "mytempreg", work.dir = getwd(), packages = character(0L), namespaces = character(0L), source = character(0L), load = character(0L) ) param <- BatchtoolsParam(registryargs = registryargs) param @ \item{\Rcode{resources}} A named list of key-value pairs to be subsituted into the template file; see \Rcode{?batchtools::submitJobs}. \item{\Rcode{template}} The template argument is unique to the \Rcode{BatchtoolsParam} class. It is required by the job scheduler. It defines how the jobs are submitted to the job scheduler. If the template is not given and the cluster is chosen, a default template is selected from the batchtools package. \item{\Rcode{log}} The log option is logical, TRUE/FALSE. If it is set to TRUE, then the logs which are in the registry are copied to directory given by the user using the \Rcode{logdir} argument. \item{\Rcode{logdir}} Path to the logs. It is given only if \Rcode{log=TRUE}. \item{\Rcode{resultdir}} Path to the directory is given when the job has files to be saved in a directory. \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Defining templates} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The job submission template controls how the job is processed by the job scheduler on the cluster. Obviously, the format of the template will differ depending on the type of job scheduler. Let's look at the default SLURM template as an example: <<>>= fname <- batchtoolsTemplate("slurm") cat(readLines(fname), sep="\n") @ The \Rcode{<\%= =>} blocks are automatically replaced by the values of the elements in the \Rcode{resources} argument in the \Rcode{BatchtoolsParam} constructor. Failing to specify critical parameters properly (e.g., wall time or memory limits too low) will cause jobs to crash, usually rather cryptically. We suggest setting parameters explicitly to provide robustness to changes to system defaults. Note that the \Rcode{<\%= =>} blocks themselves do not usually need to be modified in the template. The part of the template that is most likely to require explicit customization is the last line containing the call to \Rcode{Rscript}. A more customized call may be necessary if the R installation is not standard, e.g., if multiple versions of R have been installed on a cluster. For example, one might use instead: \begin{verbatim} echo 'batchtools::doJobCollection("<%= uri %>")' |\ ArbitraryRcommand --no-save --no-echo \end{verbatim} If such customization is necessary, we suggest making a local copy of the template, modifying it as required, and then constructing a \Rcode{BiocParallelParam} object with the modified template using the \Rcode{template} argument. However, we find that the default templates accessible with \Rcode{batchtoolsTemplate} are satisfactory in most cases. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Use cases} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% As an example for a BatchtoolParam job being run on an SGE cluster, we use the same \Rcode{piApprox} function as defined earlier. The example runs the function on 5 workers and submits 100 jobs to the SGE cluster. Example of SGE with minimal code: <>= library(BiocParallel) ## Pi approximation piApprox <- function(n) { nums <- matrix(runif(2 * n), ncol = 2) d <- sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } template <- system.file( package = "BiocParallel", "unitTests", "test_script", "test-sge-template.tmpl" ) param <- BatchtoolsParam(workers=5, cluster="sge", template=template) ## Run parallel job result <- bplapply(rep(10e5, 100), piApprox, BPPARAM=param) @ Example of SGE demonstrating some of \Rcode{BatchtoolsParam} methods. <>= library(BiocParallel) ## Pi approximation piApprox <- function(n) { nums <- matrix(runif(2 * n), ncol = 2) d <- sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } template <- system.file( package = "BiocParallel", "unitTests", "test_script", "test-sge-template.tmpl" ) param <- BatchtoolsParam(workers=5, cluster="sge", template=template) ## start param bpstart(param) ## Display param param ## To show the registered backend bpbackend(param) ## Register the param register(param) ## Check the registered param registered() ## Run parallel job result <- bplapply(rep(10e5, 100), piApprox) bpstop(param) @ \section{\Rcode{sessionInfo()}} <>= toLatex(sessionInfo()) @ \end{document} BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf0000644000175200017520000051621714154342007024052 0ustar00biocbuildbiocbuild%PDF-1.5 %ÐÔÅØ 46 0 obj << /Length 2103 /Filter /FlateDecode >> stream xÚí[Y“ÛÆ~ß_*?L£¹$®JdK*»$[õàZ)UXÜED4ê¨Tþ{zrAìZÞ]P*À\Ý=ÓýuOÏGŽžžàÎóÑøäán"ŒR2Ï"n0¢‚FRÀSGãit“Ñ»ñ'aà 0XhO%±RQCýFÕ«§~xÃcb‡gH*%iÂüà?åMV_¢ñH›x5JH\Hœ^¤·D£$t0ˆKî;¼*ë#†ãl>1¢&NíGõ•ö£ºð¼þ†¼aq”Œ¸#Ó!å|îùlò=¬¦Åu!ÅBÞ‹ñÝz?|"ID1’’ +w™ALEŠƒY~(šjDq\NW“&/‹QB•Ž›Ò ýi»?Ì€#×õQÚLFÇ—MYÎëvÒ*]l‰ÿð cáˆqʲ@°FB3à…"nd[ÍF “* W(™gHd$u:a0@@I(–-õïRˆë(iµ:õcÛe&n™Ãwé¾a­o E°ADµiQßJˆd„ðQ¬‚²„iÄLP¨ÇSiú—QÂÁrÙIÒÀÅDÿÕ¾Êø»r±ÌçÖÂB/ï¾&Ù⚀²U¾“9õ=(¦¤GMƒµ†)Tˆkp3·> `Ââ$Èÿ]Y4YÑÔÛdw w5‹kjðÐfw`‹Î¢‰pÚ½Ñìûw©€{ š"Ý\‰˜U0 tãªGŠÒcå±òXy¯J(¤„²`¡_ÌJ‚Ý'”ƒW2»‡)kÔz¹Ê'Ö'¿÷θnRÀ~Žãf8Ó |9BرòXyÄ·€o¾ l§]˜”íñ©¶{{7±ÞIp»“°[ 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install.packages("BiocManager") # BiocManager::install("BiocParallel") ## ----load------------------------------------------------------------------ library(BiocParallel) ## ----messages, eval = FALSE------------------------------------------------ # res <- bplapply(1:2, function(i) { message(i); Sys.sleep(3) }) ## ----messages-immediate, eval = FALSE-------------------------------------- # res <- bplapply(1:2, function(i) { # sink(NULL, type = "message") # message(i) # Sys.sleep(3) # }) ## ----errors_constructor---------------------------------------------------- param <- SnowParam() param ## ----errors_stopOnError---------------------------------------------------- param <- SnowParam(2, stop.on.error = TRUE) param bpstopOnError(param) <- FALSE ## ----errors_6tasksA_stopOnError-------------------------------------------- X <- list(1, "2", 3, 4, 5, 6) param <- SnowParam(3, tasks = length(X), stop.on.error = TRUE) ## ----errors_6tasksA_stopOnError_output------------------------------------- res <- tryCatch({ bplapply(X, sqrt, BPPARAM = param) }, error=identity) res attr(res, "result") ## ----errors_6tasks_nonstopOnError------------------------------------------ X <- list("1", 2, 3, 4, 5, 6) param <- SnowParam(3, tasks = length(X), stop.on.error = FALSE) res <- tryCatch({ bplapply(X, sqrt, BPPARAM = param) }, error=identity) res attr(res, "result") ## ----error_bptry----------------------------------------------------------- bptry({ bplapply(X, sqrt, BPPARAM=param) }) ## ----errors_3tasksA_stopOnError-------------------------------------------- X <- list(1, 2, "3", 4, 5, 6) param <- SnowParam(3, stop.on.error = TRUE) ## ----errors_3tasksA_stopOnError_output------------------------------------- bptry(bplapply(X, sqrt, BPPARAM = param)) ## ----errors_bpok_bplapply-------------------------------------------------- param <- SnowParam(2, stop.on.error=FALSE) result <- bptry(bplapply(list(1, "2", 3), sqrt, BPPARAM=param)) ## ----errors_bpok----------------------------------------------------------- bpok(result) ## ----errors_traceback------------------------------------------------------ tail(attr(result[[which(!bpok(result))]], "traceback")) ## ----redo_error------------------------------------------------------------ X <- list(1, "2", 3) param <- SnowParam(2, stop.on.error=FALSE) result <- bptry(bplapply(X, sqrt, BPPARAM=param)) result ## ----errors_BPREDO_input--------------------------------------------------- X.redo <- list(1, 2, 3) ## ----redo_run-------------------------------------------------------------- bplapply(X.redo, sqrt, BPREDO=result, BPPARAM=param) ## ----logs_constructor------------------------------------------------------ param <- SnowParam(stop.on.error=FALSE) param ## ----logs_accessors-------------------------------------------------------- bplog(param) <- TRUE bpthreshold(param) <- "TRACE" param ## ----logs_bplapply--------------------------------------------------------- tryCatch({ bplapply(list(1, "2", 3), sqrt, BPPARAM = param) }, error=function(e) invisible(e)) ## ----logs_FUN-------------------------------------------------------------- FUN <- function(i) { futile.logger::flog.debug(paste("value of 'i':", i)) if (!length(i)) { futile.logger::flog.warn("'i' has length 0") NA } else if (!is(i, "numeric")) { futile.logger::flog.debug("coercing 'i' to numeric") as.numeric(i) } else { i } } ## ----logs_FUN_WARN--------------------------------------------------------- param <- SnowParam(2, log = TRUE, threshold = "WARN", stop.on.error=FALSE) result <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) simplify2array(result) ## ----logs_FUN_DEBUG-------------------------------------------------------- param <- SnowParam(2, log = TRUE, threshold = "DEBUG", stop.on.error=FALSE) result <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) simplify2array(result) ## ----timeout_constructor--------------------------------------------------- param <- SnowParam(timeout = 20, stop.on.error=FALSE) param ## ----timeout_setter-------------------------------------------------------- param <- SnowParam(timeout = 2, stop.on.error=FALSE) fun <- function(i) { Sys.sleep(i) i } bptry(bplapply(1:3, fun, BPPARAM = param)) ## ----debug_sqrtabs--------------------------------------------------------- fun1 <- function(x) { v <- abs(x) sapply(1:length(v), function(i) sqrt(v[i])) } ## ----debug_fun1_debug------------------------------------------------------ fun2 <- function(x) { v <- abs(x) futile.logger::flog.debug( paste0("'x' = ", paste(x, collapse=","), ": length(v) = ", length(v)) ) sapply(1:length(v), function(i) { futile.logger::flog.info(paste0("'i' = ", i)) sqrt(v[i]) }) } ## ----debug_param_debug----------------------------------------------------- param <- SnowParam(3, log = TRUE, threshold = "DEBUG") ## ----debug_DEBUG----------------------------------------------------------- res <- bplapply(list(c(1,3), numeric(), 6), fun2, BPPARAM = param) res ## ----debug_sqrt------------------------------------------------------------ res <- bptry({ bplapply(list(1, "2", 3), sqrt, BPPARAM = SnowParam(3, stop.on.error=FALSE)) }) result ## ----debug_sqrt_wrap------------------------------------------------------- fun3 <- function(i) sqrt(i) ## ----sessionInfo, results="asis"------------------------------------------- toLatex(sessionInfo()) BiocParallel/inst/doc/Errors_Logs_And_Debugging.Rnw0000644000175200017520000005031514136050065023340 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{3. Errors, Logs and Debugging} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Errors, Logs and Debugging in \BiocParallel} \author{Valerie Obenchain and Martin Morgan} \date{Edited: December 16, 2015; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This vignette is part of the \BiocParallel{} package and focuses on error handling and logging. A section at the end demonstrates how the two can be used together as part of an effective debugging routine. \BiocParallel{} provides a unified interface to the parallel infrastructure in several packages including \CRANpkg{snow}, \CRANpkg{parallel}, \CRANpkg{batchtools} and \CRANpkg{foreach}. When implementing error handling in \BiocParallel{} the primary goals were to enable the return of partial results when an error is thrown (vs just the error) and to establish logging on the workers. In cases where error handling existed, such as \CRANpkg{batchtools} and \CRANpkg{foreach}, those behaviors were preserved. Clusters created with \CRANpkg{snow} and \CRANpkg{parallel} now have flexible error handling and logging available through \Rcode{SnowParam} and \Rcode{MulticoreParam} objects. In this document the term ``job'' is used to describe a single call to a bp*apply function (e.g., the \Rcode{X} in \Rcode{bplapply}). A ``job'' consists of one or more ``tasks'', where each ``task'' is run separately on a worker. The \Rpackage{BiocParallel} package is available at bioconductor.org and can be downloaded via \Rcode{BiocManager::install}: <>= if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("BiocParallel") @ Load the package: <>= library(BiocParallel) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Error Handling} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Messages and warnings} \BiocParallel{} captures messages and warnings in each job, returning the output to the manager and reporting these to the user after the completion of the entire operation. Thus <>= res <- bplapply(1:2, function(i) { message(i); Sys.sleep(3) }) @ %% reports messages only after the entire \Rcode{bplapply()} is complete. It may be desired to output messages immediatly. Do this using \Rcode{sink()}, as in the following example: <>= res <- bplapply(1:2, function(i) { sink(NULL, type = "message") message(i) Sys.sleep(3) }) @ %% This could be confusing when multiple workers write messages at the same time --the messages will be interleaved in an arbitrary way -- or when the workers are not all running on the same computer (e.g., with \Rcode{SnowParam()} or \Rcode{BatchjobsParaam()}) so should not be used in package code. \subsection{Catching errors} By default, \BiocParallel{} attempts all computations and returns any warnings and errors along with successful results. The \Rcode{stop.on.error} field controls if the job is terminated as soon as one task throws an error. This is useful when debugging or when running large jobs (many tasks) and you want to be notified of an error before all runs complete. \Rcode{stop.on.error} is \Rcode{TRUE} by default. <>= param <- SnowParam() param @ The field can be set when constructing the param or modified with the \Rcode{bpstopOnError} accessor. <>= param <- SnowParam(2, stop.on.error = TRUE) param bpstopOnError(param) <- FALSE @ In this example \Rcode{X} is length 6. By default, the elements of \Rcode{X} are divided as evenly as possible over the number of workers and run in chunks. The number of tasks is set equal to the length of \Rcode{X} which forces each element of \Rcode{X} to be executed separately (6 tasks). <>= X <- list(1, "2", 3, 4, 5, 6) param <- SnowParam(3, tasks = length(X), stop.on.error = TRUE) @ Tasks 1, 2, and 3 are assigned to the three workers, and are evaluated. Task 2 fails, stopping further computation. All successfully completed tasks are returned as the `result' attribute. Usually, this means that the results of tasks 1, 2, and 3 will be returned. <>= res <- tryCatch({ bplapply(X, sqrt, BPPARAM = param) }, error=identity) res attr(res, "result") @ Using \Rcode{stop.on.error=FALSE}, all tasks are evaluated. <>= X <- list("1", 2, 3, 4, 5, 6) param <- SnowParam(3, tasks = length(X), stop.on.error = FALSE) res <- tryCatch({ bplapply(X, sqrt, BPPARAM = param) }, error=identity) res attr(res, "result") @ \Rcode{bptry()} is a convenient way of trying to evaluate a \Rcode{bpapply}-like expression, returning the evaluated results without signalling an error. <>= bptry({ bplapply(X, sqrt, BPPARAM=param) }) @ In the next example the elements of \Rcode{X} are grouped instead of run separately. The default value for \Rcode{tasks} is 0 which means 'X' is split as evenly as possible across the number of workers. There are 3 workers so the first task consists of list(1, 2), the second is list("3", 4) and the third is list(5, 6). <>= X <- list(1, 2, "3", 4, 5, 6) param <- SnowParam(3, stop.on.error = TRUE) @ The output shows an error in when evaluating the third element, but also that the fourth element, in the same chunk as 3, was not evaluated. All elements are evaluated because they were assigned to workers before the first error occurred. <>= bptry(bplapply(X, sqrt, BPPARAM = param)) @ Side Note: Results are collected from workers as they finish which is not necessarily the same order in which they were loaded. Depending on how tasks are divided it is possible that the task with the error completes after all others so essentially all workers complete before the job is stopped. In this situation the output includes all results along with the error message and it may appear that \Rcode{stop.on.error=TRUE} did not stop the job soon enough. This is just a heads up that the usefulness of \Rcode{stop.on.error=TRUE} may vary with run time and distribution of tasks over workers. \subsection{Identify failures with \Rcode{bpok()}} The \Rcode{bpok()} function is a quick way to determine which (if any) tasks failed. In this example we use \Rcode{bptry()} to retrieve the partially evaluated expression, including the failed elements. <>= param <- SnowParam(2, stop.on.error=FALSE) result <- bptry(bplapply(list(1, "2", 3), sqrt, BPPARAM=param)) @ \Rcode{bpok} returns TRUE if the task was successful. <>= bpok(result) @ Once errors are identified with \Rcode{bpok} the traceback can be retrieved with the \Rcode{attr} function. This is possible because errors are returned as \Rcode{condition} objects with the traceback as an attribute. <>= tail(attr(result[[which(!bpok(result))]], "traceback")) @ \subsection{Rerun failed tasks with \Rcode{BPREDO}} Tasks can fail due to hardware problems or bugs in the input data. The \BiocParallel{} functions support a \Rcode{BPREDO} (re-do) argument for recomputing only the tasks that failed. A list of partial results and errors is supplied to \Rcode{BPREDO} in a second call to the function. The failed elements are identified, recomputed and inserted into the original results. The bug in this example is the second element of 'X' which is a character when it should be numeric. <>= X <- list(1, "2", 3) param <- SnowParam(2, stop.on.error=FALSE) result <- bptry(bplapply(X, sqrt, BPPARAM=param)) result @ First fix the input data. <>= X.redo <- list(1, 2, 3) @ Repeat the call to \Rcode{bplapply} this time supplying the partial results as \Rcode{BPREDO}. Only the failed calculations are computed, in the present case requiring only one worker. <>= bplapply(X.redo, sqrt, BPREDO=result, BPPARAM=param) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Logging} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% NOTE: Logging as described in this section is supported for SnowParam, MulticoreParam and SerialParam. \subsection{Parameters} Logging in \BiocParallel{} is controlled by 3 fields in the \Rcode{BiocParallelParam}: \begin{verbatim} log: TRUE or FALSE logdir: location to write log file threshold: one of "TRACE", "DEBUG", "INFO", "WARN", "ERROR", "FATAL" \end{verbatim} When \Rcode{log = TRUE} the \CRANpkg{futile.logger} package is loaded on each worker. \BiocParallel{} uses a custom script on the workers to collect log messages as well as additional statistics such as gc, runtime and node information. Output to stderr and stdout is also captured. By default \Rcode{log} is FALSE and \Rcode{threshold} is {\it INFO}. <>= param <- SnowParam(stop.on.error=FALSE) param @ Turn logging on and set the threshold to {\it TRACE}. <>= bplog(param) <- TRUE bpthreshold(param) <- "TRACE" param @ \subsection{Setting a threshold} All thresholds defined in \CRANpkg{futile.logger} are supported: {\it FATAL}, {\it ERROR}, {\it WARN}, {\it INFO}, {\it DEBUG} and {\it TRACE}. All messages greater than or equal to the severity of the threshold are shown. For example, a threshold of {\it INFO} will print all messages tagged as {\it FATAL}, {\it ERROR}, {\it WARN} and {\it INFO}. Because the default threshold is {\it INFO} it catches the {\it ERROR}-level message thrown when attempting the square root of a character ("2"). <>= tryCatch({ bplapply(list(1, "2", 3), sqrt, BPPARAM = param) }, error=function(e) invisible(e)) @ All user-supplied messages written in the \CRANpkg{futile.logger} syntax are also captured. This function performs argument checking and includes a couple of {\it WARN} and {\it DEBUG}-level messages. <>= FUN <- function(i) { futile.logger::flog.debug(paste("value of 'i':", i)) if (!length(i)) { futile.logger::flog.warn("'i' has length 0") NA } else if (!is(i, "numeric")) { futile.logger::flog.debug("coercing 'i' to numeric") as.numeric(i) } else { i } } @ Turn logging on and set the threshold to {\it WARN}. <>= param <- SnowParam(2, log = TRUE, threshold = "WARN", stop.on.error=FALSE) result <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) simplify2array(result) @ Changing the threshold to {\it DEBUG} catches both {\it WARN} and {\it DEBUG} messages. <>= param <- SnowParam(2, log = TRUE, threshold = "DEBUG", stop.on.error=FALSE) result <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) simplify2array(result) @ \subsection{Log files} When \Rcode{log == TRUE}, log messages are written to the console by default. If \Rcode{logdir} is given the output is written out to files, one per task. File names are prefixed with the name in \Rcode{bpjobname(BPPARAM)}; default is 'BPJOB'. \begin{verbatim} param <- SnowParam(2, log = TRUE, threshold = "DEBUG", logdir = tempdir()) res <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) ## loading futile.logger on workers list.files(bplogdir(param)) ## [1] "BPJOB.task1.log" "BPJOB.task2.log" \end{verbatim} Read in BPJOB.task2.log: \begin{verbatim} readLines(paste0(bplogdir(param), "/BPJOB.task2.log")) ## [1] "############### LOG OUTPUT ###############" ## [2] "Task: 2" ## [3] "Node: 2" ## [4] "Timestamp: 2015-07-08 09:03:59" ## [5] "Success: TRUE" ## [6] "Task duration: " ## [7] " user system elapsed " ## [8] " 0.009 0.000 0.011 " ## [9] "Memory use (gc): " ## [10] " used (Mb) gc trigger (Mb) max used (Mb)" ## [11] "Ncells 325664 17.4 592000 31.7 393522 21.1" ## [12] "Vcells 436181 3.4 1023718 7.9 530425 4.1" ## [13] "Log messages:" ## [14] "DEBUG [2015-07-08 09:03:59] value of 'i': 2" ## [15] "INFO [2015-07-08 09:03:59] coercing to numeric" ## [16] "DEBUG [2015-07-08 09:03:59] value of 'i': " ## [17] "WARN [2015-07-08 09:03:59] 'i' is missing" ## [18] "" ## [19] "stderr and stdout:" ## [20] "character(0)" \end{verbatim} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Worker timeout} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% NOTE: \Rcode{timeout} is supported for SnowParam and MulticoreParam. For long running jobs or untested code it can be useful to set a time limit. The \Rcode{timeout} field is the time, in seconds, allowed for each worker to complete a task; default is \Rcode{Inf}. If the task takes longer than \Rcode{timeout} a timeout error is returned. Time can be changed during param construction with the \Rcode{timeout} arg, <>= param <- SnowParam(timeout = 20, stop.on.error=FALSE) param @ or with the \Rcode{bptimeout} setter: <>= param <- SnowParam(timeout = 2, stop.on.error=FALSE) fun <- function(i) { Sys.sleep(i) i } bptry(bplapply(1:3, fun, BPPARAM = param)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Debugging} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Effective debugging strategies vary by problem and often involve a combination of error handling and logging techniques. In general, when debugging \R{}-generated errors the traceback is often the best place to start followed by adding debug messages to the worker function. When trouble shooting unexpected behavior (i.e., not a formal error or warning) adding debug messages or switching to \Rcode{SerialParam} are good approaches. Below is an overview of these different strategies. \subsection{Accessing the traceback} The traceback is a good place to start when tracking down \R{}-generated errors. Because the function is executed on the workers it's not accessible for interactive debugging with functions such as \Rcode{trace} or \Rcode{debug}. The traceback provides a snapshot of the state of the worker at the time the error was thrown. This function takes the square root of the absolute value of a vector. <>= fun1 <- function(x) { v <- abs(x) sapply(1:length(v), function(i) sqrt(v[i])) } @ Calling ``fun1'' with a character throws an error: \begin{verbatim} param <- SnowParam(stop.on.error=FALSE) result <- bptry({ bplapply(list(c(1,3), 5, "6"), fun1, BPPARAM = param) }, error=identity) result ## [[1]] ## [1] 1.000000 1.732051 ## ## [[2]] ## [1] 2.236068 ## ## [[3]] ## ## traceback() available as 'attr(x, "traceback")' \end{verbatim} Identify which elements failed with \Rcode{bpok}: \begin{verbatim} bpok(result) ## [1] TRUE TRUE FALSE \end{verbatim} The error (i.e., third element of ``res'') is a \Rcode{condition} object: \begin{verbatim} is(result[[3]], "condition") ## [1] TRUE \end{verbatim} The traceback is an attribute of the \Rcode{condition} and can be accessed with the \Rcode{attr} function. \begin{verbatim} noquote(tail(attr(result[[3]], "traceback"))) ## [1] call <- sapply(sys.calls(), deparse) ## [2] e <- structure(e, class = c("remote_error", "condition"), ## [3] traceback = capture.output(traceback(call))) ## [4] invokeRestart("abort", e) ## [5] }, "non-numeric argument to mathematical function", quote(abs(x))) ## [6] 1: h(simpleError(msg, call)) \end{verbatim} \subsection{Adding debug messages} When a \Rcode{numeric()} is passed to ``fun1'' no formal error is thrown but the length of the second list element is 2 when it should be 1. \begin{verbatim} bplapply(list(c(1,3), numeric(), 6), fun1, BPPARAM = param) ## [[1]] ## [1] 1.000000 1.732051 ## ## [[2]] ## [[2]][[1]] ## [1] NA ## ## [[2]][[2]] ## numeric(0) ## ## [[3]] ## [1] 2.44949 \end{verbatim} Without a formal error we have no traceback so we'll add a few debug messages. The \CRANpkg{futile.logger} syntax tags messages with different levels of severity. A message created with \Rcode{flog.debug} will only print if the threshold is {\it DEBUG} or lower. So in this case it will catch both INFO and DEBUG messages. ``fun2'' has debug statements that show the value of `x', length of `v' and the index `i'. <>= fun2 <- function(x) { v <- abs(x) futile.logger::flog.debug( paste0("'x' = ", paste(x, collapse=","), ": length(v) = ", length(v)) ) sapply(1:length(v), function(i) { futile.logger::flog.info(paste0("'i' = ", i)) sqrt(v[i]) }) } @ Create a param that logs at a threshold level of {\it DEBUG}. <>= param <- SnowParam(3, log = TRUE, threshold = "DEBUG") @ The debug messages reveal the problem occurs when `x' is \Rcode{numeric()}. The index for \Rcode{sapply} is along `v' which in this case has length 0. This forces `i' to take values of `1' and `0' giving an output of length 2 for the second element (i.e., \Rcode{NA} and \Rcode{numeric(0)}). <>= res <- bplapply(list(c(1,3), numeric(), 6), fun2, BPPARAM = param) res @ ``fun2'' can be fixed by using \Rcode{seq\_along(v)} to create the index instead of \Rcode{1:length(v)}. \subsection{Local debugging with \Rcode{SerialParam}} Errors that occur on parallel workers can be difficult to debug. Often the traceback sent back from the workers is too much to parse or not informative. We are also limited in that our interactive strategies of \Rcode{browser} and \Rcode{trace} are not available. One option for further debugging is to run the code in serial with \Rcode{SerialParam}. This removes the ``parallel'' component and is the same as running a straight \Rcode{*apply} function. This approach may not help if the problem was hardware related but can be very useful when the bug is in the \R{} code. We use the now familiar square root example with a bug in the second element of \Rcode{X}. <>= res <- bptry({ bplapply(list(1, "2", 3), sqrt, BPPARAM = SnowParam(3, stop.on.error=FALSE)) }) result @ \Rcode{sqrt} is an internal function. The problem is likely with our data going into the function and not the \Rcode{sqrt} function itself. We can write a small wrapper around \Rcode{sqrt} so we can see the input. <>= fun3 <- function(i) sqrt(i) @ Debug the new function: \begin{verbatim} debug(fun3) \end{verbatim} We want to recompute only elements that failed and for that we use the \Rcode{BPREDO} argument. The BPPARAM has been changed to \Rcode{SerialParam} so the job is run in the local workspace in serial. \begin{verbatim} > bplapply(list(1, "2", 3), fun3, BPREDO = result, BPPARAM = SerialParam()) Resuming previous calculation ... debugging in: FUN(...) debug: sqrt(i) Browse[2]> objects() [1] "i" Browse[2]> i [1] "2" Browse[2]> \end{verbatim} The local browsing allowed us to see the problem input was the character "2". %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= toLatex(sessionInfo()) @ \end{document} BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf0000644000175200017520000054706014154342034023353 0ustar00biocbuildbiocbuild%PDF-1.5 %ÐÔÅØ 113 0 obj << /Length 2407 /Filter /FlateDecode >> stream xÚí\_sÛ6÷§à#5¡øðú”¦i/fšK<ׇ¤K¬)ÒGRvûío´¨¸ij‘±:ŃMKb±‹Ý€´8ÚD8úþ÷W„Nh„‘ÀB+¸*‰•Šj ÿéí©_syñÕwœG#)©ˆ.¯"‘Ö4’:A‚Êèr½_Öõ‚⸪DÄͳŒJÿXm{Gâ´\û›oÍʾ·ß,Ž7y¹ñÅy¹øåò‡G6Ú'ÛÇ°@Dv û&¯²7 †ã´N‹Â'ðéùÕwŒE„#Æ%µü@†¨Ž–œ „QÏ÷¿ b¦ÎK›Å’Iÿ´‚+<•Ù6‘]™S “*~ÚfÖmOx]¹çMêtLED€Ž)Ž–Œ"I;v/×ykÖÿZ,¹N@É™Ùu¬$®}ýD>ó7ñµ¿}Qínòâãqlº¯’á#J¼î&1Jè3…(ÜéS0&I"øÄËô¢*[S¶Í˜í ìîµ×sƒ‹N<7r‚eDï—Dƒá¿*ÛºZï³6¯Ê íÞà õ%R:]%¦zGö ´HQˆˆ"B!%” ÓbÖ²óû%åˆtI'-ºaØ^R¥ãÃ`TØAy6ì"0nâ€]ˆŸ‹@ôi(ßµ³| -¢ø'4b\G\P¤ÙÂ?à)<"ºÛëyóöå·?ò„¢ùñsÀMO¼™$¨ã€3› àücµÙÌdVQ̃Çb ž1Ôé³™Çñi¡Çm6I`ûÍ‚Û£'ö´Kº3­™3Ö…çìb þÝ hØ[»_I²IbñïLÛ¡ø´[=nkÓl«b=cPžw¶* Q d8)ÈÔyÒ(´Î& ­ÃJÌÂŒiafœ›(‰TØ¢ Ä@<žˆ°‡¹éʼn°@³QœˆO'úÙƼ+¿¶¿0Ý1Å6ß™j߆SŠˆçHìŒÉ»ï!‰ ðhøíÒ¼Ñk­Q$€O âc?›E ÐHS1ŽP‹I"ÔϳÌ4Í!j·¦»ñAë̬ÒÌÂÞu8{ˆÁ¡ç<|-& ø>_¯o^?·v»þgÁ•ñoîÊâ<]¹Њ‰´YZ¹òfðîs8ùGT,¢ƒ Hò`»þšëà¬ô(–¦D]ú‰w¦ÓÃéI‚¨Í'ñ%–<ác‰ê|2‰Fš¨/*‘"K9–(-&“$aàpGÁrŸS=—HÀK2=éMZ§»³;ojŒŽü³FÃ3:6J EãàÛIöŸjh³Û®<%€=lÂÜ‚  ÑÓ€L‚Ǭ?4¡¸v‡Ûé@¦æë§ËÄ ^ú‘L”!®È\21û£¨c™îäœ. 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΀ÐåöR…èg}‰^·7?4 HAs‚¾Ä ÛÖŽ)8Ý >AsbØíÞeЦØó€"¯1Ô&Þo\#à *As‚æ-‰1·oµÞ-·¯¯eãnzdîùÿ/`ÒýS›lÊSÑ#YÁÓÚ_Ù´§Wße3žÞ—ɨYP‹Øô´ý˜ïc  Õ Ô‚:OßvÌÓÏöwµ « endstream endobj startxref 182943 %%EOF BiocParallel/inst/doc/Introduction_To_BiocParallel.R0000644000175200017520000001365714154342067023550 0ustar00biocbuildbiocbuild## ----style, eval=TRUE, echo=FALSE, results="asis"-------------------------- BiocStyle::latex() ## ----setup, echo=FALSE----------------------------------------------------- suppressPackageStartupMessages({ library(BiocParallel) library(VariantAnnotation) library(GenomicAlignments) library(RNAseqData.HNRNPC.bam.chr14) library(TxDb.Hsapiens.UCSC.hg19.knownGene) }) ## ----BiocManager, eval=FALSE----------------------------------------------- # # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("BiocParallel") ## ----BiocParallel---------------------------------------------------------- library(BiocParallel) ## ----quickstart_FUN-------------------------------------------------------- FUN <- function(x) { round(sqrt(x), 4) } ## ----quickstart_registry--------------------------------------------------- registered() ## ----configure_registry, eval=FALSE---------------------------------------- # options(MulticoreParam=quote(MulticoreParam(workers=4))) ## ----quickstart_bplapply_default, eval=FALSE------------------------------- # bplapply(1:4, FUN) ## ----quickstart_snow------------------------------------------------------- param <- SnowParam(workers = 2, type = "SOCK") bplapply(1:4, FUN, BPPARAM = param) ## ----BiocParallelParam_SerialParam----------------------------------------- serialParam <- SerialParam() serialParam ## ----BiocParallelParam_MulticoreParam-------------------------------------- multicoreParam <- MulticoreParam(workers = 8) multicoreParam ## ----register_registered--------------------------------------------------- registered() ## ----register_bpparam------------------------------------------------------ bpparam() ## ----register_BatchtoolsParam---------------------------------------------- default <- registered() register(BatchtoolsParam(workers = 10), default = TRUE) ## ----register_BatchtoolsParam2--------------------------------------------- names(registered()) bpparam() ## ----register_restore------------------------------------------------------ for (param in rev(default)) register(param) ## ----error-vignette, eval=FALSE-------------------------------------------- # browseVignettes("BiocParallel") ## ----use_cases_data-------------------------------------------------------- library(RNAseqData.HNRNPC.bam.chr14) fls <- RNAseqData.HNRNPC.bam.chr14_BAMFILES ## ----forking_gr, message=FALSE--------------------------------------------- library(GenomicAlignments) ## for GenomicRanges and readGAlignments() gr <- GRanges("chr14", IRanges((1000:3999)*5000, width=1000)) ## ----forking_param--------------------------------------------------------- param <- ScanBamParam(which=range(gr)) ## ----forking_FUN----------------------------------------------------------- FUN <- function(fl, param) { gal <- readGAlignments(fl, param = param) sum(countOverlaps(gr, gal)) } ## ----forking_default_multicore--------------------------------------------- MulticoreParam() ## ----db_problems, eval = FALSE--------------------------------------------- # library(org.Hs.eg.db) # FUN <- function(x, ...) { # ... # mapIds(org.Hs.eg.db, ...) # ... # } # bplapply(X, FUN, ..., BPPARAM = MulticoreParam()) ## ----db_solution_1, eval = FALSE------------------------------------------- # FUN <- function(x, ...) { # library(org.Hs.eg.db) # ... # mapIds(org.Hs.eg.db, ...) # ... # } # bplapply(X, FUN, ..., BPPARAM = MulticoreParam()) ## ----cluster_FUN----------------------------------------------------------- FUN <- function(fl, param, gr) { suppressPackageStartupMessages({ library(GenomicAlignments) }) gal <- readGAlignments(fl, param = param) sum(countOverlaps(gr, gal)) } ## ----cluster_snow_param---------------------------------------------------- snow <- SnowParam(workers = 2, type = "SOCK") ## ----cluster_bplapply------------------------------------------------------ bplapply(fls[1:3], FUN, BPPARAM = snow, param = param, gr = gr) ## ----db_solution_2, eval = FALSE------------------------------------------- # register(SnowParam()) # default evaluation # bpstart() # start the cluster # ... # bplapply(X, FUN1, ...) # ... # bplapply(X, FUN2, ...) # re-use workers # ... # bpstop() ## ----ad_hoc_sock_snow_param------------------------------------------------ hosts <- c("rhino01", "rhino01", "rhino02") param <- SnowParam(workers = hosts, type = "SOCK") ## ----cluster-MPI-work, eval=FALSE------------------------------------------ # library(BiocParallel) # library(Rmpi) # FUN <- function(i) system("hostname", intern=TRUE) ## ----cluster-MPI, eval=FALSE----------------------------------------------- # param <- SnowParam(mpi.universe.size() - 1, "MPI") # register(param) ## ----cluster-MPI-do, eval=FALSE-------------------------------------------- # xx <- bplapply(1:100, FUN) # table(unlist(xx)) # mpi.quit() ## ----cluster-MPI-bpstart, eval=FALSE--------------------------------------- # param <- bpstart(SnowParam(mpi.universe.size() - 1, "MPI")) # register(param) # xx <- bplapply(1:100, FUN) # bpstop(param) # mpi.quit() ## ----slurm----------------------------------------------------------------- tmpl <- system.file(package="batchtools", "templates", "slurm-simple.tmpl") noquote(readLines(tmpl)) ## ----cluster-batchtools, eval=FALSE---------------------------------------- # ## define work to be done # FUN <- function(i) system("hostname", intern=TRUE) # # library(BiocParallel) # # ## register SLURM cluster instructions from the template file # param <- BatchtoolsParam(workers=5, cluster="slurm", template=tmpl) # register(param) # # ## do work # xx <- bplapply(1:100, FUN) # table(unlist(xx)) ## ----devel-bplapply-------------------------------------------------------- system.time(x <- bplapply(1:3, function(i) { Sys.sleep(i); i })) unlist(x) ## ----sessionInfo, results="asis"------------------------------------------- toLatex(sessionInfo()) BiocParallel/inst/doc/Introduction_To_BiocParallel.Rnw0000644000175200017520000007356314136050065024111 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{1. Introduction to BiocParallel} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ <>= suppressPackageStartupMessages({ library(BiocParallel) library(VariantAnnotation) library(GenomicAlignments) library(RNAseqData.HNRNPC.bam.chr14) library(TxDb.Hsapiens.UCSC.hg19.knownGene) }) @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Introduction to \BiocParallel} \author{Valerie Obenchain, Vincent Carey, Michael Lawrence, Martin Morgan\footnote{\url{Martin.Morgan@RoswellPark.org}}} \date{Edited: January 23, 2021; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Numerous approaches are available for parallel computing in \R{}. The CRAN Task View for high performance and parallel computing provides useful high-level summaries and package categorization. \url{http://cran.r-project.org/web/views/HighPerformanceComputing.html} Most Task View packages cite or identify one or more of \CRANpkg{snow}, \CRANpkg{Rmpi}, \CRANpkg{multicore} or \CRANpkg{foreach} as relevant parallelization infrastructure. Direct support in \R{} for parallel computing started with release 2.14.0 with inclusion of the \CRANpkg{parallel} package which contains modified versions of \CRANpkg{multicore} and \CRANpkg{snow}. A basic objective of \BiocParallel{} is to reduce the complexity faced when developing and using software that performs parallel computations. With the introduction of the \Rcode{BiocParallelParam} object, \BiocParallel{} aims to provide a unified interface to existing parallel infrastructure where code can be easily executed in different environments. The \Rcode{BiocParallelParam} specifies the environment of choice as well as computing resources and is invoked by `registration` or passed as an argument to the \BiocParallel{} functions. \BiocParallel{} offers the following conveniences over the `roll your own` approach to parallel programming. \begin{itemize} \setlength{\itemsep}{5pt} \item{unified interface:}{ \Rcode{BiocParallelParam} instances define the method of parallel evaluation (multi-core, snow cluster, etc.) and computing resources (number of workers, error handling, cleanup, etc.). } \item{parallel iteration over lists, files and vectorized operations:}{ \Rcode{bplapply}, \Rcode{bpmapply} and \Rcode{bpvec} provide parallel list iteration and vectorized operations. \Rcode{bpiterate} iterates through files distributing chunks to parallel workers. } \item{cluster scheduling:}{ When the parallel environment is managed by a cluster scheduler through \CRANpkg{batchtools}, job management and result retrieval are considerably simplified. } \item{support of \Rcode{foreach}:}{ The \CRANpkg{foreach} and \CRANpkg{iterators} packages are fully supported. Registration of the parallel back end uses \Rcode{BiocParallelParam} instances. } \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Quick start} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The \Rpackage{BiocParallel} package is available at bioconductor.org and can be downloaded via \Rcode{BiocManager}: <>= if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("BiocParallel") @ Load \BiocParallel{}. <>= library(BiocParallel) @ The test function simply returns the square root of ``x''. <>= FUN <- function(x) { round(sqrt(x), 4) } @ Functions in \BiocParallel use the registered back-ends for parallel evaluation. The default is the top entry of the registry list. <>= registered() @ %% Configure your R session to always use a particular back-end configure by setting options named after the back ends in an \Rcode{.Rprofile} file, e.g., <>= options(MulticoreParam=quote(MulticoreParam(workers=4))) @ When a \BiocParallel{} function is invoked with no \Rcode{BPPARAM} argument the default back-end is used. <>= bplapply(1:4, FUN) @ Environment specific back-ends can be defined for any of the registry entries. This example uses a 2-worker SOCK cluster. <>= param <- SnowParam(workers = 2, type = "SOCK") bplapply(1:4, FUN, BPPARAM = param) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{The \BiocParallel{} Interface} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Classes} \subsubsection{\Rcode{BiocParallelParam}} \Rcode{BiocParallelParam} instances configure different parallel evaluation environments. Creating or \Rcode{register()}ing a `\Rcode{Param}` allows the same code to be used in different parallel environments without a code re-write. Params listed are supported on all of Unix, Mac and Windows except \Rcode{MulticoreParam} which is Unix and Mac only. \begin{itemize} \setlength{\itemsep}{5pt} \item{\Rcode{SerialParam}: } Supported on all platforms. Evaluate \BiocParallel-enabled code with parallel evaluation disabled. This approach is useful when writing new scripts and trying to debug code. \item{\Rcode{MulticoreParam}: } Supported on Unix and Mac. On Windows, \Rcode{MulticoreParam} dispatches to \Rcode{SerialParam}. Evaluate \BiocParallel-enabled code using multiple cores on a single computer. When available, this is the most efficient and least troublesome way to parallelize code. Windows does not support multi-core evaluation (the \Rcode{MulticoreParam} object can be used, but evaluation is serial). On other operating systems, the default number of workers equals the value of the global option \Rcode{mc.cores} (e.g., \Rcode{getOption("mc.cores")}) or, if that is not set, the number of cores returned by \Rcode{parallel::detectCores() - 2}; when number of cores cannot be determined, the default is 1. \Rcode{MulticoreParam} uses 'forked' processes with 'copy-on-change' semantics -- memory is only copied when it is changed. This makes it very efficient to invoke compared to other back-ends. There are several important caveats to using \Rcode{MulticoreParam}. Forked processes are not available on Windows. Some environments, e.g., \emph{RStudio}, do not work well with forked processes, assuming that \R{} code evaluation is single-threaded. Some external resources, e.g., access to files or data bases, maintain state in a way that assumes the resource is accessed only by a single thread. A subtle cost is that \R's garbage collector runs periodically, and 'marks' memory as in use. This effectively triggers a copy of the marked memory. \R's generational garbage collector is triggered at difficult-to-predict times; the effect in a long-running forked process is that the memory is eventually copied. See \href{https://support.bioconductor.org/p/70196/#70509}{this post} for additional details. \Rcode{MulticoreParam} is based on facilities originally implemented in the \CRANpkg{multicore} package and subsequently the \CRANpkg{parallel} package in base \R{}. \item{\Rcode{SnowParam}: } Supported on all platforms. Evaluate \BiocParallel-enabled code across several distinct \R{} instances, on one or several computers. This is a straightforward approach for executing parallel code on one or several computers, and is based on facilities originally implemented in the \CRANpkg{snow} package. Different types of \CRANpkg{snow} `back-ends' are supported, including socket and MPI clusters. \item{\Rcode{BatchtoolsParam}: } Applicable to clusters with formal schedulers. Evaluate \BiocParallel-enabled code by submitting to a cluster scheduler like SGE. \item{\Rcode{DoparParam}: } Supported on all platforms. Register a parallel back-end supported by the \CRANpkg{foreach} package for use with \BiocParallel. \end{itemize} The simplest illustration of creating \Rcode{BiocParallelParam} is <>= serialParam <- SerialParam() serialParam @ Most parameters have additional arguments influencing behavior, e.g., specifying the number of `cores' to use when creating a \Rcode{MulticoreParam} instance <>= multicoreParam <- MulticoreParam(workers = 8) multicoreParam @ Arguments are described on the corresponding help page, e.g., \Rcode{?MulticoreParam}. \subsubsection{\Rcode{register()}ing \Rcode{BiocParallelParam} instances} The list of registered \Rcode{BiocParallelParam} instances represents the user's preferences for different types of back-ends. Individual algorithms may specify a preferred back-end, and different back-ends maybe chosen when parallel evaluation is nested. The registry behaves like a `stack' in that the last entry registered is added to the top of the list and becomes the ``next used`` (i.e., the default). \Rcode{registered} invoked with no arguments lists all back-ends. <>= registered() @ \Rcode{bpparam} returns the default from the top of the list. <>= bpparam() @ Add a specialized instance with \Rcode{register}. When \Rcode{default} is TRUE, the new instance becomes the default. <>= default <- registered() register(BatchtoolsParam(workers = 10), default = TRUE) @ BatchtoolsParam has been moved to the top of the list and is now the default. <>= names(registered()) bpparam() @ Restore the original registry <>= for (param in rev(default)) register(param) @ \subsection{Functions} \subsubsection{Parallel looping, vectorized and aggregate operations} These are used in common functions, implemented as much as possible for all back-ends. The functions (see the help pages, e.g., \Rcode{?bplapply} for a full definition) include \begin{description} \item{\Rcode{bplapply(X, FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to each element of \Rcode{X}. \Rcode{bplapply} invokes \Rcode{FUN} \Rcode{length(X)} times, each time with a single element of \Rcode{X}. \item{\Rcode{bpmapply(FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to the first, second, etc., elements of each argument in \ldots. \item{\Rcode{bpiterate(ITER, FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to the output of function \Rcode{ITER}. Data chunks are returned by \Rcode{ITER} and distributed to parallel workers along with \Rcode{FUN}. Intended for iteration though an undefined number of data chunks (i.e., records in a file). \item{\Rcode{bpvec(X, FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to subsets of \Rcode{X}. \Rcode{bpvec} invokes function \Rcode{FUN} as many times as there are cores or cluster nodes, with \Rcode{FUN} receiving a subset (typically more than 1 element, in contrast to \Rcode{bplapply}) of \Rcode{X}. \item{\Rcode{bpaggregate(x, data, FUN, ...)}: } Use the formula in \Rcode{x} to aggregate \Rcode{data} using \Rcode{FUN}. \end{description} \subsubsection{Parallel evaluation environment} These functions query and control the state of the parallel evaluation environment. \begin{description} \item{\Rcode{bpisup(x)}: } Query a \Rcode{BiocParallelParam} back-end \Rcode{x} for its status. \item{\Rcode{bpworkers}; \Rcode{bpnworkers}: } Query a \Rcode{BiocParallelParam} back-end for the number of workers available for parallel evaluation. \item{\Rcode{bptasks}: } Divides a job (e.g., single call to *lapply function) into tasks. Applicable to \Rcode{MulticoreParam} only; \Rcode{DoparParam} and \Rcode{BatchtoolsParam} have their own approach to dividing a job among workers. \item{\Rcode{bpstart(x)}: } Start a parallel back end specified by \Rcode{BiocParallelParam} \Rcode{x}, if possible. \item{\Rcode{bpstop(x)}: } Stop a parallel back end specified by \Rcode{BiocParallelParam} \Rcode{x}. \end{description} \subsubsection{Error handling and logging} Logging and advanced error recovery is available in \Rcode{BiocParallel} 1.1.25 and later. For a more details see the vignette titled "Error Handling and Logging": <>= browseVignettes("BiocParallel") @ \subsubsection{Locks and counters} Inter-process (i.e., single machine) locks and counters are supported using \Rcode{ipclock()}, \Rcode{ipcyield()}, and friends. Use these to synchronize computation, e.g., allowing only a single process to write to a file at a time. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Use cases} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Sample data are BAM files from a transcription profiling experiment available in the \Rpackage{RNAseqData.HNRNPC.bam.chr14} package. <>= library(RNAseqData.HNRNPC.bam.chr14) fls <- RNAseqData.HNRNPC.bam.chr14_BAMFILES @ \subsection{Single machine} Common approaches on a single machine are to use multiple cores in forked processes, or to use clusters of independent processes. For purely \R-based computations on non-Windows computers, there are substantial benefits, such as shared memory, to be had using forked processes. However, this approach is not portable across platforms, and fails when code uses functionality, e.g., file or data base access, that assumes only a single thread is accessing the resource. While use of forked processes with \Rcode{MulticoreParam} is an attractive solution for scripts using pure \R{} functionality, robust and complex code often requires use of independent processes and \Rcode{SnowParam}. \subsubsection{Forked processes with \Rcode{MulticoreParam}} This example counts overlaps between BAM files and a defined set of ranges. First create a GRanges with regions of interest (in practice this could be large). <>= library(GenomicAlignments) ## for GenomicRanges and readGAlignments() gr <- GRanges("chr14", IRanges((1000:3999)*5000, width=1000)) @ A \Rclass{ScanBamParam} defines regions to extract from the files. <>= param <- ScanBamParam(which=range(gr)) @ FUN counts overlaps between the ranges in `gr` and the files. <>= FUN <- function(fl, param) { gal <- readGAlignments(fl, param = param) sum(countOverlaps(gr, gal)) } @ All parameters necessary for running a job in a multi-core environment are specified in the \Rclass{MulticoreParam} instance. <>= MulticoreParam() @ The \BiocParallel{} functions, such as \Rfunction{bplapply}, use information in the \Rclass{MulticoreParam} to set up the appropriate back-end and pass relevant arguments to low-level functions. \begin{verbatim} > bplapply(fls[1:3], FUN, BPPARAM = MulticoreParam(), param = param) $ERR127306 [1] 1185 $ERR127307 [1] 1123 $ERR127308 [1] 1241 \end{verbatim} Shared memory environments eliminate the need to pass large data between workers or load common packages. Note that in this code the GRanges data was not passed to all workers in \Rcode{bplapply} and FUN did not need to load \Biocpkg{GenomicAlignments} for access to the \Rcode{readGAlignments} function. Problems with forked processes occur when code implementating functionality used by the workers is not written in anticipation of use by forked processes. One example is the database connection underlying Bioconductor's \Rcode{org.*} packages. This psudoe-code <>= library(org.Hs.eg.db) FUN <- function(x, ...) { ... mapIds(org.Hs.eg.db, ...) ... } bplapply(X, FUN, ..., BPPARAM = MulticoreParam()) @ %% is likely to fail, because \Rcode{library(org.Hs.eg.db)} opens a database connection that is accessed by multiple processes. A solution is to ensure that the database is opened independently in each process <>= FUN <- function(x, ...) { library(org.Hs.eg.db) ... mapIds(org.Hs.eg.db, ...) ... } bplapply(X, FUN, ..., BPPARAM = MulticoreParam()) @ \subsubsection{Clusters of independent processes with \Rcode{SnowParam}} Both Windows and non-Windows machines can use the cluster approach to spawn processes. \BiocParallel{} back-end choices for clusters on a single machine are \Rclass{SnowParam} for configuring a Snow cluster or the \Rclass{DoparParam} for use with the \Rpackage{foreach} package. To re-run the counting example, FUN needs to modified such that `gr` is passed as a formal argument and required libraries are loaded on each worker. (In general, this is not necessary for functions defined in a package name space, see Section~\ref{sec:developers}.) <>= FUN <- function(fl, param, gr) { suppressPackageStartupMessages({ library(GenomicAlignments) }) gal <- readGAlignments(fl, param = param) sum(countOverlaps(gr, gal)) } @ Define a 2-worker SOCK Snow cluster. <>= snow <- SnowParam(workers = 2, type = "SOCK") @ A call to \Rcode{bplapply} with the \Rclass{SnowParam} creates the cluster and distributes the work. <>= bplapply(fls[1:3], FUN, BPPARAM = snow, param = param, gr = gr) @ The FUN written for the cluster adds some overhead due to the passing of the GRanges and the loading of \Biocpkg{GenomicAlignments} on each worker. This approach, however, has the advantage that it works on most platforms and does not require a coding change when switching between windows and non-windows machines. If several \Rcode{bplapply()} statements are likely to require the same resource, it often makes sense to create a cluster once using \Rcode{bpstart()}. The workers are re-used by each call to \Rcode{bplapply()}, so they do not have to re-load packages, etc. <>= register(SnowParam()) # default evaluation bpstart() # start the cluster ... bplapply(X, FUN1, ...) ... bplapply(X, FUN2, ...) # re-use workers ... bpstop() @ \subsection{\emph{Ad hoc} cluster of multiple machines} We use the term \emph{ad hoc} cluster to define a group of machines that can communicate with each other and to which the user has password-less log-in access. This example uses a group of compute machines ("the rhinos") on the FHCRC network. \subsubsection{Sockets} On Linux and Mac OS X, a socket cluster is created across machines by supplying machine names as the \Rcode{workers} argument to a \Rclass{BiocParallelParam} instance instead of a number. Each name represents an \R{} process; repeat names indicate multiple workers on the same machine. Create a \Rclass{SnowParam} with 2 cpus from `rhino01` and 1 from `rhino02`. <>= hosts <- c("rhino01", "rhino01", "rhino02") param <- SnowParam(workers = hosts, type = "SOCK") @ Execute FUN 4 times across the workers. \begin{verbatim} > FUN <- function(i) system("hostname", intern=TRUE) > bplapply(1:4, FUN, BPPARAM = param) [[1]] [1] "rhino01" [[2]] [1] "rhino01" [[3]] [1] "rhino02" [[4]] [1] "rhino01" \end{verbatim} When creating a cluster across Windows machines \Rcode{workers} must be IP addresses (e.g., "140.107.218.57") instead of machine names. \subsubsection{MPI} An MPI cluster across machines is created with \emph{mpirun} or \emph{mpiexec} from the command line or a script. A list of machine names provided as the -hostfile argument defines the mpi universe. The hostfile requests 2 processors on 3 different machines. \begin{verbatim} rhino01 slots=2 rhino02 slots=2 rhino03 slots=2 \end{verbatim} From the command line, start a single interactive \R{} process on the current machine. \begin{verbatim} mpiexec --np 1 --hostfile hostfile R --vanilla \end{verbatim} Load \BiocParallel{} and create an MPI Snow cluster. The number of \Rcode{workers} in \Rclass{SnowParam} should match the number of slots requested in the hostfile. Using a smaller number of workers uses a subset of the slots. \begin{verbatim} > library(BiocParallel) > param <- SnowParam(workers = 6, type = "MPI") \end{verbatim} Execute FUN 6 times across the workers. \begin{verbatim} > FUN <- function(i) system("hostname", intern=TRUE) > bplapply(1:6, FUN, BPPARAM = param) bplapply(1:6, FUN, BPPARAM = param) 6 slaves are spawned successfully. 0 failed. [[1]] [1] "rhino01" [[2]] [1] "rhino02" [[3]] [1] "rhino02" [[4]] [1] "rhino03" [[5]] [1] "rhino03" [[6]] [1] "rhino01" \end{verbatim} Batch jobs can be launched with mpiexec and R CMD BATCH. Code to be executed is in `Rcode.R`. \begin{verbatim} mpiexec --hostfile hostfile R CMD BATCH Rcode.R \end{verbatim} \subsection{Clusters with schedulers} Computer clusters are far from standardized, so the following may require significant adaptation; it is written from experience here at FHCRC, where we have a large cluster managed via SLURM. Nodes on the cluster have shared disks and common system images, minimizing complexity about making data resources available to individual nodes. There are two simple models for use of the cluster, Cluster-centric and R-centric. \subsubsection{Cluster-centric} The idea is to use cluster management software to allocate resources, and then arrange for an \R{} script to be evaluated in the context of allocated resources. NOTE: Depending on your cluster configuration it may be necessary to add a line to the template file instructing workers to use the version of R on the master / head node. Otherwise the default R on the worker nodes will be used. For SLURM, we might request space for 4 tasks (with \verb+salloc+ or \verb+sbatch+), arrange to start the MPI environment (with \verb+orterun+) and on a single node in that universe run an \R{} script \verb+BiocParallel-MPI.R+. The command is \begin{verbatim} $ salloc -N 4 orterun -n 1 R -f BiocParallel-MPI.R \end{verbatim} The \R{} script might do the following, using MPI for parallel evaluation. Start by loading necessary packages and defining \Rcode{FUN} work to be done <>= library(BiocParallel) library(Rmpi) FUN <- function(i) system("hostname", intern=TRUE) @ %% Create a \Rclass{SnowParam} instance with the number of nodes equal to the size of the MPI universe minus 1 (let one node dispatch jobs to workers), and register this instance as the default <>= param <- SnowParam(mpi.universe.size() - 1, "MPI") register(param) @ %% Evaluate the work in parallel, process the results, clean up, and quit <>= xx <- bplapply(1:100, FUN) table(unlist(xx)) mpi.quit() @ %% The entire session is as follows: \begin{knitrout} \definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe} \begin{verbatim} $ salloc -N 4 orterun -n 1 R --vanilla -f BiocParallel-MPI.R salloc: Job is in held state, pending scheduler release salloc: Pending job allocation 6762292 salloc: job 6762292 queued and waiting for resources salloc: job 6762292 has been allocated resources salloc: Granted job allocation 6762292 ## ... > FUN <- function(i) system("hostname", intern=TRUE) > > library(BiocParallel) > library(Rmpi) > param <- SnowParam(mpi.universe.size() - 1, "MPI") > register(param) > xx <- bplapply(1:100, FUN) 4 slaves are spawned successfully. 0 failed. > table(unlist(xx)) gizmof13 gizmof71 gizmof86 gizmof88 25 25 25 25 > > mpi.quit() salloc: Relinquishing job allocation 6762292 salloc: Job allocation 6762292 has been revoked. \end{verbatim} \end{kframe} \end{knitrout} One advantage of this approach is that the responsibility for managing the cluster lies firmly with the cluster management software -- if one wants more nodes, or needs special resources, then adjust parameters to \verb+salloc+ (or \verb+sbatch+). Notice that workers are spawned within the \Rcode{bplapply} function; it might often make sense to more explicitly manage workers with \Rfunction{bpstart} and \Rfunction{bpstop}, e.g., <>= param <- bpstart(SnowParam(mpi.universe.size() - 1, "MPI")) register(param) xx <- bplapply(1:100, FUN) bpstop(param) mpi.quit() @ \subsubsection{R-centric} A more \R-centric approach might start an \R{} script on the head node, and use \Rpackage{batchtools} to submit jobs from within the \R{} session. One way of doing this is to create a file containing a template for the job submission step, e.g., for SLURM; a starting point might be found at <>= tmpl <- system.file(package="batchtools", "templates", "slurm-simple.tmpl") noquote(readLines(tmpl)) @ The \R{} script, run interactively or from the command line, might then look like <>= ## define work to be done FUN <- function(i) system("hostname", intern=TRUE) library(BiocParallel) ## register SLURM cluster instructions from the template file param <- BatchtoolsParam(workers=5, cluster="slurm", template=tmpl) register(param) ## do work xx <- bplapply(1:100, FUN) table(unlist(xx)) @ %% The code runs on the head node until \Rcode{bplapply}, where the \R{} script interacts with the SLURM scheduler to request a SLURM allocation, run jobs, and retrieve results. The argument \Rcode{4} to \Rcode{BatchtoolsParam} specifies the number of workers to request from the scheduler; \Rcode{bplapply} divides the 100 jobs among the 4 workers. If \Rcode{BatchtoolsParam} had been created without specifying any workers, then 100 jobs implied by the argument to \Rcode{bplapply} would be associated with 100 tasks submitted to the scheduler. Because cluster tasks are running in independent \R{} instances, and often on physically separate machines, a convenient `best practice' is to write \Rcode{FUN} in a `functional programming' manner, such that all data required for the function is passed in as arguments or (for large data) loaded implicitly or explicitly (e.g., via an \R{} library) from disk. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Analyzing genomic data in \Bioconductor{}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% General strategies exist for handling large genomic data that are well suited to \R{} programs. A manuscript titled \emph{Scalable Genomics with \R{} and \Bioconductor{}} (\url{http://arxiv.org/abs/1409.2864}) by Michael Lawrence and Martin Morgan, reviews several of these approaches and demonstrate implementation with \Bioconductor{} packages. Problem areas include scalable processing, summarization and visualization. The techniques presented include restricting queries, compressing data, iterating, and parallel computing. Ideas are presented in an approachable fashion within a framework of common use cases. This is a benificial read for anyone anyone tackling genomics problems in \R{}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{For developers} \label{sec:developers} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Developers wishing to use \BiocParallel{} in their own packages should include \BiocParallel{} in the \texttt{DESCRIPTION} file \begin{verbatim} Imports: BiocParallel \end{verbatim} and import the functions they wish to use in the \texttt{NAMESPACE} file, e.g., \begin{verbatim} importFrom(BiocParallel, bplapply) \end{verbatim} Then invoke the desired function in the code, e.g., <>= system.time(x <- bplapply(1:3, function(i) { Sys.sleep(i); i })) unlist(x) @ %% This will use the back-end returned by \Rcode{bpparam()}, by default a \Rcode{MulticoreParam()} instance or the user's preferred back-end if they have used \Rcode{register()}. The \Rcode{MulticoreParam} back-end does not require any special configuration or set-up and is therefore the safest option for developers. Unfortunately, \Rcode{MulticoreParam} provides only serial evaluation on Windows. Developers should document that their function uses \BiocParallel{} functions on the man page, and should perhaps include in their function signature an argument \Rcode{BPPARAM=bpparam()}. Developers should NOT use `register()` in package code -- this sets a preference that influences use of `bplapply()` and friends in all packages, not just their package. Developers wishing to invoke back-ends other than \Rcode{MulticoreParam}, or to write code that works across Windows, macOS and Linux, need to take special care to ensure that required packages, data, and functions are available and loaded on the remote nodes. In \Rcode{bplapply()}, the environment of \Rcode{FUN} (other than the global environment) is serialized to the workers. 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MulticoreParam(RNGseed = 100)) stopifnot( identical(result1, result5) ) } ## ----different-workers-tasks----------------------------------------------- result6 <- bplapply(1:3, runif, BPPARAM = SnowParam(workers = 2, RNGseed = 100)) result7 <- bplapply(1:3, runif, BPPARAM = SnowParam(workers = 3, RNGseed = 100)) result8 <- bplapply( 1:3, runif, BPPARAM = SnowParam(workers = 2, tasks = 3, RNGseed = 100) ) stopifnot( identical(result1, result6), identical(result1, result7), identical(result1, result8) ) ## ----bpiterate-ITER_FUN_FACTORY-------------------------------------------- ITER_FUN_FACTORY <- function() { x <- 1:3 i <- 0L function() { i <<- i + 1L if (i > length(x)) return(NULL) x[[i]] } } ## ----bpiterate-ITER-------------------------------------------------------- ITER <- ITER_FUN_FACTORY() ITER() ITER() ITER() ITER() ## ----bpiterate------------------------------------------------------------- result9 <- bpiterate( ITER_FUN_FACTORY(), runif, BPPARAM = SerialParam(RNGseed = 100) ) stopifnot( identical(result1, result9) ) ## ----bptry----------------------------------------------------------------- FUN1 <- function(i) { if (identical(i, 2L)) { ## error when evaluating the second element stop("i == 2") } else runif(i) } result10 <- bptry(bplapply( 1:3, FUN1, BPPARAM = SerialParam(RNGseed = 100, stop.on.error = FALSE) )) result10 ## ----bptry-REDO------------------------------------------------------------ FUN2 <- function(i) { if (identical(i, 2L)) { ## the random number stream should be in the same state as the ## first time through the loop, and rnorm(i) should return ## same result as FUN runif(i) } else { ## if this branch is used, then we are incorrectly updating ## already calculated elements -- '0' in the output would ## indicate this error 0 } } result11 <- bplapply( 1:3, FUN2, BPREDO = result10, BPPARAM = SerialParam(RNGseed = 100, stop.on.error = FALSE) ) stopifnot( identical(result1, result11) ) ## ----RNGseed-and-set-seed-------------------------------------------------- set.seed(200) value <- runif(1) set.seed(200) result12 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) stopifnot( identical(result1, result12), identical(value, runif(1)) ) ## ----not-RNGseed-and-set-seed---------------------------------------------- set.seed(100) value <- runif(1) set.seed(100) result13 <- bplapply(1:3, runif, BPPARAM = SerialParam()) stopifnot( !identical(result1, result13), identical(value, runif(1)) ) ## ----bpstart-basic--------------------------------------------------------- param <- bpstart(SerialParam(RNGseed = 100)) result16 <- bplapply(1:3, runif, BPPARAM = param) bpstop(param) stopifnot( identical(result1, result16) ) ## ----bpstart-continuation-------------------------------------------------- param <- bpstart(SerialParam(RNGseed = 100)) result16 <- bplapply(1:3, runif, BPPARAM = param) result17 <- bplapply(1:3, runif, BPPARAM = param) bpstop(param) stopifnot( identical(result1, result16), !identical(result1, result17) ) ## ----lapply---------------------------------------------------------------- set.seed(100) result20 <- lapply(1:3, runif) stopifnot( !identical(result1, result20) ) ## ----sessionInfo, echo=FALSE----------------------------------------------- sessionInfo() BiocParallel/inst/doc/Random_Numbers.Rnw0000644000175200017520000002450214136050065021255 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{4. Random Numbers in BiocParallel} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ <>= suppressPackageStartupMessages({ library(BiocParallel) }) @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Random Numbers in BiocParallel} \author{Martin Morgan\footnote{\url{Martin.Morgan@RoswellPark.org}}} \date{Edited: 7 September, 2021; Compiled: \today} \begin{document} \maketitle \tableofcontents \section{Scope} \BiocParallel{} enables use of random number streams in a reproducible manner. This document applies to the following \Rcode{*Param()}: \begin{itemize} \item \Rcode{SerialParam()}: sequential evaluation in a single \emph{R} process. \item \Rcode{SnowParam()}: parallel evaluation in multiple independent \emph{R} processes. \item \Rcode{MulticoreParam())}: parallel evaluation in \emph{R} sessions running in forked threads. Not available on Windows. \end{itemize} The \Rcode{*Param()} can be used for evaluation with: \begin{itemize} \item \Rcode{bplapply()}: \Rcode{lapply()}-like application of a user-supplied function \Rcode{FUN} to a vector or list of elements \Rcode{X}. \item \Rcode{bpiterate()}: apply a user-supplied function \Rcode{FUN} to an unknown number of elements resulting from successive calls to a user-supplied function \Rcode{ITER}. \end{itemize} The reproducible random number implementation also supports: \begin{itemize} \item \Rcode{bptry()} and the \Rcode{BPREDO=} argument, for re-evaluation of elements that fail (e.g., because of a bug in \Rcode{FUN}). \end{itemize} \section{Essentials} \subsection{Use of \Rcode{bplapply()} and \Rcode{RNGseed=}} Attach \BiocParallel{} and ensure that the version is greater than 1.27.5 <>= library(BiocParallel) stopifnot( packageVersion("BiocParallel") > "1.27.5" ) @ For reproducible calculation, use the \Rcode{RNGseed=} argument in any of the \Rcode{*Param()} constructors. <>= result1 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) result1 @ Repeating the calculation with the same value for \Rcode{RNGseed=} results in the same result; a different random number seed results in different results. <>= result2 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) stopifnot( identical(result1, result2) ) result3 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 200)) result3 stopifnot( !identical(result1, result3) ) @ Results are invariant across \Rcode{*Param()} <>= result4 <- bplapply(1:3, runif, BPPARAM = SnowParam(RNGseed = 100)) stopifnot( identical(result1, result4) ) if (!identical(.Platform$OS.type, "windows")) { result5 <- bplapply(1:3, runif, BPPARAM = MulticoreParam(RNGseed = 100)) stopifnot( identical(result1, result5) ) } @ Parallel backends can adjust the number of \Rcode{workers} (processes performing the evaluation) and \Rcode{tasks} (how elements of \Rcode{X} are distributed between workers). Results are invariant to these parameters. This is illustrated with \Rcode{SnowParam()}, but applies also to \Rcode{MulticoreParam()}. <>= result6 <- bplapply(1:3, runif, BPPARAM = SnowParam(workers = 2, RNGseed = 100)) result7 <- bplapply(1:3, runif, BPPARAM = SnowParam(workers = 3, RNGseed = 100)) result8 <- bplapply( 1:3, runif, BPPARAM = SnowParam(workers = 2, tasks = 3, RNGseed = 100) ) stopifnot( identical(result1, result6), identical(result1, result7), identical(result1, result8) ) @ Subsequent sections illustrate results with \Rcode{SerialParam()}, but identical results are obtained with \Rcode{SnowParam()} and \Rcode{MulticoreParam()}. \subsection{Use with \Rcode{bpiterate()}} \Rcode{bpiterate()} allows parallel processing of a 'stream' of data as a series of tasks, with a task consisting of a portion of the overall data. It is useful when the data size is not known or easily partitioned into elements of a vector or list. A real use case might involve iterating through a BAM file, where a task represents successive records (perhaps 100,000 per task) in the file. Here we illustrate with a simple example -- iterating through a vector \Rcode{x = 1:3} <>= ITER_FUN_FACTORY <- function() { x <- 1:3 i <- 0L function() { i <<- i + 1L if (i > length(x)) return(NULL) x[[i]] } } @ \Rcode{ITER\_FUN\_FACTORY()} is used to create a function that, on each invocation, returns the next task (here, an element of \Rcode{x}; in a real example, perhaps 100000 records from a BAM file). When there are no more tasks, the function returns \Rcode{NULL} <>= ITER <- ITER_FUN_FACTORY() ITER() ITER() ITER() ITER() @ In our simple example, \Rcode{bpiterate()} is performing the same computations as \Rcode{bplapply()} so the results, including the random number streams used by each task in \Rcode{bpiterate()}, are the same <>= result9 <- bpiterate( ITER_FUN_FACTORY(), runif, BPPARAM = SerialParam(RNGseed = 100) ) stopifnot( identical(result1, result9) ) @ \subsection{Use with \Rcode{bptry()}} \Rcode{bptry()} in conjunction with the \Rcode{BPREDO=} argument to \Rcode{bplapply()} or \Rcode{bpiterate()} allows for graceful recovery from errors. Here a buggy \Rcode{FUN1()} produces an error for the second element. \Rcode{bptry()} allows evaluation to continue for other elements of \Rcode{X}, despite the error. This is shown in the result. <>= FUN1 <- function(i) { if (identical(i, 2L)) { ## error when evaluating the second element stop("i == 2") } else runif(i) } result10 <- bptry(bplapply( 1:3, FUN1, BPPARAM = SerialParam(RNGseed = 100, stop.on.error = FALSE) )) result10 @ \Rcode{FUN2()} illustrates the flexibility of \Rcode{bptry()} by fixing the bug when \Rcode{i == 2}, but also generating incorrect results if invoked for previously correct values. The identity of the result to the original computation shows that only the error task is re-computed, and that the random number stream used by the task is identical to the original stream. <>= FUN2 <- function(i) { if (identical(i, 2L)) { ## the random number stream should be in the same state as the ## first time through the loop, and rnorm(i) should return ## same result as FUN runif(i) } else { ## if this branch is used, then we are incorrectly updating ## already calculated elements -- '0' in the output would ## indicate this error 0 } } result11 <- bplapply( 1:3, FUN2, BPREDO = result10, BPPARAM = SerialParam(RNGseed = 100, stop.on.error = FALSE) ) stopifnot( identical(result1, result11) ) @ \subsection{Relationship between \Rcode{RNGseed=} and \Rcode{set.seed()}} The global random number stream (influenced by \Rcode{set.seed()}) is ignored by \BiocParallel{}, and \BiocParallel{} does NOT increment the global stream. <>= set.seed(200) value <- runif(1) set.seed(200) result12 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) stopifnot( identical(result1, result12), identical(value, runif(1)) ) @ When \Rcode{RNGseed=} is not used, an internal stream (not accessible to the user) is used and \BiocParallel{} does NOT increment the global stream. <>= set.seed(100) value <- runif(1) set.seed(100) result13 <- bplapply(1:3, runif, BPPARAM = SerialParam()) stopifnot( !identical(result1, result13), identical(value, runif(1)) ) @ \subsection{\Rcode{bpstart()} and random number streams} In all of the examples so far \Rcode{*Param()} objects are passed to \Rcode{bplapply()} or \Rcode{bpiterate()} in the 'stopped' state. Internally, \Rcode{bplapply()} and \Rcode{bpiterate()} invoke \Rcode{bpstart()} to establish the computational environment (e.g., starting workers for \Rcode{SnowParam()}). \Rcode{bpstart()} can be called explicitly, e.g., to allow workers to be used across calls to \Rcode{bplapply()}. The cluster random number stream is initiated with \Rcode{bpstart()}. Thus <>= param <- bpstart(SerialParam(RNGseed = 100)) result16 <- bplapply(1:3, runif, BPPARAM = param) bpstop(param) stopifnot( identical(result1, result16) ) @ This allows a second call to \Rcode{bplapply} to represent a continuation of a random number computation -- the second call to \Rcode{bplapply()} results in different random number streams for each element of \Rcode{X}. <>= param <- bpstart(SerialParam(RNGseed = 100)) result16 <- bplapply(1:3, runif, BPPARAM = param) result17 <- bplapply(1:3, runif, BPPARAM = param) bpstop(param) stopifnot( identical(result1, result16), !identical(result1, result17) ) @ \subsection{Relationship between \Rcode{bplapply()} and \Rcode{lapply()}} The results from \Rcode{bplapply()} are different from the results from \Rcode{lapply()}, even with the same random number seed. This is because correctly implemented parallel random streams require use of a particular random number generator invoked in specific ways for each element of \Rcode{X}, as outlined in the Implementation notes section. <>= set.seed(100) result20 <- lapply(1:3, runif) stopifnot( !identical(result1, result20) ) @ \section{Implementation notes} The implementation uses the L'Ecuyer-CMRG random number generator (see \Rcode{?RNGkind} and \Rcode{?parallel::clusterSetRNGStream} for additional details). This random number generates independent streams and substreams of random numbers. In \BiocParallel{}, each call to \Rcode{bpstart()} creates a new stream from the L'Ecuyer-CMRG generator. Each element in \Rcode{bplapply()} or \Rcode{bpiterate()} creates a new substream. Each application of \Rcode{FUN} is therefore using the L'Ecuyer-CMRG random number generator, with a substream that is independent of the substreams of all other elements. Within the user-supplied \Rcode{FUN} of \Rcode{bplapply()} or \Rcode{bpiterate()}, it is a mistake to use \Rcode{RNGkind()} to set a different random number generator, or to use \Rcode{set.seed()}. 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159111 %%EOF BiocParallel/inst/snow/0000755000175200017520000000000014136050065016070 5ustar00biocbuildbiocbuildBiocParallel/inst/snow/RMPInode.R0000755000175200017520000000126114136050065017633 0ustar00biocbuildbiocbuildlocal({ snowlib <- Sys.getenv("R_SNOW_LIB") outfile <- Sys.getenv("R_SNOW_OUTFILE") args <- commandArgs() pos <- match("--args", args) args <- args[-(1 : pos)] for (a in args) { pos <- regexpr("=", a) name <- substr(a, 1, pos - 1) value <- substr(a,pos + 1, nchar(a)) switch(name, SNOWLIB = snowlib <- value, OUT = outfile <- value) } if (! (snowlib %in% .libPaths())) .libPaths(c(snowlib, .libPaths())) library(methods) ## because Rscript as of R 2.7.0 doesn't load methods loadNamespace("Rmpi") loadNamespace("snow") BiocParallel::bprunMPIworker() quit("no") }) BiocParallel/inst/snow/RSOCKnode.R0000644000175200017520000000160714136050065017746 0ustar00biocbuildbiocbuildlocal({ master <- "localhost" port <- "" snowlib <- Sys.getenv("R_SNOW_LIB") outfile <- Sys.getenv("R_SNOW_OUTFILE") ##**** defaults to ""; document args <- commandArgs() pos <- match("--args", args) args <- args[-(1 : pos)] for (a in args) { pos <- regexpr("=", a) name <- substr(a, 1, pos - 1) value <- substr(a,pos + 1, nchar(a)) switch(name, MASTER = master <- value, PORT = port <- value, SNOWLIB = snowlib <- value, OUT = outfile <- value) } if (! (snowlib %in% .libPaths())) .libPaths(c(snowlib, .libPaths())) library(methods) ## because Rscript as of R 2.7.0 doesn't load methods loadNamespace("snow") if (port == "") port <- getClusterOption("port") BiocParallel::.bpworker_impl(snow::makeSOCKmaster(master, port)) quit("no") }) BiocParallel/inst/unitTests/0000755000175200017520000000000014136050065017104 5ustar00biocbuildbiocbuildBiocParallel/inst/unitTests/test_BatchJobsParam.R0000644000175200017520000000071614136050065023112 0ustar00biocbuildbiocbuildtest_BatchJobsParam <- function() { param <- BatchJobsParam(2, progressbar=FALSE, cleanup=TRUE) checkEquals(as.list(sqrt(1:3)), bplapply(1:3, sqrt, BPPARAM=param)) X <- list(1, "2", 3) param <- BatchJobsParam(2, progressbar=FALSE, stop.on.error=FALSE) res <- tryCatch({ bplapply(X, sqrt, BPPARAM=param) }, error=identity) checkTrue(is(res, "bplist_error")) checkTrue(is(attr(res, "result")[[2]], "remote_error")) } BiocParallel/inst/unitTests/test_BatchtoolsParam.R0000644000175200017520000003571214136050065023361 0ustar00biocbuildbiocbuild.old_options <- NULL .setUp <- function() .old_options <<- options(BIOCPARALLEL_BATCHTOOLS_REMOVE_REGISTRY_WAIT = 1) .tearDown <- function() { options(.old_options) } .n_connections <- function() { gc() # close connections nrow(showConnections()) } test_BatchtoolsParam_constructor <- function() { param <- BatchtoolsParam() checkTrue(validObject(param)) checkTrue(is(param$registry, "NULLRegistry")) isWindows <- .Platform$OS.type == "windows" cluster <- if (isWindows) "socket" else "multicore" nworkers <- if (isWindows) snowWorkers() else multicoreWorkers() checkIdentical(cluster, bpbackend(param)) checkIdentical(nworkers, bpnworkers(param)) checkIdentical(3L, bpnworkers(BatchtoolsParam(3L))) cluster <- "socket" param <- BatchtoolsParam(cluster=cluster) checkIdentical(cluster, bpbackend(param)) checkIdentical(nworkers, bpnworkers(param)) cluster <- "multicore" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(cluster=cluster) checkIdentical(cluster, bpbackend(param)) checkIdentical(nworkers, bpnworkers(param)) } cluster <- "interactive" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(cluster=cluster) checkIdentical(cluster, bpbackend(param)) checkIdentical(1L, bpnworkers(param)) } cluster <- "sge" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) } cluster <- "lsf" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) } cluster <- "slurm" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) } cluster <- "openlava" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) } cluster <- "torque" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) } cluster <- "unknown" checkException(BatchtoolsParam(cluster=cluster)) } test_BatchtoolsWorkers <- function() { socket <- snowWorkers() multicore <- multicoreWorkers() isWindows <- .Platform$OS.type == "windows" checkIdentical( if (isWindows) socket else multicore, batchtoolsWorkers() ) checkIdentical(socket, batchtoolsWorkers("socket")) cluster <- "multicore" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) checkIdentical(multicore, batchtoolsWorkers(cluster)) checkIdentical(1L, batchtoolsWorkers("interactive")) checkException(batchtoolsWorkers("unknown")) } .test_BatchtoolsParam_bpisup_start_stop <- function(param) { n_connections <- .n_connections() checkIdentical(FALSE, bpisup(param)) checkIdentical(TRUE, bpisup(bpstart(param))) checkIdentical(FALSE, bpisup(bpstop(param))) checkIdentical(n_connections, .n_connections()) } test_BatchtoolsParam_bpisup_start_stop_default <- function() { param <- BatchtoolsParam(workers=2) .test_BatchtoolsParam_bpisup_start_stop(param) } test_BatchtoolsParam_bpisup_start_stop_socket <- function() { cluster <- "socket" param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) .test_BatchtoolsParam_bpisup_start_stop(param) } test_BatchtoolsParam_bpisup_start_stop_interactive <- function() { cluster <- "interactive" param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical(cluster, bpbackend(param)) .test_BatchtoolsParam_bpisup_start_stop(param) } test_BatchtoolsParam_bplapply <- function() { n_connections <- .n_connections() fun <- function(x) Sys.getpid() ## Check for all cluster types cluster <- "interactive" param <- BatchtoolsParam(workers=2, cluster=cluster) result <- bplapply(1:5, fun, BPPARAM=param) checkIdentical(1L, length(unique(unlist(result)))) cluster <- "multicore" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) result <- bplapply(1:5, fun, BPPARAM=param) checkIdentical(2L, length(unique(unlist(result)))) } cluster <- "socket" param <- BatchtoolsParam(workers=2, cluster=cluster) result <- bplapply(1:5, fun, BPPARAM=param) checkIdentical(2L, length(unique(unlist(result)))) checkIdentical(n_connections, .n_connections()) } ## Check registry test_BatchtoolsParam_registry <- function() { n_connections <- .n_connections() param <- BatchtoolsParam() checkTrue(is(param$registry, "NULLRegistry")) bpstart(param) checkTrue(!is(param$registry, "NULLRegistry")) checkTrue(is(param$registry, "Registry")) bpstop(param) checkIdentical(n_connections, .n_connections()) } ## Check bpjobname test_BatchtoolsParam_bpjobname <- function() { checkIdentical("BPJOB", bpjobname(BatchtoolsParam())) checkIdentical("myjob", bpjobname(BatchtoolsParam(jobname="myjob"))) } ## Check bpstopOnError test_BatchtoolsParam_bpstopOnError <- function() { checkTrue(bpstopOnError(BatchtoolsParam())) checkIdentical(FALSE, bpstopOnError(BatchtoolsParam(stop.on.error=FALSE))) } ## Check bptimeout test_BatchtoolsParam_bptimeout <- function() { checkEquals(30L * 24L * 60L * 60L, bptimeout(BatchtoolsParam())) checkEquals(123L, bptimeout(BatchtoolsParam(timeout=123))) } ## Check bpRNGseed test_BatchtoolsParam_bpRNGseed <- function() { n_connections <- .n_connections() ## Check setting RNGseed param <- BatchtoolsParam(RNGseed=123L) checkEqualsNumeric(123L, bpRNGseed(param)) ## Check reset RNGseed new_seed <- 234L bpRNGseed(param) <- new_seed checkEqualsNumeric(new_seed, bpRNGseed(param)) ## Check after bpstart bpstart(param) checkEqualsNumeric(new_seed, bpRNGseed(param)) checkEqualsNumeric(new_seed, param$registry$seed) bpstop(param) ## Check failure to reset ## ## Check NULL value param <- BatchtoolsParam() checkTrue(is.na(bpRNGseed(param))) ## ## Check fail checkException({bpRNGseed(param) <- "abc"}) checkIdentical(n_connections, .n_connections()) } test_BatchtoolsParam_bplog <- function() { n_connections <- .n_connections() ## Test param w/o log and logdir checkTrue(is.na(bplogdir(BatchtoolsParam()))) checkTrue(!bplog(BatchtoolsParam())) ## test param with log, w/o logdir param <- BatchtoolsParam(log=TRUE) checkTrue(bplog(param)) checkTrue(is.na(bplogdir(param))) ## Check if setter works temp_log_dir <- tempfile() dir.create(temp_log_dir) bplogdir(param) <- temp_log_dir checkIdentical(temp_log_dir, bplogdir(param)) ## test param without log and w logdir checkException(BatchtoolsParam(logdir=temp_log_dir)) ## check logs in logdir param <- BatchtoolsParam(log=TRUE, logdir=temp_log_dir) bplapply(1:5, sqrt, BPPARAM=param) checkTrue(file.exists(temp_log_dir)) checkTrue(file.exists(file.path(temp_log_dir, "logs"))) checkIdentical(n_connections, .n_connections()) } test_BatchtoolsParam_available_clusters <- function() { clusters <- BiocParallel:::.BATCHTOOLS_CLUSTERS checkTrue(all.equal( c("socket", "multicore", "interactive", "sge", "slurm", "lsf", "openlava", "torque"), clusters)) } test_BatchtoolsParam_template <- function() { .bptemplate <- BiocParallel:::.bptemplate cluster <- "socket" param <- BatchtoolsParam(cluster=cluster) checkTrue(is.na(.bptemplate(param))) cluster <- "multicore" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(cluster=cluster) checkTrue(is.na(.bptemplate(param))) } cluster <- "interactive" param <- BatchtoolsParam(cluster=cluster) checkTrue(is.na(.bptemplate(param))) ## Test clusters with template cluster <- "sge" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical("sge-simple.tmpl", basename(.bptemplate(param))) } cluster <- "slurm" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical("slurm-simple.tmpl", basename(.bptemplate(param))) } cluster <- "lsf" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical("lsf-simple.tmpl", basename(.bptemplate(param))) } cluster <- "openlava" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical("openlava-simple.tmpl", basename(.bptemplate(param))) } cluster <- "torque" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) checkIdentical("torque-lido.tmpl", basename(.bptemplate(param))) } ## Check setting template to file path cluster <- "sge" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { template <- system.file( "templates", "sge-simple.tmpl", package="batchtools" ) param <- BatchtoolsParam( workers=2, cluster=cluster, template=template ) checkIdentical(template, .bptemplate(param)) } } ## Run only of SGE clusters, this will fail on other machines test_BatchtoolsParam_sge <- function() { n_connections <- .n_connections() if (!BiocParallel:::.batchtoolsClusterAvailable("sge")) return() fun <- function(x) Sys.getpid() template <- system.file( package="BiocParallel", "unitTests", "test_script", "test-sge-template.tmpl" ) param <- BatchtoolsParam(workers=2, cluster="sge", template=template) bpstart(param) checkIdentical("SGE", param$registry$backend) result <- bplapply(1:5, fun, BPPARAM=param) checkIdentical(2L, length(unique(unlist(result)))) bpstop(param) checkIdentical(n_connections, .n_connections()) } ## TODO: write tests for other cluster types, slurm, lsf, torque, openlava test_BatchtoolsParam_bpmapply <- function() { n_connections <- .n_connections() fun <- function(x, y, z) x + y + z ## Initial test param <- BatchtoolsParam() result <- bpmapply(fun, x = 1:3, y = 1:3, MoreArgs = list(z = 1), SIMPLIFY = TRUE, BPPARAM = param) checkIdentical(c(3,5,7), result) cluster <- "interactive" param <- BatchtoolsParam(workers=2, cluster=cluster) result <- bpmapply(fun, x = 1:3, y = 1:3, MoreArgs = list(z = 1), SIMPLIFY = TRUE, BPPARAM=param) checkIdentical(c(3,5,7), result) cluster <- "multicore" if (BiocParallel:::.batchtoolsClusterAvailable(cluster)) { param <- BatchtoolsParam(workers=2, cluster=cluster) result <- bpmapply(fun, x = 1:3, y = 1:3, MoreArgs = list(z = 1), SIMPLIFY = TRUE, BPPARAM=param) checkIdentical(c(3,5,7), result) } cluster <- "socket" param <- BatchtoolsParam(workers=2, cluster=cluster) result <- bpmapply(fun, x = 1:3, y = 1:3, MoreArgs = list(z = 1), SIMPLIFY = TRUE, BPPARAM=param) checkIdentical(c(3,5,7), result) checkIdentical(n_connections, .n_connections()) } test_BatchtoolsParam_bpvec <- function() { ## Mutlticore param <- BatchtoolsParam(workers=2) result <- bpvec(1:10, seq_along, BPPARAM=param) target <- as.integer(rep(1:5, 2)) checkIdentical(target, result) ## socket param <- BatchtoolsParam(workers=2, cluster="socket") result <- bpvec(1:10, seq_along, BPPARAM=param) target <- as.integer(rep(1:5,2)) checkIdentical(target, result) } test_BatchtoolsParam_bpvectorize <- function() { psqrt <- bpvectorize(sqrt) checkTrue(is(psqrt, "function")) ## Mutlticore param <- BatchtoolsParam(workers=2) bpseq_along <- bpvectorize(seq_along, BPPARAM=param) res <- bpseq_along(1:10) target <- as.integer(rep(1:5, 2)) checkIdentical(as.integer(target), res) ## Socket param <- BatchtoolsParam(workers=2, cluster="socket") bpseq_along <- bpvectorize(seq_along, BPPARAM=param) res <- bpseq_along(1:10) target <- as.integer(rep(1:5, 2)) checkIdentical(as.integer(target), res) } test_BatchtoolsParam_bpiterate <- function() { n_connections <- .n_connections() ## Iterator function ITER <- function(n=5) { i <- 0L function() { i <<- i + 1L if (i > n) return(NULL) rep(i, 100) } } ## test function FUN <- function(x, k) { sum(x) + k } ## Multicore cluster param <- BatchtoolsParam() res <- bpiterate(ITER=ITER(), FUN=FUN, k=5, BPPARAM=param) ## Check Identical result target <- list(105, 205, 305, 405, 505) checkIdentical(target, res) ## socket cluster param <- BatchtoolsParam(cluster="socket") res <- bpiterate(ITER=ITER(), FUN=FUN, k=5, BPPARAM=param) ## Check Identical result checkIdentical(target, res) ## Test REDUCE on socket res <- bpiterate(ITER=ITER(), FUN=FUN, k=5, REDUCE=`+`, BPPARAM=param) ## Check Identical result checkIdentical(1525, res) ## Test REDUCE, init on mutlicore param <- BatchtoolsParam() res <- bpiterate(ITER=ITER(), FUN=FUN, k=5, REDUCE=`+`, init = 10, BPPARAM=param) ## Check Identical result checkIdentical(1535, res) checkIdentical(n_connections, .n_connections()) } test_BatchtoolsParam_bpsaveregistry <- function() { .bpregistryargs <- BiocParallel:::.bpregistryargs .bpsaveregistry <- BiocParallel:::.bpsaveregistry .bpsaveregistry_path <- BiocParallel:::.bpsaveregistry_path file.dir <- tempfile() ## Set param with save registry registryargs <- batchtoolsRegistryargs(file.dir = file.dir) param <- BatchtoolsParam(saveregistry=TRUE, registryargs = registryargs) checkIdentical(.bpsaveregistry(param), TRUE) checkIdentical(.bpregistryargs(param)$file.dir, file.dir) ## increment path extension file.dir <- file.path(dirname(file.dir), basename(file.dir)) checkIdentical(.bpsaveregistry_path(param), paste0(file.dir, "-1")) dir.create(.bpsaveregistry_path(param)) checkIdentical(.bpsaveregistry_path(param), paste0(file.dir, "-2")) ## create registry path <- .bpsaveregistry_path(param) checkTrue(!dir.exists(path)) res <- bplapply(1:5, sqrt, BPPARAM=param) checkTrue(dir.exists(path)) } BiocParallel/inst/unitTests/test_BiocParallelParam.R0000644000175200017520000000105614136050065023602 0ustar00biocbuildbiocbuildtest_BiocParallelParam <- function() { ## BiocParallelParam is a virtual class checkException(BiocParallel:::.BiocParallelParam(), silent=TRUE) ## minimal non-virtual class & constructor .A <- setRefClass("A", contains = "BiocParallelParam") A <- function(...) { prototype <- .prototype_update(.BiocParallelParam_prototype, ...) do.call(.A, prototype) } ## no arg constructor checkTrue(validObject(A())) ## non-default inherited slot checkIdentical("WARN", bpthreshold(A(threshold = "WARN"))) } BiocParallel/inst/unitTests/test_DoparParam.R0000644000175200017520000000145514136050065022321 0ustar00biocbuildbiocbuildtest_DoparParam_orchestration_error <- function() { test <- requireNamespace("foreach", quietly = TRUE) && requireNamespace("doParallel", quietly = TRUE) if (!test) DEACTIVATED("'foreach' or 'doParallel' not available") if (identical(.Platform$OS.type, "windows")) DEACTIVATED("'DoparParam' orchestration error test not run on Windows") old_warn <- options(warn = 2) on.exit(options(old_warn)) y <- tryCatch({ cl <- parallel::makeCluster(1L) doParallel::registerDoParallel(cl) bplapply(1L, function(x) quit("no"), BPPARAM = DoparParam()) }, error = function(e) { conditionMessage(e) }, finally = { parallel::stopCluster(cl) }) checkTrue(startsWith(y, "'DoparParam()' foreach() error occurred: ")) } BiocParallel/inst/unitTests/test_MulticoreParam.R0000644000175200017520000000141214136050065023210 0ustar00biocbuildbiocbuildtest_MulticoreParam_progressbar <- function() { if (.Platform$OS.type == "windows") return() checkIdentical(bptasks(MulticoreParam()), 0L) checkIdentical(bptasks(MulticoreParam(tasks = 0L, progressbar = TRUE)), 0L) checkIdentical( bptasks(MulticoreParam(progressbar = TRUE)), .Machine$integer.max ) } test_MulticoreParam_bpforceGC <- function() { if (.Platform$OS.type == "windows") return() checkIdentical(TRUE, bpforceGC(MulticoreParam())) checkIdentical(FALSE, bpforceGC(MulticoreParam(force.GC = FALSE))) checkIdentical(TRUE, bpforceGC(MulticoreParam(force.GC = TRUE))) checkException(MulticoreParam(force.GC = NA), silent = TRUE) checkException(MulticoreParam(force.GC = 1:2), silent = TRUE) } BiocParallel/inst/unitTests/test_SerialParam.R0000644000175200017520000000210014136050065022457 0ustar00biocbuildbiocbuildtest_SerialParam_bpnworkers <- function() { checkIdentical(1L, bpnworkers(SerialParam())) checkIdentical(1L, bpnworkers(bpstart(SerialParam()))) checkIdentical(1L, bpnworkers(bpstop(bpstart(SerialParam())))) } test_SerialParam_bpbackend <- function() { checkIdentical(NULL, bpbackend(SerialParam())) checkTrue(is(bpbackend(bpstart(SerialParam())), "SerialBackend")) checkIdentical(NULL, bpbackend(bpstop(bpstart(SerialParam())))) } test_SerialParam_bpforceGC <- function() { checkIdentical(FALSE, bpforceGC(SerialParam())) checkIdentical(FALSE, bpforceGC(SerialParam(force.GC = FALSE))) checkIdentical(TRUE, bpforceGC(SerialParam(force.GC = TRUE))) checkException(SerialParam(force.GC = NA), silent = TRUE) checkException(SerialParam(force.GC = 1:2), silent = TRUE) } test_SerialParam_bpisup_start_stop <- function() { param <- SerialParam() checkIdentical(FALSE, bpisup(param)) # not always up param <- bpstart(param) checkIdentical(TRUE, bpisup(param)) param <- bpstop(param) checkIdentical(FALSE, bpisup(param)) } BiocParallel/inst/unitTests/test_SnowParam.R0000644000175200017520000000726014136050065022202 0ustar00biocbuildbiocbuildtest_SnowParam_construction <- function() { checkException(SnowParam(logdir = tempdir())) p <- MulticoreParam(jobname = 'test') checkIdentical(bpjobname(p), 'test') } test_SnowParam_SOCK <- function() { if (!requireNamespace("snow", quietly=TRUE)) DEACTIVATED("'snow' package did not load") param <- SnowParam(2, "SOCK", tasks=2) checkIdentical(FALSE, bpisup(param)) exp <- bplapply(1:2, function(i) Sys.getpid(), BPPARAM=param) checkIdentical(2L, length(unique(unlist(exp)))) checkIdentical(FALSE, bpisup(param)) } test_SnowParam_SOCK_character <- function() { bpstop(bpstart(SnowParam("localhost"))) } test_SnowParam_MPI <- function() { if (.Platform$OS.type == "windows") DEACTIVATED("MPI tests not run on Windows") DEACTIVATED("MPI tests not run") param <- SnowParam(2, "MPI", tasks=2) checkIdentical(FALSE, bpisup(param)) exp <- bplapply(1:2, function(i) mpi.comm.rank(), BPPARAM=param) checkIdentical(c(1L, 2L), sort(unlist(exp))) checkIdentical(FALSE, bpisup(param)) } test_SnowParam_coerce_from_SOCK <- function() { if (!requireNamespace("snow", quietly=TRUE)) DEACTIVATED("'snow' package did not load") cl <- parallel::makeCluster(2L, "SOCK") p <- as(cl, "SnowParam") checkTrue(validObject(p)) obs <- tryCatch(bpstart(p), error=conditionMessage) exp <- "'bpstart' not available; instance from outside BiocParallel?" checkIdentical(exp, obs) obs <- tryCatch(bpstop(p), warning=conditionMessage) exp <- "'bpstop' not available; instance from outside BiocParallel?" checkIdentical(exp, obs) exp <- bplapply(1:2, function(i) Sys.getpid(), BPPARAM=p) checkIdentical(2L, length(unique(unlist(exp)))) checkIdentical(TRUE, bpisup(p)) parallel::stopCluster(cl) } test_SnowParam_coerce_from_MPI <- function() { if (.Platform$OS.type == "windows") DEACTIVATED("MPI tests not run on Windows") if (!requireNamespace("snow", quietly=TRUE) || !requireNamespace("Rmpi", quietly=TRUE)) DEACTIVATED("'snow' and/or 'Rmpi' package did not load") DEACTIVATED("MPI tests not run") cl <- parallel::makeCluster(2L, "MPI") p <- as(cl, "SnowParam") checkTrue(validObject(p)) obs <- tryCatch(bpstart(p), error=conditionMessage) exp <- "'bpstart' not available; instance from outside BiocParallel?" checkIdentical(exp, obs) obs <- tryCatch(bpstop(p), error=conditionMessage) exp <- "'bpstop' not available; instance from outside BiocParallel?" checkIdentical(exp, obs) exp <- bplapply(1:2, function(i) mpi.comm.rank(), BPPARAM=p) checkIdentical(c(1L, 2L), sort(unlist(exp))) checkIdentical(TRUE, bpisup(p)) parallel::stopCluster(cl) } test_SnowParam_workers <- function() { if (.Platform$OS.type == "windows") return() if (!requireNamespace("snow", quietly=TRUE) || !requireNamespace("Rmpi", quietly=TRUE)) DEACTIVATED("'snow' and/or 'Rmpi' package did not load") checkException(SnowParam("host", "MPI"), silent=TRUE) checkException(SnowParam("host", "FORK"), silent=TRUE) } test_SnowParam_progressbar <- function() { checkIdentical(bptasks(SnowParam()), 0L) checkIdentical(bptasks(SnowParam(tasks = 0L, progressbar = TRUE)), 0L) checkIdentical(bptasks(SnowParam(progressbar = TRUE)), .Machine$integer.max) } test_SnowParam_bpforceGC <- function() { checkIdentical(FALSE, bpforceGC(SnowParam())) checkIdentical(FALSE, bpforceGC(SnowParam(force.GC = FALSE))) checkIdentical(TRUE, bpforceGC(SnowParam(force.GC = TRUE))) checkException(SnowParam(force.GC = NA), silent = TRUE) checkException(SnowParam(force.GC = 1:2), silent = TRUE) } BiocParallel/inst/unitTests/test_bpaggregate.R0000644000175200017520000000176214136050065022544 0ustar00biocbuildbiocbuildtest_bpaggregate <- function() { x <- data.frame(a=1:10, b=10:1) by <- list(c(rep("a", 5), rep("b", 5))) simplify <- TRUE FUN <- mean x1 <- aggregate(x, by=by, FUN=FUN) param <- bpparam() bpworkers(param) <- 2 x2 <- bpaggregate(x, by=by, FUN=FUN, BPPARAM=param, simplify=simplify) checkEquals(x1, x2) by[[2]] <- c(rep("c", 8), rep("d", 2)) x1 <- aggregate(x, by=by, FUN=FUN) x2 <- bpaggregate(x, by=by, FUN=FUN, BPPARAM=param, simplify=simplify) checkEquals(x1, x2) closeAllConnections() TRUE } test_bpaggregate_formula <- function() { f <- Sepal.Length ~ Species iris1 <- iris iris1$Species <- # FIXME: bpaggregate doesn't respect factor as.character(iris1$Species) x1 <- aggregate(f, data=iris1, FUN=sum) x2 <- bpaggregate(f, data = iris1, FUN = sum) checkIdentical(x1, x2) iris1 <- iris1[sample(nrow(iris1)),] x3 <- bpaggregate(f, data = iris1, FUN = sum) checkIdentical(x2, x3) } BiocParallel/inst/unitTests/test_bpexportglobals.R0000644000175200017520000000256314136050065023503 0ustar00biocbuildbiocbuildtest_bpexportglobals_params <- function() { ## Multicore if (.Platform$OS.type == "unix") { param <- MulticoreParam() checkIdentical(bpexportglobals(param), TRUE) bpexportglobals(param) <- FALSE checkIdentical(bpexportglobals(param), FALSE) param <- MulticoreParam(exportglobals=FALSE) checkIdentical(bpexportglobals(param), FALSE) } ## Snow param <- SnowParam() checkIdentical(bpexportglobals(param), TRUE) bpexportglobals(param) <- FALSE checkIdentical(bpexportglobals(param), FALSE) param <- SnowParam(exportglobals=FALSE) checkIdentical(bpexportglobals(param), FALSE) ## Batchtools param <- BatchtoolsParam() checkIdentical(bpexportglobals(param), TRUE) bpexportglobals(param) <- FALSE checkIdentical(bpexportglobals(param), FALSE) param <- BatchtoolsParam(exportglobals=FALSE) checkIdentical(bpexportglobals(param), FALSE) } test_bpexportglobals_bplapply <- function() { oopts <- options(BAR="baz") on.exit(options(oopts)) param <- SnowParam(2L, exportglobals=FALSE) current <- bplapply(1:2, function(i) getOption("BAR"), BPPARAM=param) checkIdentical(NULL, unlist(current)) param <- SnowParam(2L, exportglobals=TRUE) current <- bplapply(1:2, function(i) getOption("BAR"), BPPARAM=param) checkIdentical("baz", unique(unlist(current))) } BiocParallel/inst/unitTests/test_bpiterate.R0000644000175200017520000001007014136050065022243 0ustar00biocbuildbiocbuildquiet <- suppressWarnings .lazyCount <- function(count) { i <- 0L function() { if (i >= count) return(NULL) i <<- i + 1L i } } test_bpiterate_Params <- function() { ## chunks greater than number of workers x <- 1:5 expected <- lapply(x, sqrt) FUN <- function(count, ...) sqrt(count) params <- list(serial=SerialParam(), snow=SnowParam(2)) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2) for (p in params) { ITER <- .lazyCount(length(x)) quiet(res <- bpiterate(ITER, FUN, BPPARAM=p)) checkIdentical(expected, res) } ## chunks less than number of workers x <- 1:2 expected <- lapply(x, sqrt) FUN <- function(count, ...) sqrt(count) params <- list(serial=SerialParam(), snow=SnowParam(3)) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(3) for (p in params) { ITER <- .lazyCount(length(x)) quiet(res <- bpiterate(ITER, FUN, BPPARAM=p)) checkIdentical(expected, res) } doParallel::registerDoParallel(2) params <- list(dopar=DoparParam(), batchjobs=BatchJobsParam(2, progressbar=FALSE)) for (p in params) { ITER <- .lazyCount(length(x)) checkException(bpiterate(ITER, FUN, BPPARAM=p), silent=TRUE) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } test_bpiterate_REDUCE <- function() { ncount <- 3L params <- list(snow=SnowParam(ncount)) ## On Windows MulticoreParam dispatches to SerialParam where ## 'reduce.in.order' does not apply (always TRUE) if (.Platform$OS.type != "windows") params <- c(params, multi=MulticoreParam(ncount)) for (p in params) { ## no REDUCE FUN <- function(count, ...) rep(count, 10) ITER <- .lazyCount(ncount) res <- bpiterate(ITER, FUN, BPPARAM=p) checkTrue(length(res) == ncount) expected <- list(rep(1L, 10), rep(2L, 10), rep(3L, 10)) checkIdentical(expected, res) ## REDUCE FUN <- function(count, ...) rep(count, 10) ITER <- .lazyCount(ncount) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=`+`) checkIdentical(rep(6L, 10), res) FUN <- function(count, ...) { Sys.sleep(3 - count) count } ## 'reduce.in.order' FALSE ITER <- .lazyCount(ncount) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=paste0, reduce.in.order=FALSE) checkIdentical("321", res) ITER <- .lazyCount(ncount) res <- quiet(bpiterate(ITER, FUN, BPPARAM=p, REDUCE=paste0, init=0, reduce.in.order=FALSE)) checkIdentical("0321", res) ## 'reduce.in.order' TRUE ITER <- .lazyCount(ncount) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=paste0, reduce.in.order=TRUE) checkIdentical("123", res) ITER <- .lazyCount(ncount) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=paste0, init=0, reduce.in.order=TRUE) checkIdentical("0123", res) } ## clean up closeAllConnections() TRUE } test_bpiterate_REDUCE_SerialParam <- function() { p <- SerialParam() FUN <- identity ## REDUCE missing, concatenate ITER <- .lazyCount(0) res <- bpiterate(ITER, FUN, BPPARAM=p) checkIdentical(list(), res) ITER <- .lazyCount(1) res <- bpiterate(ITER, FUN, BPPARAM=p) checkIdentical(list(1L), res) ITER <- .lazyCount(5) res <- bpiterate(ITER, FUN, BPPARAM=p) checkIdentical(as.list(1:5), res) ## REDUCE == `+` ITER <- .lazyCount(0) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=`+`) checkIdentical(list(), res) ITER <- .lazyCount(1) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=`+`) checkIdentical(1L, res) ITER <- .lazyCount(5) res <- bpiterate(ITER, FUN, BPPARAM=p, REDUCE=`+`) checkIdentical(15L, res) } BiocParallel/inst/unitTests/test_bplapply.R0000644000175200017520000000761414136050065022121 0ustar00biocbuildbiocbuildquiet <- suppressWarnings test_bplapply_Params <- function() { doParallel::registerDoParallel(2) params <- list(serial=SerialParam(), snow=SnowParam(2), dopar=DoparParam(), batchjobs=BatchJobsParam(2, progressbar=FALSE)) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2) x <- 1:10 expected <- lapply(x, sqrt) for (param in names(params)) { current <- quiet(bplapply(x, sqrt, BPPARAM=params[[param]])) checkIdentical(expected, current) } # test empty input for (param in names(params)) { current <- quiet(bplapply(list(), identity, BPPARAM=params[[param]])) checkIdentical(list(), current) } # unnamed args for BatchJobs -> dispatches to batchMap f <- function(i, x, y, ...) { list(y, i, x) } current <- bplapply(2:1, f, c("A", "B"), x=10, BPPARAM=BatchJobsParam(2, progressbar=FALSE)) checkTrue(all.equal(current[[1]], list(c("A", "B"), 2, 10))) checkTrue(all.equal(current[[2]], list(c("A", "B"), 1, 10))) ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } test_bplapply_symbols <- function() { doParallel::registerDoParallel(2) params <- list(serial=SerialParam(), snow=SnowParam(2), dopar=DoparParam() ## FIXME, batchjobs=BatchJobsParam(2, progressbar=FALSE)) ) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2) x <- list(as.symbol(".XYZ")) expected <- lapply(x, as.character) for (param in names(params)) { current <- quiet(bplapply(x, as.character, BPPARAM=params[[param]])) checkIdentical(expected, current) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } test_bplapply_named_list <- function() { X <- list() Y <- character() checkIdentical(X, bplapply(X, identity)) checkIdentical(X, bplapply(Y, identity)) names(X) <- names(Y) <- character() checkIdentical(X, bplapply(X, identity)) checkIdentical(X, bplapply(Y, identity)) X <- list(a = 1:2) checkIdentical(X, bplapply(X, identity)) X <- list(c(a = 1)) checkIdentical(X, bplapply(X, identity)) X <- list(A = c(a = 1:2, b = 1:3), B = c(b = 1:2)) checkIdentical(X, bplapply(X, identity)) X <- list(a = 1:2, b = 3:4) checkIdentical(X, bplapply(X, identity)) X <- list(c(a = 1)) checkIdentical(X, bplapply(X, identity)) X <- list(A = c(a = 1, b=2), B = c(c = 1, d = 2)) checkIdentical(X, bplapply(X, identity)) } test_bplapply_named_list_with_REDO <- function(){ X = setNames(1:3, letters[1:3]) param = SerialParam(stop.on.error = FALSE) FUN1 = function(i) if (i == 2) stop() else i result <- bptry(bplapply(X, FUN1, BPPARAM = param)) checkIdentical(names(result), names(X)) FUN2 = function(i) 0 redo <- bplapply(X, FUN2, BPREDO = result, BPPARAM = param) checkIdentical(names(redo), names(X)) } test_bplapply_custom_subsetting <- function(){ ## We have a class A in the previous unit test .B <- setClass("B", slots = c(b = "integer")) setMethod("[", "B", function(x, i, j, ...) initialize(x, b = x@b[i])) setMethod("length", "B", function(x) length(x@b)) as.list.B <<- function(x, ...) lapply(seq_along(x), function(i) x[i]) x <- .B(b = 1:3) expected <- lapply(x, function(elt) elt@b) current <- quiet(bplapply(x, function(elt) elt@b, BPPARAM=SerialParam())) checkIdentical(expected, current) ## Remote worker does not have the definition of the class B res <- tryCatch( bplapply(x, function(elt) elt@b, BPPARAM=SnowParam(workers = 2)), error = identity ) checkTrue(is(res, "bplist_error")) rm(as.list.B, inherits = TRUE) } BiocParallel/inst/unitTests/test_bploop.R0000644000175200017520000000372114136050065021564 0ustar00biocbuildbiocbuildtest_reducer_1 <- function() { r <- BiocParallel:::.reducer() checkIdentical(list(), r$value()) checkIdentical(list(1), { r$add(1, list(1)); r$value() }) checkIdentical(list(1, 2), { r$add(2, list(2)); r$value() }) checkIdentical(list(1, 2, 4), { r$add(4, list(4)); r$value() }) checkIdentical(list(1, 2, 3, 4), { r$add(3, list(3)); r$value() }) } test_reducer_2 <- function() { r <- BiocParallel:::.reducer(c) checkIdentical(list(), r$value()) checkIdentical(c(1), { r$add(1, list(1)); r$value() }) checkIdentical(c(1, 2), { r$add(2, list(2)); r$value() }) checkIdentical(c(1, 2, 4), { r$add(4, list(4)); r$value() }) checkIdentical(c(1, 2, 4, 3), { r$add(3, list(3)); r$value() }) } test_reducer_3 <- function() { r <- BiocParallel:::.reducer(`+`, init=0) checkIdentical(0, r$value()) checkIdentical(1, { r$add(1, list(1)); r$value() }) checkIdentical(3, { r$add(2, list(2)); r$value() }) checkIdentical(7, { r$add(4, list(4)); r$value() }) } test_reducer_4 <- function() { r <- BiocParallel:::.reducer(c, reduce.in.order=TRUE) checkIdentical(list(), r$value()) checkIdentical(1, { r$add(1, list(1)); r$value() }) checkIdentical(c(1, 2), { r$add(2, list(2)); r$value() }) checkIdentical(TRUE, r$isComplete()) checkIdentical(c(1, 2), { r$add(4, list(4)); r$value() }) checkIdentical(FALSE, r$isComplete()) checkIdentical(c(1, 2, 3, 4), { r$add(3, list(3)); r$value() }) checkIdentical(TRUE, r$isComplete()) } test_reducer_5 <- function() { r <- BiocParallel:::.reducer(`+`, init=0, reduce.in.order=TRUE) checkIdentical(0, r$value()) checkIdentical(1, { r$add(1, list(1)); r$value() }) checkIdentical(3, { r$add(2, list(2)); r$value() }) checkIdentical(TRUE, r$isComplete()) checkIdentical(3, { r$add(4, list(4)); r$value() }) checkIdentical(FALSE, r$isComplete()) checkIdentical(10, { r$add(3, list(3)); r$value() }) checkIdentical(TRUE, r$isComplete()) } BiocParallel/inst/unitTests/test_bpmapply.R0000644000175200017520000001064514136050065022120 0ustar00biocbuildbiocbuildquiet <- suppressWarnings test_bpmapply_MoreArgs_names <- function() { ## https://github.com/Bioconductor/BiocParallel/issues/51 f <- function(x, y) x target <- bpmapply(f, 1:3, MoreArgs=list(x=1L)) checkIdentical(rep(1L, 3), target) } test_bpmapply_Params <- function() { if (.Platform$OS.type != "windows") { doParallel::registerDoParallel(2) params <- list(serial=SerialParam(), snow=SnowParam(2), dopar=DoparParam(), batchjobs=BatchJobsParam(2, progressbar=FALSE), mc <- MulticoreParam(2)) x <- 1:10 y <- rev(x) f <- function(x, y) x + y expected <- x + y for (param in params) { current <- quiet(bpmapply(f, x, y, BPPARAM=param)) checkIdentical(expected, current) closeAllConnections() } # test names and simplify x <- setNames(1:5, letters[1:5]) for (param in params) { for (SIMPLIFY in c(FALSE, TRUE)) { for (USE.NAMES in c(FALSE, TRUE)) { expected <- mapply(identity, x, USE.NAMES=USE.NAMES, SIMPLIFY=SIMPLIFY) current <- quiet(bpmapply(identity, x, USE.NAMES=USE.NAMES, SIMPLIFY=SIMPLIFY, BPPARAM=param)) checkIdentical(expected, current) closeAllConnections() } } } # test MoreArgs x <- setNames(1:5, letters[1:5]) f <- function(x, m) { x + m } expected <- mapply(f, x, MoreArgs=list(m=1)) for (param in params) { current <- quiet(bpmapply(f, x, MoreArgs=list(m=1), BPPARAM=param)) checkIdentical(expected, current) closeAllConnections() } # test empty input for (param in params) { current <- quiet(bpmapply(identity, BPPARAM=param)) checkIdentical(list(), current) closeAllConnections() } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } else TRUE } test_bpmapply_symbols <- function() { doParallel::registerDoParallel(2) params <- list(serial=SerialParam(), snow=SnowParam(2), dopar=DoparParam()) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2) x <- list(as.symbol(".XYZ")) expected <- mapply(as.character, x) for (param in names(params)) { current <- bpmapply(as.character, x, BPPARAM=params[[param]]) checkIdentical(expected, current) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } test_bpmapply_named_list <- function() { X <- list() Y <- character() checkIdentical(X, bpmapply(identity)) checkIdentical(X, bpmapply(identity, X)) checkIdentical(mapply(identity, Y), bpmapply(identity, Y)) checkIdentical(X, bpmapply(identity, USE.NAMES = FALSE)) checkIdentical(X, bpmapply(identity, X, USE.NAMES = FALSE)) checkIdentical(X, bpmapply(identity, Y, USE.NAMES = FALSE)) names(X) <- names(Y) <- character() checkIdentical(X, bpmapply(identity, X)) checkIdentical(X, bpmapply(identity, Y)) checkIdentical(list(), bpmapply(identity, X, USE.NAMES = FALSE)) checkIdentical(list(), bpmapply(identity, Y, USE.NAMES = FALSE)) Y1 <- setNames(letters, letters) Y2 <- setNames(letters, LETTERS) checkIdentical(mapply(identity, Y1), bpmapply(identity, Y1)) checkIdentical(mapply(identity, Y2), bpmapply(identity, Y2)) X <- list(c(a = 1)) checkIdentical(X, bpmapply(identity, X, SIMPLIFY = FALSE)) X <- list(a = 1:2) checkIdentical(X, bpmapply(identity, X, SIMPLIFY = FALSE)) X <- list(a = 1:2, b = 1:4) checkIdentical(X, bpmapply(identity, X, SIMPLIFY = FALSE)) X <- list(A = c(a = 1:3)) checkIdentical(X, bpmapply(identity, X, SIMPLIFY = FALSE)) X <- list(A = c(a = 1, b=2), B = c(c = 1, d = 2)) checkIdentical(X, bpmapply(identity, X, SIMPLIFY = FALSE)) ## named arguments to bpmapply Y <- 1:3 checkIdentical(Y, bpmapply(identity, x = Y)) } BiocParallel/inst/unitTests/test_bpvalidate.R0000644000175200017520000000611214136050065022401 0ustar00biocbuildbiocbuildBPValidate <- BiocParallel:::BPValidate test_bpvalidate_basic_ok <- function() { target <- BPValidate() checkIdentical(target, bpvalidate(function() {} )) checkIdentical(target, bpvalidate(function(x) x )) checkIdentical(target, bpvalidate(function(x) x() )) checkIdentical(target, bpvalidate(function(..., x) x )) checkIdentical(target, bpvalidate(function(..., x) x() )) checkIdentical(target, bpvalidate(function(y, x) y(x) )) checkIdentical(target, bpvalidate(function(y, x) y(x=x) )) checkIdentical(target, bpvalidate(function(y, ...) y(...) )) checkIdentical(target, bpvalidate(local({i = 2; function(y) y + i}))) checkIdentical( target, bpvalidate(local({i = 2; local({function(y) y + i})})) ) checkIdentical(target, bpvalidate(sqrt)) } test_bpvalidate_basic_fail <- function() { target <- BPValidate(unknown = "x") suppressWarnings({ checkIdentical(target, bpvalidate(function() x )) checkIdentical(target, bpvalidate(function() x() )) checkIdentical(target, bpvalidate(function(y) x + y )) checkIdentical(target, bpvalidate(function(y) y(x) )) checkIdentical(target, bpvalidate(function(y) y(x=x) )) checkIdentical(target, bpvalidate(function(y, ...) y(x) )) checkIdentical(target, bpvalidate(function(y, ...) y(x=x) )) }) } test_bpvalidate_search_path <- function() { target <- BPValidate(symbol = "x", environment = ".test_env") .test_env <- new.env(parent=emptyenv()) .test_env$x <- NULL attach(.test_env) on.exit(detach(.test_env)) checkIdentical(target, bpvalidate(function() x )) checkIdentical(target, bpvalidate(function(...) x )) checkIdentical(target, bpvalidate(function(y, ...) y(x) )) checkIdentical(target, bpvalidate(function(y, ...) y(x=x) )) ## FIXME: should fail -- in search(), but not a function! ## checkIdentical(target, bpvalidate(function() x() )) } test_bpvalidate_defining_environemt <- function() { target1 <- BPValidate() target2 <- BPValidate(unknown = "x") h = function() { x <- 1; f = function() x; function() f() } checkIdentical(target1, bpvalidate(h)) h = function() { f = function() x; function() f() } checkIdentical(target2, bpvalidate(h, "silent")) } test_bpvalidate_library <- function() { target <- BPValidate() checkException(bpvalidate(function() library("__UNKNOWN__")), silent=TRUE) checkException(bpvalidate(function() require("__UNKNOWN__")), silent=TRUE) checkIdentical(target, bpvalidate(function() library(BiocParallel))) ## FIXME: bpvalidate expects unquoted arg to library() / require() ## bpvalidate(function() library("BiocParallel")) target1 <- BPValidate( symbol = "bpvalidate", environment = "package:BiocParallel" ) checkIdentical(target1, bpvalidate(function() bpvalidate())) # inPath fun <- function() { library(BiocParallel); bpvalidate() } checkIdentical(target, bpvalidate(fun)) # in function } BiocParallel/inst/unitTests/test_bpvec.R0000644000175200017520000000267014136050065021372 0ustar00biocbuildbiocbuildtest_bpvec_Params <- function() { doParallel::registerDoParallel(2) params <- list(serial=SerialParam(), snow=SnowParam(2), batchjobs=BatchJobsParam(2, progressbar=FALSE), dopar=DoparParam()) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2) x <- rev(1:10) expected <- sqrt(x) for (param in names(params)) { current <- bpvec(x, sqrt, BPPARAM=params[[param]]) checkIdentical(current, expected) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } test_bpvec_MulticoreParam_short_jobs <- function() { ## bpvec should return min(length(X), bpnworkers()) if (.Platform$OS.type == "windows") return(TRUE) exp <- 1:2 obs <- bpvec(exp, c, AGGREGATE=list, BPPARAM=MulticoreParam(workers=4L)) checkIdentical(2L, length(obs)) checkIdentical(exp, unlist(obs)) ## clean up closeAllConnections() TRUE } test_bpvec_invalid_FUN <- function() { res <- bptry(bpvec(1:2, class, BPPARAM=SerialParam())) checkTrue(inherits(res, "bpvec_error")) } test_bpvec_named_list <- function() { X <- list() Y <- character() checkIdentical(X, bpvec(X, length)) checkIdentical(X, bpvec(Y, length)) names(X) <- names(Y) <- character() checkIdentical(X, bpvec(X, length)) checkIdentical(X, bpvec(Y, length)) } BiocParallel/inst/unitTests/test_bpvectorize.R0000644000175200017520000000121314136050065022617 0ustar00biocbuildbiocbuildtest_bpvectorize_Params <- function() { doParallel::registerDoParallel(2) params <- list(serial=SerialParam(), snow=SnowParam(2), batchjobs=BatchJobsParam(2, progressbar=FALSE), dopar=DoparParam()) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2) x <- 1:10 expected <- sqrt(x) for (param in names(params)) { psqrt <- bpvectorize(sqrt, BPPARAM=params[[param]]) checkIdentical(expected, psqrt(x)) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } BiocParallel/inst/unitTests/test_errorhandling.R0000644000175200017520000002146314136050065023132 0ustar00biocbuildbiocbuild## NOTE: On Windows, MulticoreParam() throws a warning and instantiates ## a single FORK worker using scripts from parallel. No logging or ## error catching is implemented. checkExceptionText <- function(expr, txt, negate=FALSE, msg="") { x <- try(eval(expr), silent=TRUE) checkTrue(inherits(x, "condition"), msg=msg) checkTrue(xor(negate, grepl(txt, as.character(x), fixed=TRUE)), msg=msg) } test_composeTry <- function() { .composeTry <- BiocParallel:::.composeTry X <- as.list(1:6); X[[2]] <- "2"; X[[6]] <- -1 ## fail hard, e.g., SerialParam() tsqrt <- .composeTry(sqrt, FALSE, TRUE, TRUE, timeout=20L) current <- tryCatch(lapply(X, tsqrt), error=identity) target <- tryCatch(lapply(X, sqrt), error=identity) checkIdentical(conditionMessage(target), conditionMessage(current)) ## fail soft, e.g., SerialParam(stop.on.error=FALSE) tsqrt <- .composeTry(sqrt, FALSE, FALSE, FALSE, timeout=20L) current <- tryCatch(suppressWarnings(lapply(X, tsqrt)), error=identity) target <- list(length(X)) for (i in seq_along(X)) target[[i]] <- tryCatch(suppressWarnings(sqrt(X[[i]])), error=identity) tok <- !vapply(target, is, logical(1), "error") checkIdentical(tok, bpok(current)) checkIdentical(conditionMessage(target[[which(!tok)]]), conditionMessage(current[[which(!bpok(current))]])) checkIdentical(target[tok], current[bpok(current)]) ## fail soft on an individual worker; entire vector returned with ## 'unevaluated' components. e.g., SnowParam(stop.on.error=TRUE) tsqrt <- .composeTry(sqrt, FALSE, TRUE, FALSE, timeout=20L) current <- lapply(X, tsqrt) checkTrue(is(current[[2]], "remote_error")) checkTrue(all(vapply(current[-(1:2)], is, logical(1), "unevaluated_error"))) ## illogical checkException(.composeTry(sqrt, FALSE, FALSE, TRUE, timeout=20L), silent=TRUE) } test_SerialParam_stop.on.error <- function() { X <- list(1, "2", 3) ## stop.on.error=TRUE; lapply-like p <- SerialParam() checkIdentical(TRUE, bpstopOnError(p)) checkException(bplapply(X, sqrt, BPPARAM=p), silent=TRUE) current <- tryCatch(bplapply(X, sqrt, BPPARAM=p), error=identity) checkTrue(is(current, "bplist_error")) target <- "BiocParallel errors\n 1 remote errors, element index: 2\n 1 unevaluated and other errors\n first remote error: non-numeric argument to mathematical function" checkIdentical(target, conditionMessage(current)) target <- tryCatch(lapply(X, sqrt), error=identity) checkIdentical( conditionMessage(target), conditionMessage(attr(current, "result")[[2]]) ) result <- bptry(bplapply(X, sqrt, BPPARAM=p)) # issue #142 checkIdentical(c(TRUE, FALSE, FALSE), bpok(result)) ## stop.on.error=FALSE p <- SerialParam(stop.on.error=FALSE) # checkException(bplapply(X, sqrt, BPPARAM=p), silent=TRUE) current <- tryCatch(bplapply(X, sqrt, BPPARAM=p), error=identity) checkTrue(is(current, "bplist_error")) result <- attr(current, "result") checkIdentical(c(TRUE, FALSE, TRUE), bpok(result)) checkTrue(is(result[[2]], "remote_error")) checkIdentical(list(sqrt(1), sqrt(3)), result[bpok(result)]) result <- bptry(bplapply(X, sqrt, BPPARAM=p)) checkIdentical(c(TRUE, FALSE, TRUE), bpok(result)) } test_stop.on.error <- function() { checkException(bplapply("2", sqrt), silent=TRUE) checkException(bplapply(c(1, "2"), sqrt), silent=TRUE) checkException(bplapply(c(1, "2", 3), sqrt), silent=TRUE) cls <- tryCatch(bplapply(c(1, "2", 3), sqrt), error=class) checkIdentical(c("bplist_error", "bperror", "error", "condition"), cls) } test_catching_errors <- function() { if (.Platform$OS.type != "windows") { x <- 1:10 y <- rev(x) f <- function(x, y) if (x > y) stop("whooops") else x + y doParallel::registerDoParallel(2) params <- list( mc = MulticoreParam(2, stop.on.error=FALSE), snow=SnowParam(2, stop.on.error=FALSE), dopar=DoparParam(stop.on.error=FALSE), batchjobs=BatchJobsParam(2, progressbar=FALSE, stop.on.error=FALSE)) for (param in params) { res <- tryCatch({ bplapply(list(1, "2", 3), sqrt, BPPARAM=param) }, error=identity) checkTrue(is(res, "bplist_error")) result <- attr(res, "result") checkTrue(length(result) == 3L) msg <- "non-numeric argument to mathematical function" checkIdentical(conditionMessage(result[[2]]), msg) closeAllConnections() } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() TRUE } else TRUE } test_BPREDO <- function() { if (.Platform$OS.type != "windows") { f = sqrt x = list(1, "2", 3) x.fix = list(1, 2, 3) doParallel::registerDoParallel(2) params <- list( mc = MulticoreParam(2, stop.on.error=FALSE), snow=SnowParam(2, stop.on.error=FALSE), dopar=DoparParam(stop.on.error=FALSE), batchjobs=BatchJobsParam(2, progressbar=FALSE, stop.on.error=FALSE)) for (param in params) { res <- tryCatch({ bplapply(x, f, BPPARAM=param) }, error=identity) checkTrue(is(res, "bplist_error")) result <- attr(res, "result") checkIdentical(3L, length(result)) checkTrue(inherits(result[[2]], "remote_error")) closeAllConnections() Sys.sleep(0.25) ## data not fixed res2 <- tryCatch({ bplapply(x, f, BPPARAM=param, BPREDO=result) }, error=identity) checkTrue(is(res2, "bplist_error")) result <- attr(res2, "result") checkIdentical(3L, length(result)) checkTrue(is(result[[2]], "remote_error")) checkIdentical(as.list(sqrt(c(1, 3))), result[c(1, 3)]) closeAllConnections() Sys.sleep(0.25) ## data fixed res3 <- bplapply(x.fix, f, BPPARAM=param, BPREDO=result) checkIdentical(as.list(sqrt(1:3)), res3) closeAllConnections() Sys.sleep(0.25) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() } TRUE } test_bpvec_BPREDO <- function() { if (.Platform$OS.type != "windows") { f = function(i) if (2 %in% i) stop() else sqrt(i) x = 1:10 doParallel::registerDoParallel(2) params <- list( mc = MulticoreParam(2, stop.on.error=FALSE), snow=SnowParam(2, stop.on.error=FALSE), dopar=DoparParam(stop.on.error=FALSE), batchjobs=BatchJobsParam(2, progressbar=FALSE, stop.on.error=FALSE)) for (param in params) { res <- bptry(bpvec(x, f, BPPARAM=param), bplist_error=identity) checkTrue(is(res, "bplist_error")) result <- attr(res, "result") checkIdentical(2L, length(result)) checkTrue(inherits(result[[1]], "condition")) closeAllConnections() Sys.sleep(0.25) ## data not fixed res2 <- bptry(bpvec(x, f, BPPARAM=param, BPREDO=result), bplist_error=identity) checkTrue(is(res2, "bplist_error")) result <- attr(res2, "result") checkIdentical(2L, length(result)) checkTrue(is(result[[1]], "remote_error")) closeAllConnections() Sys.sleep(0.25) ## data fixed res3 <- bpvec(x, sqrt, BPPARAM=param, BPREDO=result) checkIdentical(sqrt(x), res3) closeAllConnections() Sys.sleep(0.25) } ## clean up env <- foreach:::.foreachGlobals rm(list=ls(name=env), pos=env) closeAllConnections() } TRUE } test_bpiterate_errors <- function() { quiet <- suppressMessages .lazyCount <- function(count) { count <- count i <- 0L function() { if (i >= count) return(NULL) else i <<- i + 1L if (i == 2) "2" else i } } FUN <- function(count, ...) { if (count == 2) stop("hit error") else count } params <- list(snow=SnowParam(2, stop.on.error=FALSE)) if (.Platform$OS.type != "windows") params$mc <- MulticoreParam(2, stop.on.error=FALSE) for (p in params) { ITER <- .lazyCount(3) quiet(res <- bpiterate(ITER, FUN, BPPARAM=p)) checkTrue(is(res[[2]], "remote_error")) closeAllConnections() } ## clean up closeAllConnections() TRUE } BiocParallel/inst/unitTests/test_internal_rng_stream.R0000644000175200017520000000052214136050065024322 0ustar00biocbuildbiocbuildtest_internal_rng_stream <- function() { if (.Platform$OS.type == "windows") { PARAM <- SnowParam } else { PARAM <- MulticoreParam } set.seed(100); exp <- runif(10) ## no change when param created (port assignment) set.seed(100); p <- PARAM(2); obs <- runif(10) checkIdentical(exp, obs) } BiocParallel/inst/unitTests/test_ipcmutex.R0000644000175200017520000000115514136050065022126 0ustar00biocbuildbiocbuildtest_ipclock <- function() { id <- ipcid() on.exit(ipcremove(id)) result <- bplapply(1:5, function(i, id) { BiocParallel::ipclock(id) Sys.sleep(.1) time <- Sys.time() BiocParallel::ipcunlock(id) time }, id, BPPARAM=SnowParam(2)) d <- diff(range(unlist(result, use.names=FALSE))) checkTrue(d > 0.4) } test_ipccounter <- function() { id <- ipcid() on.exit(ipcremove(id)) result <- bplapply(1:5, function(i, id) { BiocParallel::ipcyield(id) }, id, BPPARAM=SnowParam(2)) checkIdentical(sort(unlist(result, use.names=FALSE)), 1:5) } BiocParallel/inst/unitTests/test_logging.R0000644000175200017520000000202114136050065021707 0ustar00biocbuildbiocbuild## This code tests 'log' and 'progressbar'. test_errorhandling.R ## tests 'stop.on.error' test_log <- function() { ## SnowParam, MulticoreParam only params <- list( snow=SnowParam(2, log=FALSE, stop.on.error=FALSE), snowLog=SnowParam(2, log=TRUE, stop.on.error=FALSE)) if (.Platform$OS.type != "windows") { params$multi=MulticoreParam(3, log=FALSE, stop.on.error=FALSE) params$multiLog=MulticoreParam(3, log=TRUE, stop.on.error=FALSE) } for (param in params) { res <- suppressMessages(tryCatch({ bplapply(list(1, "2", 3), sqrt, BPPARAM=param) }, error=identity)) checkTrue(is(res, "bplist_error")) result <- attr(res, "result") checkTrue(length(result) == 3L) msg <- "non-numeric argument to mathematical function" checkIdentical(conditionMessage(result[[2]]), msg) checkTrue(length(attr(result[[2]], "traceback")) > 0L) closeAllConnections() } ## clean up closeAllConnections() TRUE } BiocParallel/inst/unitTests/test_refclass.R0000644000175200017520000000040614136050065022070 0ustar00biocbuildbiocbuildtest_SnowParam_refclass <- function() { p <- SnowParam(2) p2 <- p checkTrue(!bpisup(p)) checkTrue(!bpisup(p2)) bpstart(p) checkTrue(bpisup(p)) checkTrue(bpisup(p2)) bpstop(p) checkTrue(!bpisup(p)) checkTrue(!bpisup(p2)) } BiocParallel/inst/unitTests/test_rng.R0000644000175200017520000002766714136050065021076 0ustar00biocbuildbiocbuildtest_rng_fun_advances_generator <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator .rng_job_fun_factory <- BiocParallel:::.rng_job_fun_factory .RNGstream <- BiocParallel:::.RNGstream state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) SEED <- .RNGstream(bpstart(SerialParam())) checkIdentical( ## independently invoked with same seed --> same result .rng_job_fun_factory(function(i) rnorm(i), SEED)(2), .rng_job_fun_factory(function(i) rnorm(i), SEED)(2) ) SEED <- .RNGstream(bpstart(SerialParam())) FUN <- .rng_job_fun_factory(function(i) rnorm(i), SEED) target <- FUN(2) # two numbers from same stream FUN <- .rng_job_fun_factory(function(i) rnorm(i), SEED) obs <- c(FUN(1), FUN(1)) # two numbers from separate streams checkIdentical(obs[[1]], target[[1]]) checkTrue(obs[[2]] != target[[2]]) checkTrue(identical(state, .rng_get_generator())) } test_rng_lapply <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator .RNGstream <- BiocParallel:::.RNGstream .rng_lapply <- BiocParallel:::.rng_lapply .rng_next_substream <- BiocParallel:::.rng_next_substream state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) SEED <- .RNGstream(bpstart(SerialParam())) checkIdentical( ## same sequence of random number streams .rng_lapply(1:2, function(i) rnorm(1), BPRNGSEED = SEED), .rng_lapply(1:2, function(i) rnorm(1), BPRNGSEED = SEED) ) SEED1 <- .RNGstream(bpstart(SerialParam())) SEED2 <- .rng_next_substream(SEED1) target <- .rng_lapply(1:2, function(i) rnorm(2), BPRNGSEED = SEED1) obs <- c( .rng_lapply(1, function(i) rnorm(2), BPRNGSEED = SEED1), .rng_lapply(1, function(i) rnorm(2), BPRNGSEED = SEED2) ) checkIdentical(target, obs) checkTrue(identical(state, .rng_get_generator())) } test_rng_bplapply <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) p1 <- SerialParam(RNGseed = 123) p2 <- SnowParam(3, RNGseed = 123) p3 <- SnowParam(5, RNGseed = 123) FUN <- function(i) rnorm(2) ## SerialParam / SnowParam same results target <- bplapply(1:11, FUN, BPPARAM = p1) checkIdentical(bplapply(1:11, FUN, BPPARAM = p2), target) ## SerialParam / SnowParam same results, different number of workers checkIdentical(bplapply(1:11, FUN, BPPARAM = p3), target) if (identical(.Platform$OS.type, "unix")) { ## SerialParam / TransientMulticoreParam same results p4a <- MulticoreParam(5, RNGseed = 123) checkIdentical(bplapply(1:11, FUN, BPPARAM = p4a), target) ## SerialParam / MulticoreParam same results p4b <- bpstart(MulticoreParam(5, RNGseed = 123)) checkIdentical(bplapply(1:11, FUN, BPPARAM = p4b), target) bpstop(p4b) } ## single worker coerced to SerialParam p5 <- SnowParam(1, RNGseed = 123) checkIdentical(bplapply(1:11, FUN, BPPARAM = p5), target, "p5") checkIdentical(state$kind, .rng_get_generator()$kind) } test_rng_bpiterate <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) FUN <- function(i) rnorm(2) ITER_factory <- function() { x <- 1:11 i <- 0L function() { i <<- i + 1L if (i > length(x)) return(NULL) x[[i]] } } p1 <- SerialParam(RNGseed = 123) p2 <- SnowParam(3, RNGseed = 123) p3 <- SnowParam(5, RNGseed = 123) target <- bplapply(1:11, FUN, BPPARAM = p1) checkIdentical(target, bpiterate(ITER_factory(), FUN, BPPARAM = p1), "p1") checkIdentical(target, bpiterate(ITER_factory(), FUN, BPPARAM = p2), "p2") checkIdentical(target, bpiterate(ITER_factory(), FUN, BPPARAM = p3), "p3") if (identical(.Platform$OS.type, "unix")) { ## SerialParam / TransientMulticoreParam same results p4a <- MulticoreParam(5, RNGseed = 123) checkIdentical( target, bpiterate(ITER_factory(), FUN, BPPARAM = p4a), "p4a" ) ## SerialParam / MulticoreParam same results p4b <- bpstart(MulticoreParam(5, RNGseed = 123)) checkIdentical( target, bpiterate(ITER_factory(), FUN, BPPARAM = p4b), "p4b" ) bpstop(p4b) } checkIdentical(state$kind, .rng_get_generator()$kind) } test_rng_bpstart <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator state <- .rng_get_generator() FUN <- function(i) rnorm(2) ITER_factory <- function() { x <- 1:11 i <- 0L function() { i <<- i + 1L if (i > length(x)) return(NULL) x[[i]] } } ## bplapply p0 <- bpstart(SerialParam()) # random seed result1 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) result2 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) checkTrue(!any(result1 %in% result2)) bpstart(bpstop(p0)) # different random seed result3 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) checkTrue(!any(result3 %in% result1)) p0 <- bpstart(SerialParam(RNGseed = 123)) # set seed result1 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) result2 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) checkTrue(!any(result1 %in% result2)) bpstart(bpstop(p0)) # reset seed, same stream result3 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) result4 <- unlist(bplapply(1:11, FUN, BPPARAM = p0)) checkIdentical(result3, result1) checkIdentical(result4, result2) ## bpiterate p0 <- bpstart(SerialParam()) # random seed result1 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) result2 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) checkTrue(!any(result1 %in% result2)) bpstart(bpstop(p0)) # different random seed result3 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) checkTrue(!any(result3 %in% result1)) p0 <- bpstart(SerialParam(RNGseed = 123)) # set seed result1 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) result2 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) checkTrue(!any(result1 %in% result2)) bpstart(bpstop(p0)) # reset seed, same stream result3 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) result4 <- unlist(bpiterate(ITER_factory(), FUN, BPPARAM = p0)) checkIdentical(result3, result1) checkIdentical(result4, result2) checkIdentical(state$kind, .rng_get_generator()$kind) } .test_rng_bpstart_does_not_iterate_rng_seed <- function(param) { .rng_get_generator <- BiocParallel:::.rng_get_generator state <- .rng_get_generator() set.seed(123L) target <- runif(1L) ## bpstart() should not increment the random number seed set.seed(123L) bpstart(param) checkIdentical(target, runif(1L)) bpstop(param) ## bplapply does not increment stream set.seed(123) result <- bplapply(1:3, runif, BPPARAM = param) checkIdentical(target, runif(1L)) ## bplapply uses internal stream set.seed(123) result <- bplapply(1:3, runif, BPPARAM = param) checkTrue(!identical(result, bplapply(1:3, runif, BPPARAM = param))) checkIdentical(target, runif(1L)) target1 <- lapply(1:3, runif) checkTrue(!identical(result, target1)) checkIdentical(state$kind, .rng_get_generator()$kind) } test_rng_bpstart_does_not_iterate_rng_seed <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator TEST_FUN <- .test_rng_bpstart_does_not_iterate_rng_seed state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) TEST_FUN(SerialParam()) TEST_FUN(SnowParam(2)) if (identical(.Platform$OS.type, "unix")) TEST_FUN(MulticoreParam(2)) } .test_rng_global_and_RNGseed_indepenent <- function(param_fun) { set.seed(123) target <- bplapply(1:3, runif, BPPARAM = param_fun()) current <- bplapply(1:3, runif, BPPARAM = param_fun(RNGseed = 123)) checkTrue(!identical(target, current)) } test_rng_global_and_RNGseed_independent <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator TEST_FUN <- .test_rng_global_and_RNGseed_indepenent state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) TEST_FUN(SerialParam) TEST_FUN(SnowParam) if (identical(.Platform$OS.type, "unix")) TEST_FUN(MulticoreParam) } .test_rng_bpredo_impl <- function(param) { FUN <- function(i) rnorm(1) target <- unlist(bplapply(1:11, FUN, BPPARAM = param)) FUN0 <- function(i) { if (identical(i, 7L)) { stop("i == 7") } else rnorm(1) } result <- bptry(bplapply(1:11, FUN0, BPPARAM = param)) checkIdentical(unlist(result[-7]), target[-7]) checkTrue(inherits(result[[7]], "remote_error")) FUN1 <- function(i) { if (identical(i, 7L)) { ## the random number stream should be in the same state as the ## first time through the loop, and rnorm(1) should return ## same result as FUN rnorm(1) } else { ## if this branch is used, then we are incorrectly updating ## already calculated elements -- '0' in the output would ## indicate this error 0 } } result <- unlist(bplapply(1:11, FUN1, BPREDO = result, BPPARAM = param)) checkIdentical(result, target) bpstart(param) target1 <- unlist(bplapply(1:11, FUN, BPPARAM = param)) target2 <- unlist(bplapply(1:11, FUN, BPPARAM = param)) target3 <- unlist(bplapply(1:11, FUN, BPPARAM = param)) bpstop(param) bpstart(param) result1 <- bptry(bplapply(1:11, FUN0, BPPARAM = param)) result1_redo1 <- unlist(bplapply(1:11, FUN1, BPREDO = result1, BPPARAM = param)) result2 <- unlist(bplapply(1:11, FUN, BPPARAM = param)) result1_redo2 <- unlist(bplapply(1:11, FUN1, BPREDO = result1, BPPARAM = param)) result3 <- unlist(bplapply(1:11, FUN, BPPARAM = param)) checkIdentical(target1, result1_redo1) checkIdentical(target1, result1_redo2) checkIdentical(target2, result2) checkIdentical(target3, result3) } test_rng_bpredo <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) param <- SerialParam(RNGseed = 123, stop.on.error = FALSE) .test_rng_bpredo_impl(param) if (identical(.Platform$OS.type, "unix")) { param <- MulticoreParam(3, RNGseed = 123, stop.on.error = FALSE) .test_rng_bpredo_impl(param) } } test_rng_fallback_SerialParam <- function() { .rng_get_generator <- BiocParallel:::.rng_get_generator .rng_reset_generator <- BiocParallel:::.rng_reset_generator state <- .rng_get_generator() on.exit(.rng_reset_generator(state$kind, state$seed)) FUN <- function(i) rnorm(1) param <- SerialParam(RNGseed = 123, stop.on.error = FALSE) target <- unlist(bplapply(1:2, FUN, BPPARAM = param)) param2 <- SnowParam(RNGseed = 123, workers = 1L) checkIdentical(unlist(bplapply(1:2, FUN, BPPARAM = param2)), target) bpstart(param2) checkIdentical(bplapply(1, FUN, BPPARAM = param2)[[1]], target[1]) checkIdentical(bplapply(1, FUN, BPPARAM = param2)[[1]], target[2]) bpstop(param2) } BiocParallel/inst/unitTests/test_script/0000755000175200017520000000000014136050065021447 5ustar00biocbuildbiocbuildBiocParallel/inst/unitTests/test_script/test-sge-template.tmpl0000644000175200017520000000131614136050065025712 0ustar00biocbuildbiocbuild#!/bin/bash ## The name of the job, can be anything, simply used when displaying the list of running jobs #$ -N <%= job.name %> ## Combining output/error messages into one file #$ -j y ## Giving the name of the output log file #$ -o <%= log.file %> ## One needs to tell the queue system to use the current directory as the working directory ## Or else the script may fail as it will execute in your top level home directory /home/username #$ -cwd ## Use environment variables #$ -V ## Use correct queue #$ -q all.q ## R settings module load R/3.4.3 ## Export value of DEBUGME environemnt var to worker export DEBUGME=<%= Sys.getenv("DEBUGME") %> Rscript -e 'batchtools::doJobCollection("<%= uri %>")' exit 0 BiocParallel/inst/unitTests/test_utilities.R0000644000175200017520000001733614136050065022313 0ustar00biocbuildbiocbuildtest_splitIndicies <- function() { .splitIndices <- BiocParallel:::.splitIndices checkIdentical(list(), .splitIndices(0, 0)) checkIdentical(list(), .splitIndices(0, 1)) checkIdentical(list(), .splitIndices(0, 2)) checkIdentical(list(1:4), .splitIndices(4, 0)) checkIdentical(list(1:4), .splitIndices(4, 1)) checkIdentical(list(1:2, 3:4), .splitIndices(4, 2)) checkIdentical(as.list(1:4), .splitIndices(4, 4)) checkIdentical(as.list(1:4), .splitIndices(4, 8)) checkIdentical(list(1:4, 5:7), .splitIndices(7, 2)) } test_splitX <- function() { .splitX <- BiocParallel:::.splitX checkIdentical(list(), .splitX(character(), 0, 0)) checkIdentical(list(), .splitX(character(), 1, 0)) checkIdentical(list(), .splitX(character(), 0, 1)) checkIdentical(list(), .splitX(character(), 1, 1)) X <- LETTERS[1:4] checkIdentical(list(X), .splitX(X, 0, 0)) checkIdentical(list(X), .splitX(X, 1, 0)) checkIdentical(list(X[1:2], X[3:4]), .splitX(X, 2, 0)) checkIdentical(as.list(X), .splitX(X, 4, 0)) checkIdentical(as.list(X), .splitX(X, 8, 0)) checkIdentical(list(X[1:2], X[3:4]), .splitX(X, 2, 0)) checkIdentical(list(X), .splitX(X, 2, 1)) checkIdentical(list(X[1:2], X[3:4]), .splitX(X, 2, 2)) checkIdentical(list(X[1], X[2:3], X[4]), .splitX(X, 2, 3)) checkIdentical(as.list(X), .splitX(X, 2, 4)) } test_splitX_with_redo <- function(){ .split_X_redo <- BiocParallel:::.split_X_redo .bploop_rng_iter <- BiocParallel:::.bploop_rng_iter N <- 6 X <- 1:N task_size <- 1L redo_index <- rep(TRUE, N) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical(splittedX, as.list(X)) task_size <- 4L redo_index <- rep(TRUE, N) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical(splittedX, list(1:4, 5:6)) task_size <- 1L redo_index <- c(TRUE, FALSE, TRUE, TRUE, TRUE, FALSE) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical( splittedX, list(1L, .bploop_rng_iter(1L), 3L, 4L, 5L) ) task_size <- 2L redo_index <- c(TRUE, FALSE, TRUE, TRUE, TRUE, FALSE) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical( splittedX, list(1L, .bploop_rng_iter(1L), 3L:4L, 5L) ) task_size <- 10L redo_index <- c(TRUE, FALSE, TRUE, TRUE, TRUE, FALSE) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical( splittedX, list(1L, .bploop_rng_iter(1L), 3L:5L) ) task_size <- 1L redo_index <- c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical( splittedX, list(.bploop_rng_iter(2L), 3L) ) task_size <- 1L redo_index <- c(FALSE, FALSE, TRUE, TRUE, TRUE, TRUE) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical( splittedX, list(.bploop_rng_iter(2L), 3L, 4L, 5L, 6L) ) task_size <- 3L redo_index <- c(FALSE, FALSE, TRUE, TRUE, TRUE, TRUE) n <- .split_X_redo(X[redo_index], redo_index, task_size) splittedX <- .split_X_redo(X[redo_index], redo_index, task_size, n) checkIdentical( splittedX, list(.bploop_rng_iter(2L), 3:5, 6L) ) } test_redo_index <- function() { .redo_index <- BiocParallel:::.redo_index err <- BiocParallel:::.error("") checkIdentical(logical(), .redo_index(list(), list())) checkIdentical(TRUE, .redo_index(list(1), list(err))) checkIdentical(c(FALSE, TRUE), .redo_index(list(1, "2"), list(1, err))) ## all need recalculating checkIdentical(c(TRUE, TRUE), .redo_index(list("1", "2"), list(err, err))) ## X can be a vector checkIdentical(c(FALSE, TRUE), .redo_index(1:2, list(1, err))) ## lengths differ checkException(.redo_index(list(1, 2), list(err)), silent=TRUE) ## no previous error checkException(.redo_index(list(1, 2), list(1, 2)), silent=TRUE) } test_rename <- function() { .rename <- BiocParallel:::.rename X <- list() Y <- character() Z <- list(X) W <- list(Y) checkIdentical(X, .rename(list(), X)) checkIdentical(X, .rename(list(), Y)) checkIdentical(X, .rename(list(), Z)) checkIdentical(X, .rename(list(), W)) names(X) <- names(Y) <- character() Z <- list(X) W <- list(Y) checkIdentical(X, .rename(list(), X)) checkIdentical(X, .rename(list(), Y)) checkIdentical(list(), .rename(list(), Z)) checkIdentical(list(), .rename(list(), W)) Z <- list(x = X) W <- list(x = Y) checkIdentical(list(x = 1), .rename(list(1), Z)) checkIdentical(list(x = 1), .rename(list(1), W)) X <- list(a = 1:2) exp0 <- vector("list", length(X)) checkIdentical(setNames(exp0, names(X)), .rename(exp0, X)) X <- list(c(a = 1)) exp0 <- vector("list", length(X)) checkIdentical(exp0, .rename(exp0, X)) Y <- c(x = "a") checkIdentical(Y, .rename(Y, Y)) X <- list(a = 1:2, b = 3:4) exp0 <- vector("list", length(X)) exp <- setNames(exp0, names(X)) checkIdentical(exp, .rename(exp0, X)) X <- list(c(a = 1)) exp0 <- vector("list", length(X)) checkIdentical(exp0, .rename(exp0, X)) X <- list(A = c(a = 1, b=2), B = c(c = 1, d = 2)) exp0 <- vector("list", length(X)) exp <- setNames(exp0, names(X)) checkIdentical(exp, .rename(exp0, X)) } test_mrename <- function() { .mrename <- BiocParallel:::.mrename ## list() when `mapply()` invoked with no arguments, `mapply(identity)` X <- list() checkIdentical(X, .mrename(list(), X)) ## list(X) when `mapply()` invoked with one argument, `mapply(identity, X)` XX <- list(X) checkIdentical(list(), .mrename(list(), XX)) ## `mapply(identity, character())` returns a _named_ list() X <- character() XX <- list(X) checkIdentical(setNames(list(), character()), .mrename(list(), XX)) ## named arguments to mapply() are _not_ names of return value... X = list() XX <- list(x = X) checkIdentical(list(), .mrename(list(), XX)) ## ...except if the argument is a character() X <- character() XX <- list(x = X) checkIdentical(setNames(list(), character()), .mrename(list(), XX)) ## with multiple arguments, names are from the first argument XX = list(c(a = 1, b = 2, c = 3), c(d = 4, e = 5, f = 6)) checkIdentical( setNames(list(1, 2, 3), letters[1:3]), .mrename(list(1, 2, 3), XX) ) ## ...independent of names on the arguments XX = list(A = c(a = 1, b = 2, c = 3), B = c(d = 4, e = 5, f = 6)) checkIdentical( setNames(list(1, 2, 3), letters[1:3]), .mrename(list(1, 2, 3), XX) ) ## when the first argument is an unnamed character vector, names are values XX = list(A = letters[1:3], B = 1:3) checkIdentical( setNames(list(1, 2, 3), letters[1:3]), .mrename(list(1, 2, 3), XX) ) ## ...except if there are names on the first vector... XX = list(A = setNames(letters[1:3], LETTERS[1:3]), B = 1:3) checkIdentical( setNames(list(1, 2, 3), LETTERS[1:3]), .mrename(list(1, 2, 3), XX) ) } BiocParallel/man/0000755000175200017520000000000014147652225014710 5ustar00biocbuildbiocbuildBiocParallel/man/BatchJobsParam-class.Rd0000644000175200017520000001330114136050065021110 0ustar00biocbuildbiocbuild\name{BatchJobsParam-class} \Rdversion{1.1} \docType{class} \alias{BatchJobsParam-class} \alias{BatchJobsParam} \alias{bpbackend,BatchJobsParam-method} \alias{bpbackend<-,BatchJobsParam} \alias{bpisup,BatchJobsParam-method} \alias{bpstart,BatchJobsParam-method} \alias{bpstop,BatchJobsParam-method} \alias{bpworkers,BatchJobsParam-method} \alias{bpschedule,BatchJobsParam-method} \alias{show,BatchJobsParam-method} \title{Enable parallelization on batch systems} \description{ This class is used to parameterize scheduler options on managed high-performance computing clusters. } \usage{ BatchJobsParam(workers, cleanup = TRUE, work.dir = getwd(), stop.on.error = TRUE, seed = NULL, resources = NULL, conffile = NULL, cluster.functions = NULL, progressbar = TRUE, jobname = "BPJOB", reg.pars=list(seed=seed, work.dir=work.dir), conf.pars=list(conffile=conffile, cluster.functions=cluster.functions), submit.pars=list(resources=resources), ...) } \arguments{ \item{workers}{\code{integer(1)}} Number of workers to divide tasks (e.g., elements in the first argument of \code{bplapply}) between. On Multicore and SSH backends, this defaults to all available nodes. On managed (e.g., slurm, SGE) clusters \code{workers} defaults to \code{NA}, meaning that the number of workers equals the number of tasks. See argument \code{n.chunks} in \code{\link[BBmisc]{chunk}} and \code{\link[BatchJobs]{submitJobs}} for more information. \item{cleanup}{\code{logical(1)}} BatchJobs creates temporary directories in the \code{work.dir}. If \code{cleanup} is set to \code{TRUE} (default), the directories are removed from the file systems automatically. Set this to \code{FALSE} whenever it might become necessary to utilize any special functionality provided by BatchJobs. To retrieve the registry, call \code{\link[BatchJobs]{loadRegistry}} on the temporary directory. \item{work.dir}{\code{character(1)}} Directory to store temporary files. Note that this must be shared across computational nodes if you use a distributed computing backend. Default ist the current working directory of \code{R}, see \code{\link[base]{getwd}}. Ignored when \code{reg.pars} is provided. \item{stop.on.error}{\code{logical(1)}} Stop all jobs as soon as one jobs fails (\code{stop.on.error == TRUE}) or wait for all jobs to terminate. Default is \code{TRUE}. \item{seed}{\code{integer(1L)}} Set an initial seed for the RNG. See \code{\link[BatchJobs]{makeRegistry}} for more information. Default is \code{NULL} where a random seed is chosen upon initialization. Ignored when \code{reg.pars} is provided. \item{resources}{\code{list()}} List of job specific resources passed to \code{\link[BatchJobs]{submitJobs}}. Default is \code{NULL} where the resources defined in the configuration are used. Ignored when \code{submit.pars} is provided. \item{conffile}{\code{character(1)}} URI to a custom BatchJobs configuration file used for execution. Default is \code{NULL} which relies on BatchJobs to handle configuration files. Ignored when \code{conf.pars} is provided. \item{cluster.functions}{ClusterFunctions} Specify a specific cluster backend using on of the constructors provided by BatchJobs, see \link[BatchJobs:makeClusterFunctions]{ClusterFunctions}. Default is \code{NULL} where the default cluster functions defined in the configuration are used. Ignored when \code{conf.pars} is provided. \item{progressbar}{\code{logical(1)}} Suppress the progress bar used in BatchJobs and be less verbose. Default is \code{FALSE}. \item{jobname}{\code{character(1)}} Job name that is prepended to the output log and result files. Default is "BPJOB". \item{reg.pars}{\code{list()}} List of parameters passed to \code{BatchJobs::makeRegistry()}. When present, user-supplied arguments \code{seed} and \code{work.dir} to \code{BatchJobsParam} are ignored. \item{conf.pars}{\code{list()}} List of parameters passed to \code{BatchJobs::setConfig()}. When present, user-supplied arguments \code{conffile}, \code{cluster.functions} to \code{BatchJobsParam} are ignored. \item{submit.pars}{\code{list()}} List of parameters passed to \code{BatchJobs::submitJobs}. When present, user-supplied argument \code{resources} to \code{BatchJobsParam} is ignored. \code{submitJobs} parameters \code{reg}, \code{id} cannot be set. \item{\dots}{Addition arguments, currently not handled.} } \section{BatchJobsParam constructor}{ Return an object with specified values. The object may be saved to disk or reused within a session. } \section{Methods}{ The following generics are implemented and perform as documented on the corresponding help page: \code{\link{bpworkers}}, \code{\link{bpnworkers}}, \code{\link{bpstart}}, \code{\link{bpstop}}, \code{\link{bpisup}}, \code{\link{bpbackend}}, \code{\link{bpbackend<-}} } \author{Michel Lang, \url{mailto:michellang@gmail.com}} \seealso{ \code{getClass("BiocParallelParam")} for additional parameter classes. \code{register} for registering parameter classes for use in parallel evaluation. } \examples{ p <- BatchJobsParam(progressbar=FALSE) bplapply(1:10, sqrt, BPPARAM=p) \dontrun{ ## see vignette for additional explanation funs <- makeClusterFunctionsSLURM("~/slurm.tmpl") param <- BatchJobsParam(4, cluster.functions=funs) register(param) bplapply(1:10, function(i) sqrt) } } BiocParallel/man/BatchtoolsParam-class.Rd0000644000175200017520000002137014136050065021360 0ustar00biocbuildbiocbuild\name{BatchtoolsParam-class} \Rdversion{1.1} \docType{class} \alias{BatchtoolsParam-class} \alias{BatchtoolsParam} \alias{bpRNGseed,BatchtoolsParam-method} \alias{bpRNGseed<-,BatchtoolsParam,numeric-method} \alias{bpbackend,BatchtoolsParam-method} \alias{bpisup,BatchtoolsParam-method} \alias{bplapply,ANY,BatchtoolsParam-method} \alias{bplogdir,BatchtoolsParam-method} \alias{bplogdir<-,BatchtoolsParam,character-method} \alias{bpschedule,BatchtoolsParam-method} \alias{bpstart,BatchtoolsParam-method} \alias{bpstop,BatchtoolsParam-method} \alias{bpworkers,BatchtoolsParam-method} \alias{show,BatchtoolsParam-method} \alias{batchtoolsWorkers} \alias{batchtoolsCluster} \alias{batchtoolsTemplate} \alias{batchtoolsRegistryargs} \title{Enable parallelization on batch systems} \description{ This class is used to parameterize scheduler options on managed high-performance computing clusters using batchtools. \code{BatchtoolsParam()}: Construct a BatchtoolsParam-class object. \code{batchtoolsWorkers()}: Return the default number of workers for each backend. \code{batchtoolsTemplate()}: Return the default template for each backend. \code{batchtoolsCluster()}: Return the default cluster. \code{batchtoolsRegistryargs()}: Create a list of arguments to be used in batchtools' \code{makeRegistry}; see \code{registryargs} argument. } \usage{ BatchtoolsParam( workers = batchtoolsWorkers(cluster), cluster = batchtoolsCluster(), registryargs = batchtoolsRegistryargs(), saveregistry = FALSE, resources = list(), template = batchtoolsTemplate(cluster), stop.on.error = TRUE, progressbar = FALSE, RNGseed = NA_integer_, timeout = 30L * 24L * 60L * 60L, exportglobals=TRUE, log = FALSE, logdir = NA_character_, resultdir=NA_character_, jobname = "BPJOB" ) batchtoolsWorkers(cluster = batchtoolsCluster()) batchtoolsCluster(cluster) batchtoolsTemplate(cluster) batchtoolsRegistryargs(...) } \arguments{ \item{workers}{\code{integer(1)}} Number of workers to divide tasks (e.g., elements in the first argument of \code{bplapply}) between. On 'multicore' and 'socket' backends, this defaults to \code{multicoreWorkers()} and \code{snowWorkers()}. On managed (e.g., slurm, SGE) clusters \code{workers} has no default, meaning that the number of workers needs to be provided by the user. \item{cluster}{\code{character(1)}} Cluster type being used as the backend by \code{BatchtoolsParam}. The available options are "socket", "multicore", "interactive", "sge", "slurm", "lsf", "torque" and "openlava". The cluster type if available on the machine registers as the backend. Cluster types which need a \code{template} are "sge", "slurm", "lsf", "openlava", and "torque". If the template is not given then a default is selected from the \code{batchtools} package. \item{registryargs}{\code{list()}} Arguments given to the registry created by \code{BatchtoolsParam} to configure the registry and where it's being stored. The \code{registryargs} can be specified by the function \code{batchtoolsRegistryargs()} which takes the arguments \code{file.dir}, \code{work.dir}, \code{packages}, \code{namespaces}, \code{source}, \code{load}, \code{make.default}. It's useful to configure these option, especially the \code{file.dir} to a location which is accessible to all the nodes on your job scheduler i.e master and workers. \code{file.dir} uses a default setting to make a registry in your working directory. \item{saveregistry}{\code{logical(1)}} Option given to store the entire registry for the job(s). This functionality should only be used when debugging. The storage of the entire registry can be time and space expensive on disk. The registry will be saved in the directory specified by \code{file.dir} in \code{registryargs}; the default locatoin is the current working directory. The saved registry directories will have suffix "-1", "-2" and so on, for each time the \code{BatchtoolsParam} is used. \item{resources}{\code{named list()}} Arguments passed to the \code{resources} argument of \code{batchtools::submitJobs} during evaluation of \code{bplapply} and similar functions. These name-value pairs are used for substitution into the template file. \item{template}{\code{character(1)}} Path to a template for the backend in \code{BatchtoolsParam}. It is possible to check which template is being used by the object using the getter \code{bpbackend(BatchtoolsParam())}. The template needs to be written specific to each backend. Please check the list of available templates in the \code{batchtools} package. \item{stop.on.error}{\code{logical(1)}} Stop all jobs as soon as one jobs fails (\code{stop.on.error == TRUE}) or wait for all jobs to terminate. Default is \code{TRUE}. \item{progressbar}{\code{logical(1)}} Suppress the progress bar used in BatchtoolsParam and be less verbose. Default is \code{FALSE}. \item{RNGseed}{\code{integer(1)}} Set an initial seed for the RNG. Default is \code{NULL} where a random seed is chosen upon initialization. \item{timeout}{\code{list()}} Time (in seconds) allowed for worker to complete a task. If the computation exceeds \code{timeout} an error is thrown with message 'reached elapsed time limit'. \item{exportglobals}{\code{logical(1)}} Export \code{base::options()} from manager to workers? Default \code{TRUE}. \item{log}{\code{logical(1)}} Option given to save the logs which are produced by the jobs. If \code{log=TRUE} then the \code{logdir} option must be specified. \item{logdir}{\code{character(1)}} Path to location where logs are stored. The argument \code{log=TRUE} is required before using the logdir option. \item{resultdir}{\code{logical(1)}} Path where results are stored. \item{jobname}{\code{character(1)}} Job name that is prepended to the output log and result files. Default is "BPJOB". \item{\dots}{name-value pairs} Names and values correspond to arguments from batchtools \code{\link[batchtools]{makeRegistry}}. } \section{BatchtoolsParam constructor}{ Return an object with specified values. The object may be saved to disk or reused within a session. } \section{Methods}{ The following generics are implemented and perform as documented on the corresponding help page: \code{\link{bpworkers}}, \code{\link{bpnworkers}}, \code{\link{bpstart}}, \code{\link{bpstop}}, \code{\link{bpisup}}, \code{\link{bpbackend}}. \code{\link{bplapply}} handles arguments \code{X} of classes derived from \code{S4Vectors::List} specially, coercing to \code{list}. } \author{Nitesh Turaga, \url{mailto:nitesh.turaga@roswellpark.org}} \seealso{ \code{getClass("BiocParallelParam")} for additional parameter classes. \code{register} for registering parameter classes for use in parallel evaluation. The batchtools package. } \examples{ ## Pi approximation piApprox = function(n) { nums = matrix(runif(2 * n), ncol = 2) d = sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } piApprox(1000) ## Calculate piApprox 10 times param <- BatchtoolsParam() result <- bplapply(rep(10e5, 10), piApprox, BPPARAM=param) \dontrun{ ## see vignette for additional explanation library(BiocParallel) param = BatchtoolsParam(workers=5, cluster="sge", template="script/test-sge-template.tmpl") ## Run parallel job result = bplapply(rep(10e5, 100), piApprox, BPPARAM=param) ## bpmapply param = BatchtoolsParam() result = bpmapply(fun, x = 1:3, y = 1:3, MoreArgs = list(z = 1), SIMPLIFY = TRUE, BPPARAM = param) ## bpvec param = BatchtoolsParam(workers=2) result = bpvec(1:10, seq_along, BPPARAM=param) ## bpvectorize param = BatchtoolsParam(workers=2) ## this returns a function bpseq_along = bpvectorize(seq_along, BPPARAM=param) result = bpseq_along(1:10) ## bpiterate ITER <- function(n=5) { i <- 0L function() { i <<- i + 1L if (i > n) return(NULL) rep(i, n) } } param <- BatchtoolsParam() res <- bpiterate(ITER=ITER(), FUN=function(x,y) sum(x) + y, y=10, BPPARAM=param) ## save logs logdir <- tempfile() dir.create(logdir) param <- BatchtoolsParam(log=TRUE, logdir=logdir) res <- bplapply(rep(10e5, 10), piApprox, BPPARAM=param) ## save registry (should be used only for debugging) file.dir <- tempfile() registryargs <- batchtoolsRegistryargs(file.dir = file.dir) param <- BatchtoolsParam(saveregistry = TRUE, registryargs = registryargs) res <- bplapply(rep(10e5, 10), piApprox, BPPARAM=param) dir(dirname(file.dir), basename(file.dir)) } } BiocParallel/man/BiocParallel-package.Rd0000644000175200017520000000074414136050065021116 0ustar00biocbuildbiocbuild\name{BiocParallel-package} \alias{BiocParallel-package} \alias{BiocParallel} \docType{package} \title{Bioconductor facilities for parallel evaluation} \description{ This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. } \details{ This package uses code from the \code{\link{parallel}} package, } \author{ See \code{packageDescription("BiocParallel")}. } \keyword{package} BiocParallel/man/BiocParallelParam-class.Rd0000644000175200017520000002547314136050065021617 0ustar00biocbuildbiocbuild\name{BiocParallelParam-class} \Rdversion{1.1} \docType{class} % Class \alias{BiocParallelParam-class} \alias{BiocParallelParam} % Control \alias{bpbackend} \alias{bpbackend<-} \alias{bpbackend,missing-method} \alias{bpbackend<-,missing,ANY-method} \alias{bpisup} \alias{bpisup,ANY-method} \alias{bpisup,missing-method} \alias{bpstart} \alias{bpstart,ANY-method} \alias{bpstart,missing-method} \alias{bpstart,BiocParallelParam-method} \alias{bpstop} \alias{bpstop,ANY-method} \alias{bpstop,missing-method} \alias{bpstop,BiocParallelParam-method} \alias{bpnworkers} \alias{bpworkers} \alias{bpworkers<-} \alias{bpworkers,missing-method} \alias{bpworkers,BiocParallelParam-method} \alias{bptasks} \alias{bptasks,BiocParallelParam-method} \alias{bptasks<-} \alias{bptasks<-,BiocParallelParam,numeric-method} \alias{bpstopOnError} \alias{bpstopOnError,BiocParallelParam-method} \alias{bpstopOnError<-} \alias{bpstopOnError<-,BiocParallelParam,logical-method} \alias{bpstopOnError<-,DoparParam,logical-method} \alias{bplog} \alias{bplog<-} \alias{bplog,BiocParallelParam-method} \alias{bpthreshold} \alias{bpthreshold<-} \alias{bpthreshold,BiocParallelParam-method} \alias{bplogdir} \alias{bplogdir<-} \alias{bplogdir,BiocParallelParam-method} \alias{bplogdir<-,BiocParallelParam,character-method} \alias{bpresultdir} \alias{bpresultdir<-} \alias{bpresultdir,BiocParallelParam-method} \alias{bpresultdir<-,BiocParallelParam,character-method} \alias{bptimeout} \alias{bptimeout<-} \alias{bptimeout,BiocParallelParam-method} \alias{bptimeout<-,BiocParallelParam,numeric-method} \alias{bpexportglobals} \alias{bpexportglobals<-} \alias{bpexportglobals,BiocParallelParam-method} \alias{bpexportglobals<-,BiocParallelParam,logical-method} \alias{bpprogressbar} \alias{bpprogressbar,BiocParallelParam-method} \alias{bpprogressbar<-} \alias{bpprogressbar<-,BiocParallelParam,logical-method} \alias{bpjobname} \alias{bpjobname,BiocParallelParam-method} \alias{bpjobname<-} \alias{bpjobname<-,BiocParallelParam,character-method} \alias{bpRNGseed} \alias{bpRNGseed<-} \alias{bpRNGseed,BiocParallelParam-method} \alias{bpRNGseed<-,BiocParallelParam,NULL-method} \alias{bpRNGseed<-,BiocParallelParam,numeric-method} \alias{bpforceGC} \alias{bpforceGC,BiocParallelParam-method} \alias{bpforceGC<-} \alias{bpforceGC<-,BiocParallelParam,numeric-method} % Other methods \alias{show,BiocParallel-method} \alias{print.remote_error} \title{BiocParallelParam objects} \description{ The \code{BiocParallelParam} virtual class stores configuration parameters for parallel execution. Concrete subclasses include \code{SnowParam}, \code{MulticoreParam}, \code{BatchtoolsParam}, and \code{DoparParam} and \code{SerialParam}. } \details{ \code{BiocParallelParam} is the virtual base class on which other parameter objects build. There are 5 concrete subclasses: \describe{ \item{}{\code{SnowParam}: distributed memory computing} \item{}{\code{MulticoreParam}: shared memory computing} \item{}{\code{BatchtoolsParam}: scheduled cluster computing} \item{}{\code{DoparParam}: foreach computing} \item{}{\code{SerialParam}: non-parallel execution} } The parameter objects hold configuration parameters related to the method of parallel execution such as shared memory, independent memory or computing with a cluster scheduler. } \section{Construction}{ The \code{BiocParallelParam} class is virtual and has no constructor. Instances of the subclasses can be created with the following: \itemize{ \item \code{SnowParam()} \item \code{MulticoreParam()} \item \code{BatchtoolsParam()} \item \code{DoparParam()} \item \code{SerialParam()} } } \section{Accessors}{ \subsection{Back-end control}{ In the code below \code{BPPARAM} is a \code{BiocParallelParam} object. \describe{ \item{}{ \code{bpworkers(x)}, \code{bpworkers(x, ...)}: \code{integer(1)} or \code{character()}. Gets the number or names of the back-end workers. The setter is supported for SnowParam and MulticoreParam only. } \item{}{ \code{bpnworkers(x)}: \code{integer(1)}. Gets the number of the back-end workers. } \item{}{ \code{bptasks(x)}, \code{bptasks(x) <- value}: \code{integer(1)}. Get or set the number of tasks for a job. \code{value} must be a scalar integer >= 0L. This argument applies to \code{SnowParam} and \code{MulticoreParam} only; \code{DoparParam} and \code{BatchtoolsParam} have their own approach to dividing a job among workers. We define a job as a single call to a function such as \code{bplapply}, \code{bpmapply} etc. A task is the division of the \code{X} argument into chunks. When \code{tasks == 0} (default), \code{X} is divided by the number of workers. This approach distributes \code{X} in (approximately) equal chunks. A \code{tasks} value of > 0 dictates the total number of tasks. Values can range from 1 (all of \code{X} to a single worker) to the length of \code{X} (each element of \code{X} to a different worker); values greater than \code{length(X)} (e.g., \code{.Machine$integer.max}) are rounded to \code{length(X)}. When the length of \code{X} is less than the number of workers each element of \code{X} is sent to a worker and \code{tasks} is ignored. Another case where the \code{tasks} value is ignored is when using the \code{bpiterate} function; the number of tasks are defined by the number of data chunks returned by the \code{ITER} function. } \item{}{ \code{bpstart(x)}: \code{logical(1)}. Starts the back-end, if necessary. } \item{}{ \code{bpstop(x)}: \code{logical(1)}. Stops the back-end, if necessary and possible. } \item{}{ \code{bpisup(x)}: \code{logical(1)}. Tests whether the back-end is available for processing, returning a scalar logical value. \code{bp*} functions such as \code{bplapply} automatically start the back-end if necessary. } \item{}{ \code{bpbackend(x)}, \code{bpbackend(x) <- value}: Gets or sets the parallel \code{bpbackend}. Not all back-ends can be retrieved; see \code{methods("bpbackend")}. } \item{}{ \code{bplog(x)}, \code{bplog(x) <- value}: Get or enable logging, if available. \code{value} must be a \code{logical(1)}. } \item{}{ \code{bpthreshold(x)}, \code{bpthreshold(x) <- value}: Get or set the logging threshold. \code{value} must be a \code{character(1)} string of one of the levels defined in the \code{futile.logger} package: \dQuote{TRACE}, \dQuote{DEBUG}, \dQuote{INFO}, \dQuote{WARN}, \dQuote{ERROR}, or \dQuote{FATAL}. } \item{}{ \code{bplogdir(x)}, \code{bplogdir(x) <- value}: Get or set an optional directory for saving log files. The directory must already exist with read / write ability. } \item{}{ \code{bpresultdir(x)}, \code{bpresultdir(x) <- value}: Get or set an optional directory for saving results as 'rda' files. The directory must already exist with read / write ability. } \item{}{ \code{bptimeout(x)}, \code{bptimeout(x) <- value}: \code{numeric(1)} Time (in seconds) allowed for worker to complete a task. This value is passed to base::setTimeLimit() as both the \code{cpu} and \code{elapsed} arguments. If the computation exceeds \code{timeout} an error is thrown with message 'reached elapsed time limit'. } \item{}{ \code{bpexportglobals(x)}, \code{bpexportglobals(x) <- value}: \code{logical(1)} Export \code{base::options()} from manager to workers? Default \code{TRUE}. } \item{}{ \code{bpprogressbar(x)}, \code{bpprogressbar(x) <- value}: Get or set the value to enable text progress bar. \code{value} must be a \code{logical(1)}. } \item{}{ \code{bpRNGseed(x)}, \code{bpRNGseed(x) <- value}: Get or set the seed for random number generaton. \code{value} must be a \code{numeric(1)} or \code{NULL}. } \item{}{ \code{bpjobname(x)}, \code{bpjobname(x) <- value}: Get or set the job name. } \item{}{ \code{bpforceGC(x)}, \code{bpforceGC(x) <- value}: Get or set whether 'garbage collection' should be invoked at the end of each call to \code{FUN}. } } } \subsection{Error Handling}{ In the code below \code{BPPARAM} is a \code{BiocParallelParam} object. \describe{ \item{}{ \code{bpstopOnError(x)}, \code{bpstopOnError(x) <- value}: \code{logical()}. Controls if the job stops when an error is hit. \code{stop.on.error} controls whether the job stops after an error is thrown. When \code{TRUE}, the output contains all successfully completed results up to and including the error. When \code{stop.on.error == TRUE} all computations stop once the error is hit. When \code{FALSE}, the job runs to completion and successful results are returned along with any error messages. } } } } \section{Methods}{ \subsection{Evaluation}{ In the code below \code{BPPARAM} is a \code{BiocParallelParam} object. Full documentation for these functions are on separate man pages: see ?\code{bpmapply}, ?\code{bplapply}, ?\code{bpvec}, ?\code{bpiterate} and ?\code{bpaggregate}. \describe{ \item{}{ \code{bpmapply(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPPARAM=bpparam())} } \item{}{ \code{bplapply(X, FUN, ..., BPPARAM=bpparam())} } \item{}{ \code{bpvec(X, FUN, ..., AGGREGATE=c, BPPARAM=bpparam())} } \item{}{ \code{bpiterate(ITER, FUN, ..., BPPARAM=bpparam())} } \item{}{ \code{bpaggregate(x, data, FUN, ..., BPPARAM=bpparam())} } } } \subsection{Other}{ In the code below \code{BPPARAM} is a \code{BiocParallelParam} object. \describe{ \item{}{ \code{show(x)} } } } } \author{Martin Morgan and Valerie Obenchain.} \seealso{ \itemize{ \item \code{\link{SnowParam}} for computing in distributed memory \item \code{\link{MulticoreParam}} for computing in shared memory \item \code{\link{BatchtoolsParam}} for computing with cluster schedulers \item \code{\link{DoparParam}} for computing with foreach \item \code{\link{SerialParam}} for non-parallel execution } } \examples{ getClass("BiocParallelParam") ## For examples see ?SnowParam, ?MulticoreParam, ?BatchtoolsParam ## and ?SerialParam. } \keyword{classes} \keyword{methods} BiocParallel/man/DeveloperInterface.Rd0000644000175200017520000001377014136050065020745 0ustar00biocbuildbiocbuild\name{DeveloperInterface} \alias{.BiocParallelParam_prototype} \alias{.prototype_update} \alias{.recv_all} \alias{.recv_all,ANY-method} \alias{.recv_any} \alias{.recv_any,ANY-method} \alias{.recv_any,SerialBackend-method} \alias{.send_all} \alias{.send_all,ANY-method} \alias{.send_to} \alias{.send_to,ANY-method} \alias{.send_to,SerialBackend-method} \alias{.send} \alias{.send,ANY-method} \alias{.recv} \alias{.recv,ANY-method} \alias{.close} \alias{.close,ANY-method} \alias{.bpstart_impl} \alias{.bpstop_impl} \alias{.bpworker_impl} \alias{.bplapply_impl} \alias{.bpiterate_impl} \title{Developer interface} \description{ Functions documented on this page are meant for developers wishing to implement \code{BPPARAM} objects that extend the \code{BiocParallelParam} virtual class to support additional parallel back-ends. } \usage{ ## class extension .prototype_update(prototype, ...) ## manager interface .send_to(backend, node, value) .recv_any(backend) .send_all(backend, value) .recv_all(backend) ## worker interface .send(worker, value) .recv(worker) .close(worker) ## supporting implementations .bpstart_impl(x) .bpworker_impl(worker) .bplapply_impl(X, FUN, ..., BPREDO = list(), BPPARAM = bpparam()) .bpiterate_impl(ITER, FUN, ..., REDUCE, init, reduce.in.order = FALSE, BPPARAM = bpparam()) .bpstop_impl(x) } \arguments{ \item{prototype}{ A named \code{list} of default values for reference class fields. } \item{x}{ A \code{BPPARAM} instance. } \item{backend}{ An object containing information about the cluster, returned by \code{bpbackend()}. } \item{worker}{ The object to which the worker communicates via \code{.send} and \code{.recv}. \code{.close} terminates the worker. } \item{node}{ An integer value indicating the node in the backend to which values are to be sent or received. } \item{value}{ Any R object, to be sent to or from workers. } \item{X, ITER, FUN, REDUCE, init, reduce.in.order, BPREDO, BPPARAM}{ See \code{bplapply} and \code{bpiterate}. } \item{\ldots}{ For \code{.prototype_update()}, name-value pairs to initialize derived and base class fields. For \code{.bplapply_impl()}, \code{.bpiterate_impl()}, additional arguments to \code{FUN()}; see \code{bplapply} and \code{bpiterate}. } } \details{ Start a BPPARM implementation by creating a reference class, e.g., extending the virtual class \code{BiocParallelParam}. Because of idiosyncracies in reference class field initialization, an instance of the class should be created by calling the generator returned by \code{setRefClass()} with a list of key-value pairs providing default parameteter arguments. The default values for the \code{BiocParallelParam} base class is provided in a list \code{.BiocParallelParam_prototype}, and the function \code{.prototype_update()} updates a prototype with new values, typically provided by the user. See the example below. BPPARAM implementations need to implement \code{bpstart()} and \code{bpstop()} methods; they may also need to implement, \code{bplapply()} and \code{bpiterate()} methods. Each method usually performs implementation-specific functionality before calling the next (BiocParallelParam) method. To avoid the intricacies of multiple dispatch, the bodies of BiocParallelParam methods are available for direct use as exported symbols. \itemize{ \item \code{bpstart,BiocParallelParam-method} (\code{.bpstart_impl()}) initiates logging, random number generation, and registration of finalizers to ensure that started clusters are stopped. \item \code{bpstop,BiocParallelParam-method} (\code{.bpstop_impl()}) ensures appropriate clean-up of stopped clusters, including sending the DONE semaphore. \code{bpstart()} will usually arrange for workers to enter \code{.bpworker_impl()} to listen for and evaluate tasks. \item \code{bplapply,ANY,BiocParallelParam-method} and \code{bpiterate,ANY,BiocParallelParam-method} (\code{.bplapply_impl()}, \code{.bpiterate_impl()}) implement: serial evaluation when there is a single core or task available; \code{BPREDO} functionality, and parallel lapply-like or iterative calculation. } Invoke \code{.bpstart_impl()}, \code{.bpstop_impl()}, \code{.bplapply_impl()}, and \code{.bpiterate_impl()} after any BPPARAM-specific implementation details. New implementations will also implement \code{bpisup()} and \code{bpbackend()} / \code{bpbackend<-()}; there are no default methods. The \emph{backends} (object returned by \code{bpbackend()}) of new BPPARAM implementations must support \code{length()} (number of nodes). In addition, the backends must support \code{.send_to()} and \code{.recv_any()} manager and \code{.send()}, \code{.recv()}, and \code{.close()} worker methods. Default \code{.send_all()} and \code{.recv_all()} methods are implemented as simple iterations along the \code{length(cluster)}, invoking \code{.send_to()} or \code{.recv_any()} on each iteration. } \value{ The return value of \code{.prototype_update()} is a list with elements in \code{prototype} substituted with key-value pairs provided in \code{\ldots}. All \code{send*} and \code{recv*} functions are endomorphic, returning a \code{cluster} object. } \examples{ \donttest{ ## ## Extend BiocParallelParam; `.A()` is not meant for the end user ## .A <- setRefClass( "A", contains = "BiocParallelParam", fields = list(id = "character") ) ## Use a prototype for default values, including the prototype for ## inheritted fields .A_prototype <- c( list(id = "default_id"), .BiocParallelParam_prototype ) ## Provide a constructor for the user A <- function(...) { prototype <- .prototype_update(.A_prototype, ...) do.call(.A, prototype) } ## Provide an R function for field access bpid <- function(x) x$id ## Create and use an instance, overwriting default values bpid(A()) a <- A(id = "my_id", threshold = "WARN") bpid(a) bpthreshold(a) } } BiocParallel/man/DoparParam-class.Rd0000644000175200017520000000503514136050065020323 0ustar00biocbuildbiocbuild\name{DoparParam-class} \Rdversion{1.1} \docType{class} \alias{DoparParam-class} \alias{DoparParam} \alias{coerce,SOCKcluster,DoparParam-method} \alias{bpbackend,DoparParam-method} \alias{bpbackend<-,DoparParam,SOCKcluster-method} \alias{bpisup,DoparParam-method} \alias{bpstart,DoparParam-method} \alias{bpstop,DoparParam-method} \alias{bpworkers,DoparParam-method} \alias{show,DoparParam-method} \title{Enable parallel evaluation using registered dopar backend} \description{ This class is used to dispatch parallel operations to the dopar backend registered with the foreach package. } \usage{ DoparParam(stop.on.error=TRUE) } \details{ \code{DoparParam} can be used for shared or non-shared memory computing depending on what backend is loaded. The \code{doSNOW} package supports non-shared memory, \code{doParallel} supports both shared and non-shared. When not specified, the default number of workers in \code{DoparParam} is determined by \code{getDoParWorkers()}. See the \code{foreach} package vignette for details using the different backends: \url{http://cran.r-project.org/web/packages/foreach/vignettes/foreach.pdf} } \arguments{ \item{stop.on.error}{\code{logical(1)}} Stop all jobs as soon as one jobs fails (\code{stop.on.error == TRUE}) or wait for all jobs to terminate. Default is \code{TRUE}. } \section{DoparParam constructor}{ Return a proxy object that dispatches parallel evaluation to the registered foreach parallel backend. There are no options to the constructor. All configuration should be done through the normal interface to the foreach parallel backends. } \section{Methods}{ The following generics are implemented and perform as documented on the corresponding help page (e.g., \code{?bpisup}): \code{\link{bpworkers}}, \code{\link{bpnworkers}}, \code{\link{bpstart}}, \code{\link{bpstop}}, \code{\link{bpisup}}, \code{\link{bpbackend}}, \code{\link{bpbackend<-}}, \code{\link{bpvec}}. } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \seealso{ \code{getClass("BiocParallelParam")} for additional parameter classes. \code{register} for registering parameter classes for use in parallel evaluation. \code{foreach-package} for the parallel backend infrastructure used by this param class. } \examples{ \dontrun{ # First register a parallel backend with foreach library(doParallel) registerDoParallel(2) p <- DoparParam() bplapply(1:10, sqrt, BPPARAM=p) bpvec(1:10, sqrt, BPPARAM=p) register(DoparParam(), default=TRUE) } } \keyword{classes} BiocParallel/man/MulticoreParam-class.Rd0000644000175200017520000004530714136050065021227 0ustar00biocbuildbiocbuild\name{MulticoreParam-class} \Rdversion{1.1} \docType{class} \alias{MulticoreParam} \alias{MulticoreParam-class} \alias{multicoreWorkers} \alias{bpisup,MulticoreParam-method} \alias{bpschedule,MulticoreParam-method} \alias{bpworkers<-,MulticoreParam,numeric-method} \alias{show,MulticoreParam-method} %% implementation detail \alias{.close,TransientMulticoreParam-method} \alias{.recv,TransientMulticoreParam-method} \alias{.recv_all,TransientMulticoreParam-method} \alias{.recv_any,TransientMulticoreParam-method} \alias{.send,TransientMulticoreParam-method} \alias{.send_to,TransientMulticoreParam-method} \alias{bpbackend,TransientMulticoreParam-method} \alias{bpstart,TransientMulticoreParam-method} \alias{bpstop,TransientMulticoreParam-method} \alias{length,TransientMulticoreParam-method} \title{Enable multi-core parallel evaluation} \description{ This class is used to parameterize single computer multicore parallel evaluation on non-Windows computers. \code{multicoreWorkers()} chooses the number of workers. } \usage{ ## constructor ## ------------------------------------ MulticoreParam(workers = multicoreWorkers(), tasks = 0L, stop.on.error = TRUE, progressbar = FALSE, RNGseed = NULL, timeout = 30L * 24L * 60L * 60L, exportglobals=TRUE, log = FALSE, threshold = "INFO", logdir = NA_character_, resultdir = NA_character_, jobname = "BPJOB", force.GC = TRUE, manager.hostname = NA_character_, manager.port = NA_integer_, ...) ## detect workers ## ------------------------------------ multicoreWorkers() } \details{ \code{MulticoreParam} is used for shared memory computing. Under the hood the cluster is created with \code{makeCluster(..., type ="FORK")} from the \code{parallel} package. The default number of workers is determined by \code{multicoreWorkers()}. On windows, the number of multicore workers is always 1. Otherwise, the default is determined by an environment variable \code{R_PARALLELLY_AVAILABLECORES_FALLBACK} (see the parallely package) or \code{BIOCPARALLEL_WORKER_NUMBER} or, if those are not set, the maximum of 1 and \code{parallel::detectCores() - 2}. Machines with 3 or fewer cores, or machines where number of cores cannot be determined, are assigned a single worker. Machines with more than 127 cores are limited to the number of \emph{R} connections available when the workers start; this is 128 (a hard-coded limit in \emph{R}) minus the number of open connections as returned by \code{nrow(showConnections(all=TRUE))}. The option \code{mc.cores} can be used to specify an arbitrary number of workers, e.g., \code{options(mc.cores=4L)}. The \emph{Bioconductor} build system enforces a maximum of 4 workers. The environment variable \code{_R_CHECK_LIMIT_CORES_} (as on CRAN) enforces a maximum of 2 workers. A FORK transport starts workers with the \code{mcfork} function and communicates between master and workers using socket connections. \code{mcfork} builds on fork() and thus a Linux cluster is not supported. Because FORK clusters are Posix based they are not supported on Windows. When \code{MulticoreParam} is created/used in Windows it defaults to \code{SerialParam} which is the equivalent of using a single worker. \describe{ \item{error handling:}{ By default all computations are attempted and partial results are returned with any error messages. \itemize{ \item \code{stop.on.error} A \code{logical}. Stops all jobs as soon as one job fails or wait for all jobs to terminate. When \code{FALSE}, the return value is a list of successful results along with error messages as 'conditions'. \item The \code{bpok(x)} function returns a \code{logical()} vector that is FALSE for any jobs that threw an error. The input \code{x} is a list output from a bp*apply function such as \code{bplapply} or \code{bpmapply}. } } \item{logging:}{ When \code{log = TRUE} the \code{futile.logger} package is loaded on the workers. All log messages written in the \code{futile.logger} format are captured by the logging mechanism and returned in real-time (i.e., as each task completes) instead of after all jobs have finished. Messages sent to \emph{stdout} and \emph{stderr} are returned to the workspace by default. When \code{log = TRUE} these are diverted to the log output. Those familiar with the \code{outfile} argument to \code{makeCluster} can think of \code{log = FALSE} as equivalent to \code{outfile = NULL}; providing a \code{logdir} is the same as providing a name for \code{outfile} except that BiocParallel writes a log file for each task. The log output includes additional statistics such as memory use and task runtime. Memory use is computed by calling gc(reset=TRUE) before code evaluation and gc() (no reseet) after. The output of the second gc() call is sent to the log file. There are many ways to track memory use - this particular approach was taken because it is consistent with how the BatchJobs package reports memory on the workers. } \item{log and result files:}{ Results and logs can be written to a file instead of returned to the workspace. Writing to files is done from the master as each task completes. Options can be set with the \code{logdir} and \code{resultdir} fields in the constructor or with the accessors, \code{bplogdir} and \code{bpresultdir}. } \item{random number generation:}{ For \code{MulticoreParam}, \code{SnowParam}, and \code{SerialParam}, random number generation is controlled through the \code{RNGseed = } argument. BiocParallel uses the L'Ecuyer-CMRG random number generator described in the parallel package to generate independent random number streams. One stream is associated with each element of \code{X}, and used to seed the random number stream for the application of \code{FUN()} to \code{X[[i]]}. Thus setting \code{RNGseed = } ensures reproducibility across \code{MulticoreParam()}, \code{SnowParam()}, and \code{SerialParam()}, regardless of worker or task number. The default value \code{RNGseed = NULL} means that each evaluation of \code{bplapply} proceeds independently. For details of the L'Ecuyer generator, see ?\code{clusterSetRNGStream}. } } } \section{Constructor}{ \describe{ \item{}{ \code{MulticoreParam(workers = multicoreWorkers(), tasks = 0L, stop.on.error = FALSE, tasks = 0L, progressbar = FALSE, RNGseed = NULL, timeout = Inf, exportglobals=TRUE, log = FALSE, threshold = "INFO", logdir = NA_character_, resultdir = NA_character_, manager.hostname = NA_character_, manager.port = NA_integer_, ...)}: Return an object representing a FORK cluster. The cluster is not created until \code{bpstart} is called. Named arguments in \code{...} are passed to \code{makeCluster}. } } } \arguments{ \item{workers}{ \code{integer(1)} Number of workers. Defaults to all cores available as determined by \code{detectCores}. } \item{tasks}{ \code{integer(1)}. The number of tasks per job. \code{value} must be a scalar integer >= 0L. In this documentation a job is defined as a single call to a function, such as \code{bplapply}, \code{bpmapply} etc. A task is the division of the \code{X} argument into chunks. When \code{tasks == 0} (default, except when \code{progressbar = TRUE}), \code{X} is divided as evenly as possible over the number of workers. A \code{tasks} value of > 0 specifies the exact number of tasks. Values can range from 1 (all of \code{X} to a single worker) to the length of \code{X} (each element of \code{X} to a different worker). When the length of \code{X} is less than the number of workers each element of \code{X} is sent to a worker and \code{tasks} is ignored. When the length of \code{X} is less than \code{tasks}, \code{tasks} is treated as \code{length(X)}. } \item{stop.on.error}{ \code{logical(1)} Enable stop on error. } \item{progressbar}{ \code{logical(1)} Enable progress bar (based on plyr:::progress_text). Enabling the progress bar changes the \emph{default} value of \code{tasks} to \code{.Machine$integer.max}, so that progress is reported for each element of \code{X}. } \item{RNGseed}{ \code{integer(1)} Seed for random number generation. The seed is used to set a new, independent random number stream for each element of \code{X}. The ith element recieves the same stream seed, regardless of use of \code{SerialParam()}, \code{SnowParam()}, or \code{MulticoreParam()}, and regardless of worker or task number. When \code{RNGseed = NULL}, a random seed is used. } \item{timeout}{ \code{numeric(1)} Time (in seconds) allowed for worker to complete a task. This value is passed to base::setTimeLimit() as both the \code{cpu} and \code{elapsed} arguments. If the computation exceeds \code{timeout} an error is thrown with message 'reached elapsed time limit'. } \item{exportglobals}{ \code{logical(1)} Export \code{base::options()} from manager to workers? Default \code{TRUE}. } \item{log}{ \code{logical(1)} Enable logging. } \item{threshold}{ \code{character(1)} Logging threshold as defined in \code{futile.logger}. } \item{logdir}{ \code{character(1)} Log files directory. When not provided, log messages are returned to stdout. } \item{resultdir}{ \code{character(1)} Job results directory. When not provided, results are returned as an \R{} object (list) to the workspace. } \item{jobname}{ \code{character(1)} Job name that is prepended to log and result files. Default is "BPJOB". } \item{force.GC}{ \code{logical(1)} Whether to invoke the garbage collector after each call to \code{FUN}. The default (\code{TRUE}, explicitly call the garbage collection) can slow parallel computation, but is necessary when each call to \code{FUN} allocates a 'large' amount of memory. If \code{FUN} allocates little memory, then considerable performance improvements are gained by setting \code{force.GC = FALSE}. } \item{manager.hostname}{ \code{character(1)} Host name of manager node. See 'Global Options', in \code{\link{SnowParam}}. } \item{manager.port}{ \code{integer(1)} Port on manager with which workers communicate. See 'Global Options' in \code{\link{SnowParam}}. } \item{\dots}{ Additional arguments passed to \code{\link{makeCluster}} } } \section{Accessors: Logging and results}{ In the following code, \code{x} is a \code{MulticoreParam} object. \describe{ \item{}{ \code{bpprogressbar(x)}, \code{bpprogressbar(x) <- value}: Get or set the value to enable text progress bar. \code{value} must be a \code{logical(1)}. } \item{}{ \code{bpjobname(x)}, \code{bpjobname(x) <- value}: Get or set the job name. } \item{}{ \code{bpRNGseed(x)}, \code{bpRNGseed(x) <- value}: Get or set the seed for random number generaton. \code{value} must be a \code{numeric(1)} or \code{NULL}. } \item{}{ \code{bplog(x)}, \code{bplog(x) <- value}: Get or set the value to enable logging. \code{value} must be a \code{logical(1)}. } \item{}{ \code{bpthreshold(x)}, \code{bpthreshold(x) <- value}: Get or set the logging threshold. \code{value} must be a \code{character(1)} string of one of the levels defined in the \code{futile.logger} package: \dQuote{TRACE}, \dQuote{DEBUG}, \dQuote{INFO}, \dQuote{WARN}, \dQuote{ERROR}, or \dQuote{FATAL}. } \item{}{ \code{bplogdir(x)}, \code{bplogdir(x) <- value}: Get or set the directory for the log file. \code{value} must be a \code{character(1)} path, not a file name. The file is written out as LOGFILE.out. If no \code{logdir} is provided and \code{bplog=TRUE} log messages are sent to stdout. } \item{}{ \code{bpresultdir(x)}, \code{bpresultdir(x) <- value}: Get or set the directory for the result files. \code{value} must be a \code{character(1)} path, not a file name. Separate files are written for each job with the prefix JOB (e.g., JOB1, JOB2, etc.). When no \code{resultdir} is provided the results are returned to the session as \code{list}. } } } \section{Accessors: Back-end control}{ In the code below \code{x} is a \code{MulticoreParam} object. See the ?\code{BiocParallelParam} man page for details on these accessors. \describe{ \item{}{\code{bpworkers(x)}} \item{}{\code{bpnworkers(x)}} \item{}{\code{bptasks(x)}, \code{bptasks(x) <- value}} \item{}{\code{bpstart(x)}} \item{}{\code{bpstop(x)}} \item{}{\code{bpisup(x)}} \item{}{\code{bpbackend(x)}, \code{bpbackend(x) <- value}} } } \section{Accessors: Error Handling}{ In the code below \code{x} is a \code{MulticoreParam} object. See the ?\code{BiocParallelParam} man page for details on these accessors. \describe{ \item{}{\code{bpstopOnError(x)}, \code{bpstopOnError(x) <- value}} } } \section{Methods: Evaluation}{ In the code below \code{BPPARAM} is a \code{MulticoreParam} object. Full documentation for these functions are on separate man pages: see ?\code{bpmapply}, ?\code{bplapply}, ?\code{bpvec}, ?\code{bpiterate} and ?\code{bpaggregate}. \describe{ \item{}{ \code{bpmapply(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPPARAM=bpparam())} } \item{}{ \code{bplapply(X, FUN, ..., BPPARAM=bpparam())} } \item{}{ \code{bpvec(X, FUN, ..., AGGREGATE=c, BPPARAM=bpparam())} } \item{}{ \code{bpiterate(ITER, FUN, ..., BPPARAM=bpparam())} } \item{}{ \code{bpaggregate(x, data, FUN, ..., BPPARAM=bpparam())} } } } \section{Methods: Other}{ In the code below \code{x} is a \code{MulticoreParam} object. \describe{ \item{}{ \code{show(x)}: Displays the \code{MulticoreParam} object. } } } \section{Global Options}{ See the 'Global Options' section of \code{\link{SnowParam}} for manager host name and port defaults. } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org} and Valerie Obenchain} \seealso{ \itemize{ \item \code{register} for registering parameter classes for use in parallel evaluation. \item \code{\link{SnowParam}} for computing in distributed memory \item \code{\link{BatchJobsParam}} for computing with cluster schedulers \item \code{\link{DoparParam}} for computing with foreach \item \code{\link{SerialParam}} for non-parallel evaluation } } \examples{ ## ----------------------------------------------------------------------- ## Job configuration: ## ----------------------------------------------------------------------- ## MulticoreParam supports shared memory computing. The object fields ## control the division of tasks, error handling, logging and ## result format. bpparam <- MulticoreParam() bpparam ## By default the param is created with the maximum available workers ## determined by multicoreWorkers(). multicoreWorkers() ## Fields are modified with accessors of the same name: bplog(bpparam) <- TRUE dir.create(resultdir <- tempfile()) bpresultdir(bpparam) <- resultdir bpparam ## ----------------------------------------------------------------------- ## Logging: ## ----------------------------------------------------------------------- ## When 'log == TRUE' the workers use a custom script (in BiocParallel) ## that enables logging and access to other job statistics. Log messages ## are returned as each job completes rather than waiting for all to finish. ## In 'fun', a value of 'x = 1' will throw a warning, 'x = 2' is ok ## and 'x = 3' throws an error. Because 'x = 1' sleeps, the warning ## should return after the error. X <- 1:3 fun <- function(x) { if (x == 1) { Sys.sleep(2) sqrt(-x) ## warning x } else if (x == 2) { x ## ok } else if (x == 3) { sqrt("FOO") ## error } } ## By default logging is off. Turn it on with the bplog()<- setter ## or by specifying 'log = TRUE' in the constructor. bpparam <- MulticoreParam(3, log = TRUE, stop.on.error = FALSE) res <- tryCatch({ bplapply(X, fun, BPPARAM=bpparam) }, error=identity) res ## When a 'logdir' location is given the messages are redirected to a file: \dontrun{ bplogdir(bpparam) <- tempdir() bplapply(X, fun, BPPARAM = bpparam) list.files(bplogdir(bpparam)) } ## ----------------------------------------------------------------------- ## Managing results: ## ----------------------------------------------------------------------- ## By default results are returned as a list. When 'resultdir' is given ## files are saved in the directory specified by job, e.g., 'TASK1.Rda', ## 'TASK2.Rda', etc. \dontrun{ dir.create(resultdir <- tempfile()) bpparam <- MulticoreParam(2, resultdir = resultdir, stop.on.error = FALSE) bplapply(X, fun, BPPARAM = bpparam) list.files(bpresultdir(bpparam)) } ## ----------------------------------------------------------------------- ## Error handling: ## ----------------------------------------------------------------------- ## When 'stop.on.error' is TRUE the job is terminated as soon as an ## error is hit. When FALSE, all computations are attempted and partial ## results are returned along with errors. In this example the number of ## 'tasks' is set to equal the length of 'X' so each element is run ## separately. (Default behavior is to divide 'X' evenly over workers.) ## All results along with error: bpparam <- MulticoreParam(2, tasks = 4, stop.on.error = FALSE) res <- bptry(bplapply(list(1, "two", 3, 4), sqrt, BPPARAM = bpparam)) res ## Calling bpok() on the result list returns TRUE for elements with no error. bpok(res) ## ----------------------------------------------------------------------- ## Random number generation: ## ----------------------------------------------------------------------- ## Random number generation is controlled with the 'RNGseed' field. ## This seed is passed to parallel::clusterSetRNGStream ## which uses the L'Ecuyer-CMRG random number generator and distributes ## streams to members of the cluster. bpparam <- MulticoreParam(3, RNGseed = 7739465) bplapply(seq_len(bpnworkers(bpparam)), function(i) rnorm(1), BPPARAM = bpparam) } \keyword{classes} \keyword{methods} BiocParallel/man/SerialParam-class.Rd0000644000175200017520000001614314136050065020477 0ustar00biocbuildbiocbuild\name{SerialParam-class} \Rdversion{1.1} \docType{class} \alias{SerialParam-class} \alias{SerialParam} \alias{bpbackend,SerialParam-method} \alias{bpstart,SerialParam-method} \alias{bpstop,SerialParam-method} \alias{bpisup,SerialParam-method} \alias{bpworkers,SerialParam-method} \alias{bplog,SerialParam-method} \alias{bplogdir,SerialParam-method} \alias{bplog<-,SerialParam,logical-method} \alias{bpthreshold<-,SerialParam,character-method} \alias{bplogdir<-,SerialParam,character-method} \alias{length,SerialBackend-method} \title{Enable serial evaluation} \description{ This class is used to parameterize serial evaluation, primarily to facilitate easy transition from parallel to serial code. } \usage{ SerialParam( stop.on.error = TRUE, progressbar = FALSE, RNGseed = NULL, timeout = 30L * 24L * 60L * 60L, log = FALSE, threshold = "INFO", logdir = NA_character_, resultdir = NA_character_, jobname = "BPJOB", force.GC = FALSE ) } \details{ \code{SerialParam} is used for serial computation on a single node. Using \code{SerialParam} in conjunction with \code{bplapply} differs from use of \code{lapply} because it provides features such as error handling, logging, and random number use consistent with \code{SnowParam} and \code{MulticoreParam}. \describe{ \item{error handling:}{ By default all computations are attempted and partial results are returned with any error messages. \itemize{ \item \code{stop.on.error} A \code{logical}. Stops all jobs as soon as one job fails or wait for all jobs to terminate. When \code{FALSE}, the return value is a list of successful results along with error messages as 'conditions'. \item The \code{bpok(x)} function returns a \code{logical()} vector that is FALSE for any jobs that threw an error. The input \code{x} is a list output from a bp*apply function such as \code{bplapply} or \code{bpmapply}. } } \item{logging:}{ When \code{log = TRUE} the \code{futile.logger} package is loaded on the workers. All log messages written in the \code{futile.logger} format are captured by the logging mechanism and returned real-time (i.e., as each task completes) instead of after all jobs have finished. Messages sent to \emph{stdout} and \emph{stderr} are returned to the workspace by default. When \code{log = TRUE} these are diverted to the log output. Those familiar with the \code{outfile} argument to \code{makeCluster} can think of \code{log = FALSE} as equivalent to \code{outfile = NULL}; providing a \code{logdir} is the same as providing a name for \code{outfile} except that BiocParallel writes a log file for each task. The log output includes additional statistics such as memory use and task runtime. Memory use is computed by calling gc(reset=TRUE) before code evaluation and gc() (no reseet) after. The output of the second gc() call is sent to the log file. There are many ways to track memory use - this particular approach was taken because it is consistent with how the BatchJobs package reports memory on the workers. } \item{log and result files:}{ Results and logs can be written to a file instead of returned to the workspace. Writing to files is done from the master as each task completes. Options can be set with the \code{logdir} and \code{resultdir} fields in the constructor or with the accessors, \code{bplogdir} and \code{bpresultdir}. } \item{random number generation:}{ For \code{MulticoreParam}, \code{SnowParam}, and \code{SerialParam}, random number generation is controlled through the \code{RNGseed = } argument. BiocParallel uses the L'Ecuyer-CMRG random number generator described in the parallel package to generate independent random number streams. One stream is associated with each element of \code{X}, and used to seed the random number stream for the application of \code{FUN()} to \code{X[[i]]}. Thus setting \code{RNGseed = } ensures reproducibility across \code{MulticoreParam()}, \code{SnowParam()}, and \code{SerialParam()}, regardless of worker or task number. The default value \code{RNGseed = NULL} means that each evaluation of \code{bplapply} proceeds independently. For details of the L'Ecuyer generator, see ?\code{clusterSetRNGStream}. } } } \section{Constructor}{ \describe{ \item{}{ \code{SerialParam()}: Return an object to be used for serial evaluation of otherwise parallel functions such as \code{\link{bplapply}}, \code{\link{bpvec}}. } } } \arguments{ \item{stop.on.error}{ \code{logical(1)} Enable stop on error. } \item{progressbar}{ \code{logical(1)} Enable progress bar (based on plyr:::progress_text). } \item{RNGseed}{ \code{integer(1)} Seed for random number generation. The seed is used to set a new, independent random number stream for each element of \code{X}. The ith element recieves the same stream seed, regardless of use of \code{SerialParam()}, \code{SnowParam()}, or \code{MulticoreParam()}, and regardless of worker or task number. When \code{RNGseed = NULL}, a random seed is used. } \item{timeout}{ \code{numeric(1)} Time (in seconds) allowed for worker to complete a task. This value is passed to base::setTimeLimit() as both the \code{cpu} and \code{elapsed} arguments. If the computation exceeds \code{timeout} an error is thrown with message 'reached elapsed time limit'. } \item{log}{ \code{logical(1)} Enable logging. } \item{threshold}{ \code{character(1)} Logging threshold as defined in \code{futile.logger}. } \item{logdir}{ \code{character(1)} Log files directory. When not provided, log messages are returned to stdout. } \item{resultdir}{ \code{character(1)} Job results directory. When not provided, results are returned as an \R{} object (list) to the workspace. } \item{jobname}{ \code{character(1)} Job name that is prepended to log and result files. Default is "BPJOB". } \item{force.GC}{ \code{logical(1)} Whether to invoke the garbage collector after each call to \code{FUN}. The default (\code{FALSE}, do not explicitly call the garbage collection) rarely needs to be changed. } } \section{Methods}{ The following generics are implemented and perform as documented on the corresponding help page (e.g., \code{?bpworkers}): \code{\link{bpworkers}}. \code{\link{bpisup}}, \code{\link{bpstart}}, \code{\link{bpstop}}, are implemented, but do not have any side-effects. } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}} \seealso{ \code{getClass("BiocParallelParam")} for additional parameter classes. \code{register} for registering parameter classes for use in parallel evaluation. } \examples{ p <- SerialParam() simplify2array(bplapply(1:10, sqrt, BPPARAM=p)) bpvec(1:10, sqrt, BPPARAM=p) \dontrun{ register(SerialParam(), default=TRUE) } } \keyword{classes} BiocParallel/man/SnowParam-class.Rd0000644000175200017520000004730314136050065020210 0ustar00biocbuildbiocbuild\name{SnowParam-class} \Rdversion{1.1} \docType{class} % Class \alias{SnowParam} \alias{SnowParam-class} % Control \alias{snowWorkers} \alias{bpbackend,SnowParam-method} \alias{bpbackend<-,SnowParam,cluster-method} \alias{bpisup,SnowParam-method} \alias{bpstart,SnowParam-method} \alias{bpstop,SnowParam-method} \alias{bpworkers,SnowParam-method} \alias{bpworkers<-,SnowParam,numeric-method} \alias{bpworkers<-,SnowParam,character-method} % Accessors \alias{bplog,SnowParam-method} \alias{bplog<-,SnowParam,logical-method} \alias{bpthreshold,SnowParam-method} \alias{bpthreshold<-,SnowParam,character-method} % Other methods \alias{coerce,SOCKcluster,SnowParam-method} \alias{coerce,spawnedMPIcluster,SnowParam-method} \alias{show,SnowParam-method} % Environment variables \alias{BIOCPARALLEL_WORKER_NUMBER} \alias{R_PARALLELLY_AVAILABLECORES_FALLBACK} \title{Enable simple network of workstations (SNOW)-style parallel evaluation} \description{ This class is used to parameterize simple network of workstations (SNOW) parallel evaluation on one or several physical computers. \code{snowWorkers()} chooses the number of workers. } \usage{ ## constructor ## ------------------------------------ SnowParam(workers = snowWorkers(type), type=c("SOCK", "MPI", "FORK"), tasks = 0L, stop.on.error = TRUE, progressbar = FALSE, RNGseed = NULL, timeout = 30L * 24L * 60L * 60L, exportglobals = TRUE, log = FALSE, threshold = "INFO", logdir = NA_character_, resultdir = NA_character_, jobname = "BPJOB", force.GC = FALSE, manager.hostname = NA_character_, manager.port = NA_integer_, ...) ## coercion ## ------------------------------------ ## as(SOCKcluster, SnowParam) ## as(spawnedMPIcluster,SnowParam) ## detect workers ## ------------------------------------ snowWorkers(type = c("SOCK", "MPI", "FORK")) } \details{ \code{SnowParam} is used for distributed memory computing and supports 2 cluster types: \sQuote{SOCK} (default) and \sQuote{MPI}. The \code{SnowParam} builds on infrastructure in the \code{snow} and \code{parallel} packages and provides the additional features of error handling, logging and writing out results. The default number of workers is determined by \code{snowWorkers()}. This is determined by an environment variable \code{R_PARALLELLY_AVAILABLECORES_FALLBACK} (see the parallely package) or \code{BIOCPARALLEL_WORKER_NUMBER} or, if those are not set, the maximum of 1 and \code{parallel::detectCores() - 2}. Machines with 3 or fewer cores, or machines where number of cores cannot be determined, are assigned a single worker. Machines with more than 127 cores are limited to the number of \emph{R} connections available when the workers start; this is 128 (a hard-coded limit in \emph{R}) minus the number of open connections as returned by \code{nrow(showConnections(all=TRUE))}. The option \code{mc.cores} can be used to specify an arbitrary number of workers, e.g., \code{options(mc.cores=4L)}; the \emph{Bioconductor} build system enforces a maximum of 4 workers. The environment variable \code{_R_CHECK_LIMIT_CORES_} (as on CRAN) enforces a maximum of 2 workers. \describe{ \item{error handling:}{ By default all computations are attempted and partial results are returned with any error messages. \itemize{ \item \code{stop.on.error} A \code{logical}. Stops all jobs as soon as one job fails or wait for all jobs to terminate. When \code{FALSE}, the return value is a list of successful results along with error messages as 'conditions'. \item The \code{bpok(x)} function returns a \code{logical()} vector that is FALSE for any jobs that threw an error. The input \code{x} is a list output from a bp*apply function such as \code{bplapply} or \code{bpmapply}. } } \item{logging:}{ When \code{log = TRUE} the \code{futile.logger} package is loaded on the workers. All log messages written in the \code{futile.logger} format are captured by the logging mechanism and returned real-time (i.e., as each task completes) instead of after all jobs have finished. Messages sent to \emph{stdout} and \emph{stderr} are returned to the workspace by default. When \code{log = TRUE} these are diverted to the log output. Those familiar with the \code{outfile} argument to \code{makeCluster} can think of \code{log = FALSE} as equivalent to \code{outfile = NULL}; providing a \code{logdir} is the same as providing a name for \code{outfile} except that BiocParallel writes a log file for each task. The log output includes additional statistics such as memory use and task runtime. Memory use is computed by calling gc(reset=TRUE) before code evaluation and gc() (no reseet) after. The output of the second gc() call is sent to the log file. There are many ways to track memory use - this particular approach was taken because it is consistent with how the BatchJobs package reports memory on the workers. } \item{log and result files:}{ Results and logs can be written to a file instead of returned to the workspace. Writing to files is done from the master as each task completes. Options can be set with the \code{logdir} and \code{resultdir} fields in the constructor or with the accessors, \code{bplogdir} and \code{bpresultdir}. } \item{random number generation:}{ For \code{MulticoreParam}, \code{SnowParam}, and \code{SerialParam}, random number generation is controlled through the \code{RNGseed = } argument. BiocParallel uses the L'Ecuyer-CMRG random number generator described in the parallel package to generate independent random number streams. One stream is associated with each element of \code{X}, and used to seed the random number stream for the application of \code{FUN()} to \code{X[[i]]}. Thus setting \code{RNGseed = } ensures reproducibility across \code{MulticoreParam()}, \code{SnowParam()}, and \code{SerialParam()}, regardless of worker or task number. The default value \code{RNGseed = NULL} means that each evaluation of \code{bplapply} proceeds independently. For details of the L'Ecuyer generator, see ?\code{clusterSetRNGStream}. } NOTE: The \code{PSOCK} cluster from the \code{parallel} package does not support cluster options \code{scriptdir} and \code{useRscript}. \code{PSOCK} is not supported because these options are needed to re-direct to an alternate worker script located in BiocParallel. } } \section{Constructor}{ \describe{ \item{}{ \code{SnowParam(workers = snowWorkers(), type=c("SOCK", "MPI"), tasks = 0L, stop.on.error = FALSE, progressbar = FALSE, RNGseed = NULL, timeout = Inf, exportglobals = TRUE, log = FALSE, threshold = "INFO", logdir = NA_character_, resultdir = NA_character_, jobname = "BPJOB", manager.hostname = NA_character_, manager.port = NA_integer_, ...)}: Return an object representing a SNOW cluster. The cluster is not created until \code{bpstart} is called. Named arguments in \code{...} are passed to \code{makeCluster}. } } } \arguments{ \item{workers}{ \code{integer(1)} Number of workers. Defaults to all cores available as determined by \code{detectCores}. For a \code{SOCK} cluster \code{workers} can be a \code{character()} vector of host names. } \item{type}{ \code{character(1)} Type of cluster to use. Possible values are \code{SOCK} (default) and \code{MPI}. Instead of \code{type=FORK} use \code{MulticoreParam}. } \item{tasks}{ \code{integer(1)}. The number of tasks per job. \code{value} must be a scalar integer >= 0L. In this documentation a job is defined as a single call to a function, such as \code{bplapply}, \code{bpmapply} etc. A task is the division of the \code{X} argument into chunks. When \code{tasks == 0} (default), \code{X} is divided as evenly as possible over the number of workers. A \code{tasks} value of > 0 specifies the exact number of tasks. Values can range from 1 (all of \code{X} to a single worker) to the length of \code{X} (each element of \code{X} to a different worker). When the length of \code{X} is less than the number of workers each element of \code{X} is sent to a worker and \code{tasks} is ignored. } \item{stop.on.error}{ \code{logical(1)} Enable stop on error. } \item{progressbar}{ \code{logical(1)} Enable progress bar (based on plyr:::progress_text). } \item{RNGseed}{ \code{integer(1)} Seed for random number generation. The seed is used to set a new, independent random number stream for each element of \code{X}. The ith element recieves the same stream seed, regardless of use of \code{SerialParam()}, \code{SnowParam()}, or \code{MulticoreParam()}, and regardless of worker or task number. When \code{RNGseed = NULL}, a random seed is used. } \item{timeout}{ \code{numeric(1)} Time (in seconds) allowed for worker to complete a task. This value is passed to base::setTimeLimit() as both the \code{cpu} and \code{elapsed} arguments. If the computation exceeds \code{timeout} an error is thrown with message 'reached elapsed time limit'. } \item{exportglobals}{ \code{logical(1)} Export \code{base::options()} from manager to workers? Default \code{TRUE}. } \item{log}{ \code{logical(1)} Enable logging. } \item{threshold}{ \code{character(1)} Logging threshold as defined in \code{futile.logger}. } \item{logdir}{ \code{character(1)} Log files directory. When not provided, log messages are returned to stdout. } \item{resultdir}{ \code{character(1)} Job results directory. When not provided, results are returned as an \R{} object (list) to the workspace. } \item{jobname}{ \code{character(1)} Job name that is prepended to log and result files. Default is "BPJOB". } \item{force.GC}{ \code{logical(1)} Whether to invoke the garbage collector after each call to \code{FUN}. The default (\code{FALSE}, do not explicitly call the garbage collection) rarely needs to be changed. } \item{manager.hostname}{ \code{character(1)} Host name of manager node. See 'Global Options', below. } \item{manager.port}{ \code{integer(1)} Port on manager with which workers communicate. See 'Global Options', below. } \item{\dots}{ Additional arguments passed to \code{\link{makeCluster}} } } \section{Accessors: Logging and results}{ In the following code, \code{x} is a \code{SnowParam} object. \describe{ \item{}{ \code{bpprogressbar(x)}, \code{bpprogressbar(x) <- value}: Get or set the value to enable text progress bar. \code{value} must be a \code{logical(1)}. } \item{}{ \code{bpjobname(x)}, \code{bpjobname(x) <- value}: Get or set the job name. } \item{}{ \code{bpRNGseed(x)}, \code{bpRNGseed(x) <- value}: Get or set the seed for random number generaton. \code{value} must be a \code{numeric(1)} or \code{NULL}. } \item{}{ \code{bplog(x)}, \code{bplog(x) <- value}: Get or set the value to enable logging. \code{value} must be a \code{logical(1)}. } \item{}{ \code{bpthreshold(x)}, \code{bpthreshold(x) <- value}: Get or set the logging threshold. \code{value} must be a \code{character(1)} string of one of the levels defined in the \code{futile.logger} package: \dQuote{TRACE}, \dQuote{DEBUG}, \dQuote{INFO}, \dQuote{WARN}, \dQuote{ERROR}, or \dQuote{FATAL}. } \item{}{ \code{bplogdir(x)}, \code{bplogdir(x) <- value}: Get or set the directory for the log file. \code{value} must be a \code{character(1)} path, not a file name. The file is written out as BPLOG.out. If no \code{logdir} is provided and \code{bplog=TRUE} log messages are sent to stdout. } \item{}{ \code{bpresultdir(x)}, \code{bpresultdir(x) <- value}: Get or set the directory for the result files. \code{value} must be a \code{character(1)} path, not a file name. Separate files are written for each job with the prefix TASK (e.g., TASK1, TASK2, etc.). When no \code{resultdir} is provided the results are returned to the session as \code{list}. } } } \section{Accessors: Back-end control}{ In the code below \code{x} is a \code{SnowParam} object. See the ?\code{BiocParallelParam} man page for details on these accessors. \describe{ \item{}{\code{bpworkers(x)}, \code{bpworkers(x) <- value}, \code{bpnworkers(x)}} \item{}{\code{bptasks(x)}, \code{bptasks(x) <- value}} \item{}{\code{bpstart(x)}} \item{}{\code{bpstop(x)}} \item{}{\code{bpisup(x)}} \item{}{\code{bpbackend(x)}, \code{bpbackend(x) <- value}} } } \section{Accessors: Error Handling}{ In the code below \code{x} is a \code{SnowParam} object. See the ?\code{BiocParallelParam} man page for details on these accessors. \describe{ \item{}{\code{bpstopOnError(x)}, \code{bpstopOnError(x) <- value}} } } \section{Methods: Evaluation}{ In the code below \code{BPPARAM} is a \code{SnowParam} object. Full documentation for these functions are on separate man pages: see ?\code{bpmapply}, ?\code{bplapply}, ?\code{bpvec}, ?\code{bpiterate} and ?\code{bpaggregate}. \describe{ \item{}{ \code{bpmapply(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPPARAM=bpparam())} } \item{}{ \code{bplapply(X, FUN, ..., BPPARAM=bpparam())} } \item{}{ \code{bpvec(X, FUN, ..., AGGREGATE=c, BPPARAM=bpparam())} } \item{}{ \code{bpiterate(ITER, FUN, ..., BPPARAM=bpparam())} } \item{}{ \code{bpaggregate(x, data, FUN, ..., BPPARAM=bpparam())} } } } \section{Methods: Other}{ In the code below \code{x} is a \code{SnowParam} object. \describe{ \item{}{ \code{show(x)}: Displays the \code{SnowParam} object. } \item{}{ \code{bpok(x)}: Returns a \code{logical()} vector: FALSE for any jobs that resulted in an error. \code{x} is the result list output by a \code{BiocParallel} function such as \code{bplapply} or \code{bpmapply}. } } } \section{Coercion}{ \describe{ \item{}{ \code{as(from, "SnowParam")}: Creates a \code{SnowParam} object from a \code{SOCKcluster} or \code{spawnedMPIcluster} object. Instances created in this way cannot be started or stopped. } } } \section{Global Options}{ The environment variable \code{BIOCPARALLEL_WORKER_NUMBER} and the the global option \code{mc.cores} influences the number of workers determined by \code{snowWorkers()} (described above) or \code{multicoreWorkers()} (see \code{\link{multicoreWorkers}}). Workers communicate to the master through socket connections. Socket connections require a hostname and port. These are determined by arguments \code{manager.hostname} and \code{manager.port}; default values are influenced by global options. The default manager hostname is "localhost" when the number of workers are specified as a \code{numeric(1)}, and \code{Sys.info()[["nodename"]]} otherwise. The hostname can be over-ridden by the envirnoment variable \code{MASTER}, or the global option \code{bphost} (e.g., \code{options(bphost=Sys.info()[["nodename"]])}. The default port is chosen as a random value between 11000 and 11999. The port may be over-ridden by the environment variable \code{R_PARALLEL_PORT} or \code{PORT}, and by the option \code{ports}, e.g., \code{options(ports=12345L)}. } \author{Martin Morgan and Valerie Obenchain.} \seealso{ \itemize{ \item \code{register} for registering parameter classes for use in parallel evaluation. \item \code{\link{MulticoreParam}} for computing in shared memory \item \code{\link{BatchJobsParam}} for computing with cluster schedulers \item \code{\link{DoparParam}} for computing with foreach \item \code{\link{SerialParam}} for non-parallel evaluation } } \examples{ ## ----------------------------------------------------------------------- ## Job configuration: ## ----------------------------------------------------------------------- ## SnowParam supports distributed memory computing. The object fields ## control the division of tasks, error handling, logging and result ## format. bpparam <- SnowParam() bpparam ## Fields are modified with accessors of the same name: bplog(bpparam) <- TRUE dir.create(resultdir <- tempfile()) bpresultdir(bpparam) <- resultdir bpparam ## ----------------------------------------------------------------------- ## Logging: ## ----------------------------------------------------------------------- ## When 'log == TRUE' the workers use a custom script (in BiocParallel) ## that enables logging and access to other job statistics. Log messages ## are returned as each job completes rather than waiting for all to ## finish. ## In 'fun', a value of 'x = 1' will throw a warning, 'x = 2' is ok ## and 'x = 3' throws an error. Because 'x = 1' sleeps, the warning ## should return after the error. X <- 1:3 fun <- function(x) { if (x == 1) { Sys.sleep(2) if (TRUE & c(TRUE, TRUE)) ## warning x } else if (x == 2) { x ## ok } else if (x == 3) { sqrt("FOO") ## error } } ## By default logging is off. Turn it on with the bplog()<- setter ## or by specifying 'log = TRUE' in the constructor. bpparam <- SnowParam(3, log = TRUE, stop.on.error = FALSE) tryCatch({ bplapply(X, fun, BPPARAM = bpparam) }, error=identity) ## When a 'logdir' location is given the messages are redirected to a ## file: \dontrun{ dir.create(logdir <- tempfile()) bplogdir(bpparam) <- logdir bplapply(X, fun, BPPARAM = bpparam) list.files(bplogdir(bpparam)) } ## ----------------------------------------------------------------------- ## Managing results: ## ----------------------------------------------------------------------- ## By default results are returned as a list. When 'resultdir' is given ## files are saved in the directory specified by job, e.g., 'TASK1.Rda', ## 'TASK2.Rda', etc. \dontrun{ dir.create(resultdir <- tempfile()) bpparam <- SnowParam(2, resultdir = resultdir) bplapply(X, fun, BPPARAM = bpparam) list.files(bpresultdir(bpparam)) } ## ----------------------------------------------------------------------- ## Error handling: ## ----------------------------------------------------------------------- ## When 'stop.on.error' is TRUE the process returns as soon as an error ## is thrown. ## When 'stop.on.error' is FALSE all computations are attempted. Partial ## results are returned along with errors. Use bptry() to see the ## partial results bpparam <- SnowParam(2, stop.on.error = FALSE) res <- bptry(bplapply(list(1, "two", 3, 4), sqrt, BPPARAM = bpparam)) res ## Calling bpok() on the result list returns TRUE for elements with no ## error. bpok(res) ## ----------------------------------------------------------------------- ## Random number generation: ## ----------------------------------------------------------------------- ## Random number generation is controlled with the 'RNGseed' field. ## This seed is passed to parallel::clusterSetRNGStream ## which uses the L'Ecuyer-CMRG random number generator and distributes ## streams for each job bpparam <- SnowParam(3, RNGseed = 7739465) bplapply(seq_len(bpnworkers(bpparam)), function(i) rnorm(1), BPPARAM = bpparam) } \keyword{classes} \keyword{methods} BiocParallel/man/bpaggregate.Rd0000644000175200017520000000570014136050065017441 0ustar00biocbuildbiocbuild\name{bpaggregate} \alias{bpaggregate} \alias{bpaggregate,formula,BiocParallelParam-method} \alias{bpaggregate,matrix,BiocParallelParam-method} \alias{bpaggregate,data.frame,BiocParallelParam-method} \alias{bpaggregate,ANY,missing-method} \title{Apply a function on subsets of data frames} \description{ This is a parallel version of \code{\link[stats]{aggregate}}. } \usage{ \S4method{bpaggregate}{formula,BiocParallelParam}(x, data, FUN, ..., BPREDO=list(), BPPARAM=bpparam()) \S4method{bpaggregate}{data.frame,BiocParallelParam}(x, by, FUN, ..., simplify=TRUE, BPREDO=list(), BPPARAM=bpparam()) \S4method{bpaggregate}{matrix,BiocParallelParam}(x, by, FUN, ..., simplify=TRUE, BPREDO=list(), BPPARAM=bpparam()) \S4method{bpaggregate}{ANY,missing}(x, ..., BPREDO=list(), BPPARAM=bpparam()) } \arguments{ \item{x}{A \code{data.frame}, \code{matrix} or a formula. } \item{by}{A list of factors by which \code{x} is split; applicable when \code{x} is \code{data.frame} or \code{matrix}. } \item{data}{A \code{data.frame}; applicable when \code{x} is a \code{formula}. } \item{FUN}{Function to apply. } \item{...}{Additional arguments for \code{FUN}. } \item{simplify}{If set to \code{TRUE}, the return values of \code{FUN} will be simplified using \code{\link{simplify2array}}. } \item{BPPARAM}{An optional \code{\link{BiocParallelParam}} instance determining the parallel back-end to be used during evaluation. } \item{BPREDO}{A \code{list} of output from \code{bpaggregate} with one or more failed elements. When a list is given in \code{BPREDO}, \code{bpok} is used to identify errors, tasks are rerun and inserted into the original results. } } \details{ \code{bpaggregate} is a generic with methods for \code{data.frame} \code{matrix} and \code{formula} objects. \code{x} is divided into subsets according to factors in \code{by}. Data chunks are sent to the workers, \code{FUN} is applied and results are returned as a \code{data.frame}. The function is similar in spirit to \code{\link[stats]{aggregate}} from the stats package but \code{\link[stats]{aggregate}} is not explicitly called. The \code{bpaggregate} \code{formula} method reformulates the call and dispatches to the \code{data.frame} method which in turn distributes data chunks to workers with \code{bplapply}. } \value{ See \code{\link[stats]{aggregate}}. } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org}. } \examples{ if (interactive() && require(Rsamtools) && require(GenomicAlignments)) { fl <- system.file("extdata", "ex1.bam", package="Rsamtools") param <- ScanBamParam(what = c("flag", "mapq")) gal <- readGAlignments(fl, param=param) ## Report the mean map quality by range cutoff: cutoff <- rep(0, length(gal)) cutoff[start(gal) > 1000 & start(gal) < 1500] <- 1 cutoff[start(gal) > 1500] <- 2 bpaggregate(as.data.frame(mcols(gal)$mapq), list(cutoff = cutoff), mean) } } BiocParallel/man/bpiterate.Rd0000644000175200017520000001574314136050065017160 0ustar00biocbuildbiocbuild\name{bpiterate} \alias{bpiterate} \alias{bpiterate,ANY,ANY,missing-method} \alias{bpiterate,ANY,ANY,SerialParam-method} \alias{bpiterate,ANY,ANY,BiocParallelParam-method} \alias{bpiterate,ANY,ANY,SnowParam-method} \alias{bpiterate,ANY,ANY,BatchJobsParam-method} \alias{bpiterate,ANY,ANY,DoparParam-method} \alias{bpiterate,ANY,ANY,BatchtoolsParam-method} \title{Parallel iteration over an indeterminate number of data chunks} \description{ \code{bpiterate} iterates over an indeterminate number of data chunks (e.g., records in a file). Each chunk is processed by parallel workers in an asynchronous fashion; as each worker finishes it receives a new chunk. Data are traversed a single time. } \usage{ bpiterate(ITER, FUN, ..., BPPARAM=bpparam()) \S4method{bpiterate}{ANY,ANY,missing}(ITER, FUN, ..., BPPARAM=bpparam()) \S4method{bpiterate}{ANY,ANY,BatchtoolsParam}( ITER, FUN, ..., REDUCE, init, reduce.in.order=FALSE, BPPARAM=bpparam() ) } \arguments{ \item{ITER}{ A function with no arguments that returns an object to process, generally a chunk of data from a file. When no objects are left (i.e., end of file) it should return NULL and continue to return NULL regardless of the number of times it is invoked after reaching the end of file. This function is run on the master. } \item{FUN}{ A function to process the object returned by \code{ITER}; run on parallel workers separate from the master. When BPPARAM is a MulticoreParam, FUN is `decorated` with additional arguments and therefore must have \dots in the signature. } \item{BPPARAM}{An optional \code{\link{BiocParallelParam}} instance determining the parallel back-end to be used during evaluation, or a \code{list} of \code{BiocParallelParam} instances, to be applied in sequence for nested calls to \code{bpiterate}. } \item{REDUCE}{Optional function that combines (reduces) output from \code{FUN}. As each worker returns, the data are combined with the \code{REDUCE} function. \code{REDUCE} takes 2 arguments; one is the current result and the other is the output of \code{FUN} from a worker that just finished.} \item{init}{Optional initial value for \code{REDUCE}; must be of the same type as the object returned from \code{FUN}. When supplied, \code{reduce.in.order} is set to TRUE.} \item{reduce.in.order}{Logical. When TRUE, REDUCE is applied to the results from the workers in the same order the tasks were sent out.} \item{\dots}{Arguments to other methods, and named arguments for \code{FUN}.} } \details{ Supported for \code{SnowParam}, \code{MulticoreParam} and \code{BatchtoolsParam}. \code{bpiterate} iterates through an unknown number of data chunks, dispatching chunks to parallel workers as they become available. In contrast, other \code{bp*apply} functions such as \code{bplapply} or \code{bpmapply} require the number of data chunks to be specified ahead of time. This quality makes \code{bpiterate} useful for iterating through files of unknown length. \code{ITER} serves up chunks of data until the end of the file is reached at which point it returns NULL. Note that \code{ITER} should continue to return NULL reguardless of the number of times it is invoked after reaching the end of the file. \code{FUN} is applied to each object (data chunk) returned by \code{ITER}. } \value{ By default, a \code{list} the same length as the number of chunks in \code{ITER()}. When \code{REDUCE} is used, the return is consistent with application of the reduction. } \author{ Valerie Obenchain \url{mailto:vobencha@fhcrc.org}. } \seealso{ \itemize{ \item \code{\link{bpvec}} for parallel, vectorized calculations. \item \code{\link{bplapply}} for parallel, lapply-like calculations. \item \code{\link{BiocParallelParam}} for details of \code{BPPARAM}. \item \code{\link{BatchtoolsParam}} for details of \code{BatchtoolsParam}. } } \examples{ \dontrun{ if (require(Rsamtools) && require(RNAseqData.HNRNPC.bam.chr14) && require(GenomicAlignments) && require(ShortRead)) { ## ---------------------------------------------------------------------- ## Iterate through a BAM file ## ---------------------------------------------------------------------- ## Select a single file and set 'yieldSize' in the BamFile object. fl <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[[1]] bf <- BamFile(fl, yieldSize = 300000) ## bamIterator() is initialized with a BAM file and returns a function. ## The return function requires no arguments and iterates through the ## file returning data chunks the size of yieldSize. bamIterator <- function(bf) { done <- FALSE if (!isOpen( bf)) open(bf) function() { if (done) return(NULL) yld <- readGAlignments(bf) if (length(yld) == 0L) { close(bf) done <<- TRUE NULL } else yld } } ## FUN counts reads in a region of interest. roi <- GRanges("chr14", IRanges(seq(19e6, 107e6, by = 10e6), width = 10e6)) counter <- function(reads, roi, ...) { countOverlaps(query = roi, subject = reads) } ## Initialize the iterator. ITER <- bamIterator(bf) ## The number of chunks returned by ITER() determines the result length. bpparam <- MulticoreParam(workers = 3) ## bpparam <- BatchtoolsParam(workers = 3), see ?BatchtoolsParam bpiterate(ITER, counter, roi = roi, BPPARAM = bpparam) ## Re-initialize the iterator and combine on the fly with REDUCE: ITER <- bamIterator(bf) bpparam <- MulticoreParam(workers = 3) bpiterate(ITER, counter, REDUCE = sum, roi = roi, BPPARAM = bpparam) ## ---------------------------------------------------------------------- ## Iterate through a FASTA file ## ---------------------------------------------------------------------- ## Set data chunk size with 'n' in the FastqStreamer object. sp <- SolexaPath(system.file('extdata', package = 'ShortRead')) fl <- file.path(analysisPath(sp), "s_1_sequence.txt") ## Create an iterator that returns data chunks the size of 'n'. fastqIterator <- function(fqs) { done <- FALSE if (!isOpen(fqs)) open(fqs) function() { if (done) return(NULL) yld <- yield(fqs) if (length(yld) == 0L) { close(fqs) done <<- TRUE NULL } else yld } } ## The process function summarizes the number of times each sequence occurs. summary <- function(reads, ...) { ShortRead::tables(reads, n = 0)$distribution } ## Create a param. bpparam <- SnowParam(workers = 2) ## Initialize the streamer and iterator. fqs <- FastqStreamer(fl, n = 100) ITER <- fastqIterator(fqs) bpiterate(ITER, summary, BPPARAM = bpparam) ## Results from the workers are combined on the fly when REDUCE is used. ## Collapsing the data in this way can substantially reduce memory ## requirements. fqs <- FastqStreamer(fl, n = 100) ITER <- fastqIterator(fqs) bpiterate(ITER, summary, REDUCE = merge, all = TRUE, BPPARAM = bpparam) } } } \keyword{manip} \keyword{methods} BiocParallel/man/bplapply.Rd0000644000175200017520000000432614136050065017017 0ustar00biocbuildbiocbuild\name{bplapply} \alias{bplapply} \alias{bplapply,ANY,list-method} \alias{bplapply,ANY,missing-method} \alias{bplapply,ANY,BiocParallelParam-method} \alias{bplapply,ANY,BatchJobsParam-method} \alias{bplapply,ANY,DoparParam-method} \alias{bplapply,ANY,SerialParam-method} \alias{bplapply,ANY,SnowParam-method} \title{Parallel lapply-like functionality} \description{ \code{bplapply} applies \code{FUN} to each element of \code{X}. Any type of object \code{X} is allowed, provided \code{length}, \code{[}, and \code{[[} methods are available. The return value is a \code{list} of length equal to \code{X}, as with \code{\link[base]{lapply}}. } \usage{ bplapply(X, FUN, ..., BPREDO = list(), BPPARAM=bpparam()) } \arguments{ \item{X}{ Any object for which methods \code{length}, \code{[}, and \code{[[} are implemented. } \item{FUN}{ The \code{function} to be applied to each element of \code{X}. } \item{\dots}{ Additional arguments for \code{FUN}, as in \code{\link{lapply}}. } \item{BPPARAM}{ An optional \code{\link{BiocParallelParam}} instance determining the parallel back-end to be used during evaluation, or a \code{list} of \code{BiocParallelParam} instances, to be applied in sequence for nested calls to \pkg{BiocParallel} functions. } \item{BPREDO}{A \code{list} of output from \code{bplapply} with one or more failed elements. When a list is given in \code{BPREDO}, \code{bpok} is used to identify errors, tasks are rerun and inserted into the original results. } } \details{ See \code{methods{bplapply}} for additional methods, e.g., \code{method?bplapply("MulticoreParam")}. } \value{See \code{\link[base]{lapply}}.} \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org}. Original code as attributed in \code{\link{mclapply}}. } \seealso{ \itemize{ \item \code{\link{bpvec}} for parallel, vectorized calculations. \item \code{\link{BiocParallelParam}} for possible values of \code{BPPARAM}. } } \examples{ methods("bplapply") ## ten tasks (1:10) so ten calls to FUN default registered parallel ## back-end. Compare with bpvec. fun <- function(v) { message("working") ## 10 tasks sqrt(v) } bplapply(1:10, fun) } \keyword{manip} BiocParallel/man/bploop.Rd0000644000175200017520000000621414147652225016475 0ustar00biocbuildbiocbuild\name{bploop} \Rdversion{1.1} % Class \alias{bploop} % managers \alias{bploop.lapply} \alias{bploop.iterate} \alias{bprunMPIslave} \alias{bprunMPIworker} \title{Internal Functions for SNOW-style Parallel Evaluation} \description{ The functions documented on this page are primarily for use within \pkg{BiocParallel} to enable SNOW-style parallel evaluation, using communication between manager and worker nodes through sockets. } \usage{ \S3method{bploop}{lapply}(manager, X, FUN, ARGS, BPPARAM, ...) \S3method{bploop}{iterate}(manager, ITER, FUN, ARGS, BPPARAM, REDUCE, init, reduce.in.order, progress.length, ...) } \arguments{ \item{manager}{An object representing the manager node. For workers, this is the node to which the worker will communicate. For managers, this is the form of iteration -- \code{lapply} or \code{iterate}.} \item{X}{A vector of jobs to be performed.} \item{FUN}{A function to apply to each job.} \item{ARGS}{A list of arguments to be passed to \code{FUN}.} \item{BPPARAM}{An instance of a \code{BiocParallelParam} class.} \item{ITER}{A function used to generate jobs. No more jobs are available when \code{ITER()} returns \code{NULL}.} \item{REDUCE}{(Optional) A function combining two values returned by \code{FUN} into a single value.} \item{init}{(Optional) Initial value for reduction.} \item{reduce.in.order}{(Optional) logical(1) indicating that reduction must occur in the order jobs are dispatched (\code{TRUE}) or that reduction can occur in the order jobs are completed (\code{FALSE}).} \item{\ldots}{Additional arguments, ignored in all cases.} \item{progress.length}{(Optional) The length of the progress bar. Used by \code{bploop.lapply} to indicate use of a progress bar rather than iterator.} } \details{ Workers enter a loop. They wait to receive a message (\R list) from the \code{manager}. The message contains a \code{type} element, with evaluation as follows: \describe{ \item{\dQuote{EXEC}}{Execute the \R{} code in the message, returning the result to the \code{manager}.} \item{\dQuote{DONE}}{Signal termination to the \code{manager}, terminate the worker.} } Managers under \code{lapply} dispatch pre-determined jobs, \code{X}, to workers, collecting the results from and dispatching new jobs to the first available worker. The manager returns a list of results, in a one-to-one correspondence with the order of jobs supplied, when all jobs have been evaluated. Managers under \code{iterate} dispatch an undetermined number of jobs to workers, collecting previous jobs from and dispatching new jobs to the first available worker. Dispatch continues until available jobs are exhausted. The return value is by default a list of results in a one-to-one correspondence with the order of jobs supplied. The return value is influenced by \code{REDUCE}, \code{init}, and \code{reduce.in.order}. } \author{ Valerie Obenchain, Martin Morgan. Derived from similar functionality in the \pkg{snow} and \pkg{parallel} packages. } \examples{ ## These functions are not meant to be called by the end user. } BiocParallel/man/bpmapply.Rd0000644000175200017520000000523314136050065017016 0ustar00biocbuildbiocbuild\name{bpmapply} \alias{bpmapply} \alias{bpmapply,ANY,list-method} \alias{bpmapply,ANY,missing-method} \alias{bpmapply,ANY,BiocParallelParam-method} \title{Parallel mapply-like functionality} \description{ \code{bpmapply} applies \code{FUN} to first elements of \code{...}, the second elements and so on. Any type of object in \code{...} is allowed, provided \code{length}, \code{[}, and \code{[[} methods are available. The return value is a \code{list} of length equal to the length of all objects provided, as with \code{\link[base]{mapply}}. } \usage{ bpmapply(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) \S4method{bpmapply}{ANY,missing}(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) \S4method{bpmapply}{ANY,BiocParallelParam}(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPREDO=list(), BPPARAM=bpparam()) } \arguments{ \item{FUN}{The \code{function} to be applied to each element passed via \code{...}. } \item{\dots}{Objects for which methods \code{length}, \code{[}, and \code{[[} are implemented. All objects must have the same length or shorter objects will be replicated to have length equal to the longest. } \item{MoreArgs}{List of additional arguments to \code{FUN}. } \item{SIMPLIFY}{ If \code{TRUE} the result will be simplified using \code{\link{simplify2array}}. } \item{USE.NAMES}{If \code{TRUE} the result will be named. } \item{BPPARAM}{An optional \code{\link{BiocParallelParam}} instance defining the parallel back-end to be used during evaluation. } \item{BPREDO}{A \code{list} of output from \code{bpmapply} with one or more failed elements. When a list is given in \code{BPREDO}, \code{bpok} is used to identify errors, tasks are rerun and inserted into the original results. } } \details{ See \code{methods{bpmapply}} for additional methods, e.g., \code{method?bpmapply("MulticoreParam")}. } \value{See \code{\link[base]{mapply}}.} \author{ Michel Lang . Original code as attributed in \code{\link{mclapply}}. } \seealso{ \itemize{ \item \code{\link{bpvec}} for parallel, vectorized calculations. \item \code{\link{BiocParallelParam}} for possible values of \code{BPPARAM}. } } \examples{ methods("bpmapply") fun <- function(greet, who) { paste(Sys.getpid(), greet, who) } greet <- c("morning", "night") who <- c("sun", "moon") param <- bpparam() original <- bpworkers(param) bpworkers(param) <- 2 result <- bpmapply(fun, greet, who, BPPARAM = param) cat(paste(result, collapse="\n"), "\n") bpworkers(param) <- original } \keyword{manip} BiocParallel/man/bpok.Rd0000644000175200017520000000670214136050065016127 0ustar00biocbuildbiocbuild\name{bpok} \alias{bpok} \alias{bperrorTypes} \title{Resume computation with partial results} \description{ Identifies unsuccessful results returned from \code{bplapply}, \code{bpmapply}, \code{bpvec}, \code{bpaggregate} or \code{bpvectorize}. } \usage{ bpok(x, type = bperrorTypes()) bperrorTypes() } \arguments{ \item{x}{ Results returned from a call to \code{bp*apply}. } \item{type}{ A character(1) error type, from the vector returned by \code{bperrorTypes()} and described below } } \details{ \code{bpok()} returns a \code{logical()} vector: FALSE for any jobs that resulted in an error. \code{x} is the result list output by \code{bplapply}, \code{bpmapply}, \code{bpvec}, \code{bpaggregate} or \code{bpvectorize}. \code{bperrorTypes()} returns a character() vector of possible error types generated during parallel evaluation. Types are: \itemize{ \item{\code{bperror}: Any of the following errors. This is the default value for \code{bpok()}.} \item{\code{remote_error}: An \emph{R} error occurring while evaluating \code{FUN()}, e.g., taking the square root of a character vector, \code{sqrt("One")}.} \item{\code{unevaluated_error}: When \code{*Param(stop.on.error = TRUE)} (default), a remote error halts evaluation of other tasks assigned to the same worker. The return value for these unevaluated elements is an error of type \code{unevaluated_error}.} \item{\code{not_available_error}: Only produced by \code{DoparParam()} when a remote error occurs during evaluation of an element of \code{X} -- \code{DoparParam()} sets all values after the remote error to this class.} \item{\code{worker_comm_error}: An error occurring while trying to communicate with workers, e.g., when a worker quits unexpectedly. when this type of error occurs, the length of the result may differ from the length of the input \code{X}. } } } \author{Michel Lang, Martin Morgan and Valerie Obenchain} \examples{ ## ----------------------------------------------------------------------- ## Catch errors: ## ----------------------------------------------------------------------- ## By default 'stop.on.error' is TRUE in BiocParallelParam objects. If ## 'stop.on.error' is TRUE an ill-fated bplapply() simply stops, ## displaying the error message. param <- SnowParam(workers = 2, stop.on.error = TRUE) tryCatch({ bplapply(list(1, "two", 3), sqrt, BPPARAM = param) }, error=identity) ## If 'stop.on.error' is FALSE then the computation continues. Errors ## are signalled but the full evaluation can be retrieved param <- SnowParam(workers = 2, stop.on.error = FALSE) X <- list(1, "two", 3) result <- bptry(bplapply(X, sqrt, BPPARAM = param)) result ## Check for errors: fail <- !bpok(result) fail ## Access the traceback with attr(): tail(attr(result[[2]], "traceback"), 5) ## ----------------------------------------------------------------------- ## Resume calculations: ## ----------------------------------------------------------------------- ## The 'resume' mechanism is triggered by supplying a list of partial ## results as 'BPREDO'. Data elements that failed are rerun and merged ## with previous results. ## A call of sqrt() on the character "2" returns an error. Fix the input ## data by changing the character "2" to a numeric 2: X_mod <- list(1, 2, 3) bplapply(X_mod, sqrt, BPPARAM = param , BPREDO = result) } BiocParallel/man/bpschedule.Rd0000644000175200017520000000224314136050065017306 0ustar00biocbuildbiocbuild\name{bpschedule} \alias{bpschedule} \alias{bpschedule,missing-method} \alias{bpschedule,ANY-method} \title{Schedule back-end Params} \description{ Use functions on this page to influence scheduling of parallel processing. } \usage{ bpschedule(x) } \arguments{ \item{x}{ An instance of a \code{BiocParallelParam} class, e.g., \code{\link{MulticoreParam}}, \code{\link{SnowParam}}, \code{\link{DoparParam}}. \code{x} can be missing, in which case the default back-end (see \code{\link{register}}) is used. } } \details{ \code{bpschedule} returns a logical(1) indicating whether the parallel evaluation should occur at this point. } \value{ \code{bpschedule} returns a scalar logical. } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org}. } \seealso{ \code{\link{BiocParallelParam}} for possible values of \code{x}. } \examples{ bpschedule(SnowParam()) # TRUE bpschedule(MulticoreParam(2)) # FALSE on windows p <- MulticoreParam() bpschedule(p) # TRUE bplapply(1:2, function(i, p) { bpschedule(p) # FALSE }, p = p, BPPARAM=p) } \keyword{manip} BiocParallel/man/bptry.Rd0000644000175200017520000000365714136050065016342 0ustar00biocbuildbiocbuild\name{bptry} \alias{bptry} \title{Try expression evaluation, recovering from bperror signals} \description{ This function is meant to be used as a wrapper around \code{bplapply()} and friends, returning the evaluated expression rather than signalling an error. } \usage{ bptry(expr, ..., bplist_error, bperror) } \arguments{ \item{expr}{An R expression; see \code{\link{tryCatch}}.} \item{bplist_error}{ A \sQuote{handler} function of a single argument, used to catch \code{bplist_error} conditions signalled by \code{expr}. A \code{bplist_error} condition is signalled when an element of \code{bplapply} and other iterations contain a evaluation that failed. When missing, the default retrieves the \dQuote{result} attribute from the error, containing the partially evaluated results. Setting \code{bplist_error=identity} returns the evaluated condition. Setting \code{bplist_error=stop} passes the condition to other handlers, notably the handler provided by \code{bperror}. } \item{bperror}{ A \sQuote{handler} function of a single argument, use to catch \code{bperror} conditions signalled by \code{expr}. A \code{bperror} is a base class to all errors signaled by \pkg{BiocParallel} code. When missing, the default returns the condition without signalling an error. } \item{\dots}{ Additional named handlers passed to \code{tryCatch()}. These user-provided handlers are evaluated before default handlers \code{bplist_error}, \code{bperror}. } } \value{ The partially evaluated list of results. } \author{Martin Morgan \email{martin.morgan@roswellpark.org}} \seealso{ \code{\link{tryCatch}}, \code{\link{bplapply}}. } \examples{ param = registered()[[1]] param X = list(1, "2", 3) bptry(bplapply(X, sqrt)) # bplist_error handler bptry(bplapply(X, sqrt), bplist_error=identity) # bperror handler } \keyword{manip} BiocParallel/man/bpvalidate.Rd0000644000175200017520000001210414136050065017300 0ustar00biocbuildbiocbuild\name{bpvalidate} \alias{bpvalidate} \alias{BPValidate-class} \alias{show,BPValidate-method} \title{Tools for developing functions for parallel execution in distributed memory} \description{ \code{bpvalidate} interrogates the function environment and search path to locate undefined symbols. } \usage{ bpvalidate(fun, signal = c("warning", "error", "silent")) } \arguments{ \item{fun}{The function to be checked. \code{typeof(fun)} must return either \code{"closure"} or \code{"builtin"}.} \item{signal}{\code{character(1)} matching \code{"warning", "error", "silent"} or a function with signature \code{(..., call.)} to be invoked when reporting errors. Using \code{"silent"} suppresses output; \code{"warning"} and \code{"error"} emit warnings or errors when \code{fun} contains references to unknown variables or variables defined in the global environment (and hence not serialized to workers).} } \details{ \code{bpvalidate} tests if a function can be run in a distributed memory environment (e.g., SOCK clusters, Windows machines). \code{bpvalidate} looks in the environment of \code{fun}, in the NAMESPACE exports of libraries loaded in \code{fun}, and along the search path to identify any symbols outside the scope of \code{fun}. \code{bpvalidate} can be used to check functions passed to the bp* family of functions in \code{BiocParallel} or other packages that support parallel evaluation on clusters such as \code{snow}, \code{BatchJobs}, \code{Rmpi}, etc. \describe{ \item{testing package functions}{ The environment of a function defined inside a package is the NAMESPACE of the package. It is important to test these functions as they will be called from within the package, with the appropriate environment. Specifically, do not copy/paste the function into the workspace; once this is done the GlobalEnv becomes the function environment. To test a package function, load the package then call the function by name (myfun) or explicitly (mypkg:::myfun) if not exported. } \item{testing workspace functions}{ The environment of a function defined in the workspace is the GlobalEnv. Because these functions do not have an associated package NAMESPACE, the functions and variables used in the body must be explicitly passed or defined. See examples. Defining functions in the workspace is often done during development or testing. If the function is later moved inside a package, it can be rewritten in a more lightweight form by taking advantage of imported symbols in the package NAMESPACE. } } NOTE: \code{bpvalidate} does not currently work on Generics. } \value{ An object of class \code{BPValidate} summarizing symbols identified in the global environment or search path, or undefined in the enviornments the function was defined in. Details are only available via `show()`. } \author{ Martin Morgan \url{mailto:mtmorgan.bioc@gmail.com} and Valerie Obenchain. } \examples{ ## --------------------------------------------------------------------- ## Interactive use ## --------------------------------------------------------------------- fun <- function() .__UNKNOWN_SYMBOL__ bpvalidate(fun, "silent") ## --------------------------------------------------------------------- ## Testing package functions ## --------------------------------------------------------------------- \dontrun{ library(myPkg) ## Test exported functions by name or the double colon: bpvalidate(myExportedFun) bpvalidate(myPkg::myExportedFun) ## Non-exported functions are called with the triple colon: bpvalidate(myPkg:::myInternalFun) } ## --------------------------------------------------------------------- ## Testing workspace functions ## --------------------------------------------------------------------- ## Functions defined in the workspace have the .GlobalEnv as their ## environment. Often the symbols used inside the function body ## are not defined in .GlobalEnv and must be passed explicitly. ## Loading libraries: ## In 'fun1' countBam() is flagged as unknown: fun1 <- function(fl, ...) countBam(fl) v <- bpvalidate(fun1) ## countBam() is not defined in .GlobalEnv and must be passed as ## an argument or made available by loading the library. fun2 <- function(fl, ...) { Rsamtools::countBam(fl) } v <- bpvalidate(fun2) ## Passing arguments: ## 'param' is defined in the workspace but not passed to 'fun3'. ## bpvalidate() flags 'param' as being found '.GlobalEnv' which means ## it is not defined in the function environment or inside the function. library(Rsamtools) param <- ScanBamParam(flag=scanBamFlag(isMinusStrand=FALSE)) fun3 <- function(fl, ...) { Rsamtools::countBam(fl, param=param) } v <- bpvalidate(fun3) ## 'param' is explicitly passed by adding it as a formal argument. fun4 <- function(fl, ..., param) { Rsamtools::countBam(fl, param=param) } bpvalidate(fun4) ## The corresponding call to a bp* function includes 'param': \dontrun{ bplapply(files, fun4, param=param, BPPARAM=SnowParam(2)) } } \keyword{manip} BiocParallel/man/bpvec.Rd0000644000175200017520000001020714136050065016266 0ustar00biocbuildbiocbuild\name{bpvec} \alias{bpvec} \alias{bpvec,ANY,missing-method} \alias{bpvec,ANY,list-method} \alias{bpvec,ANY,BiocParallelParam-method} \title{Parallel, vectorized evaluation} \description{ \code{bpvec} applies \code{FUN} to subsets of \code{X}. Any type of object \code{X} is allowed, provided \code{length}, and \code{[} are defined on \code{X}. \code{FUN} is a function such that \code{length(FUN(X)) == length(X)}. The objects returned by \code{FUN} are concatenated by \code{AGGREGATE} (\code{c()} by default). The return value is \code{FUN(X)}. } \usage{ bpvec(X, FUN, ..., AGGREGATE=c, BPREDO=list(), BPPARAM=bpparam()) } \arguments{ \item{X}{ Any object for which methods \code{length} and \code{[} are implemented. } \item{FUN}{ A function to be applied to subsets of \code{X}. The relationship between \code{X} and \code{FUN(X)} is 1:1, so that \code{length(FUN(X, ...)) == length(X)}. The return value of separate calls to \code{FUN} are concatenated with \code{AGGREGATE}. } \item{\dots}{Additional arguments for \code{FUN}. } \item{AGGREGATE}{A function taking any number of arguments \code{...} called to reduce results (elements of the \code{...} argument of \code{AGGREGATE} from parallel jobs. The default, \code{c}, concatenates objects and is appropriate for vectors; \code{rbind} might be appropriate for data frames. } \item{BPPARAM}{ An optional \code{\link{BiocParallelParam}} instance determining the parallel back-end to be used during evaluation, or a \code{list} of \code{BiocParallelParam} instances, to be applied in sequence for nested calls to \pkg{BiocParallel} functions. } \item{BPREDO}{A \code{list} of output from \code{bpvec} with one or more failed elements. When a list is given in \code{BPREDO}, \code{bpok} is used to identify errors, tasks are rerun and inserted into the original results. } } \details{ This method creates a vector of indices for \code{X} that divide the elements as evenly as possible given the number of \code{bpworkers()} and \code{bptasks()} of \code{BPPARAM}. Indices and data are passed to \code{bplapply} for parallel evaluation. The distinction between \code{bpvec} and \code{bplapply} is that \code{bplapply} applies \code{FUN} to each element of \code{X} separately whereas \code{bpvec} assumes the function is vectorized, e.g., \code{c(FUN(x[1]), FUN(x[2]))} is equivalent to \code{FUN(x[1:2])}. This approach can be more efficient than \code{bplapply} but requires the assumption that \code{FUN} takes a vector input and creates a vector output of the same length as the input which does not depend on partitioning of the vector. This behavior is consistent with \code{parallel:::pvec} and the \code{?pvec} man page should be consulted for further details. } \value{ The result should be identical to \code{FUN(X, ...)} (assuming that \code{AGGREGATE} is set appropriately). When evaluation of individual elements of \code{X} results in an error, the result is a \code{list} with the same geometry (i.e., \code{lengths()}) as the split applied to \code{X} to create chunks for parallel evaluation; one or more elements of the list contain a \code{bperror} element, indicting that the vectorized calculation failed for at least one of the index values in that chunk. An error is also signaled when \code{FUN(X)} does not return an object of the same length as \code{X}; this condition is only detected when the number of elements in \code{X} is greater than the number of workers. } \author{Martin Morgan \url{mailto:mtmorgan@fhcrc.org}.} \seealso{ \code{\link{bplapply}} for parallel lapply. \code{\link{BiocParallelParam}} for possible values of \code{BPPARAM}. \code{\link{pvec}} for background. } \examples{ methods("bpvec") ## ten tasks (1:10), called with as many back-end elements are specified ## by BPPARAM. Compare with bplapply fun <- function(v) { message("working") sqrt(v) } system.time(result <- bpvec(1:10, fun)) result ## invalid FUN -- length(class(X)) is not equal to length(X) bptry(bpvec(1:2, class, BPPARAM=SerialParam())) } \keyword{manip} BiocParallel/man/bpvectorize.Rd0000644000175200017520000000440014136050065017521 0ustar00biocbuildbiocbuild\name{bpvectorize} \alias{bpvectorize} \alias{bpvectorize,ANY,ANY-method} \alias{bpvectorize,ANY,missing-method} \title{Transform vectorized functions into parallelized, vectorized function} \description{ This transforms a vectorized function into a parallel, vectorized function. Any function \code{FUN} can be used, provided its parallelized argument (by default, the first argument) has a \code{length} and \code{[} method defined, and the return value of \code{FUN} can be concatenated with \code{c}. } \usage{ bpvectorize(FUN, ..., BPREDO=list(), BPPARAM=bpparam()) \S4method{bpvectorize}{ANY,ANY}(FUN, ..., BPREDO=list(), BPPARAM=bpparam()) \S4method{bpvectorize}{ANY,missing}(FUN, ..., BPREDO=list(), BPPARAM=bpparam()) } \arguments{ \item{FUN}{A function whose first argument has a \code{length} and can be subset \code{[}, and whose evaluation would benefit by splitting the argument into subsets, each one of which is independently transformed by \code{FUN}. The return value of \code{FUN} must support concatenation with \code{c}. } \item{...}{Additional arguments to parallization, unused. } \item{BPPARAM}{An optional \code{\link{BiocParallelParam}} instance determining the parallel back-end to be used during evaluation. } \item{BPREDO}{A \code{list} of output from \code{bpvectorize} with one or more failed elements. When a list is given in \code{BPREDO}, \code{bpok} is used to identify errors, tasks are rerun and inserted into the original results. } } \details{ The result of \code{bpvectorize} is a function with signature \code{\dots}; arguments to the returned function are the original arguments \code{FUN}. \code{BPPARAM} is used for parallel evaluation. When \code{BPPARAM} is a class for which no method is defined (e.g., \code{\link{SerialParam}}), \code{FUN(X)} is used. See \code{methods{bpvectorize}} for additional methods, if any. } \value{ A function taking the same arguments as \code{FUN}, but evaluated using \code{\link{bpvec}} for parallel evaluation across available cores. } \author{ Ryan Thompson \url{mailto:rct@thompsonclan.org} } \seealso{\code{bpvec}} \examples{ psqrt <- bpvectorize(sqrt) ## default parallelization psqrt(1:10) } \keyword{interface} BiocParallel/man/ipcmutex.Rd0000644000175200017520000000663714136050065017041 0ustar00biocbuildbiocbuild\name{ipcmutex} \alias{ipclocked} \alias{ipclock} \alias{ipctrylock} \alias{ipcunlock} \alias{ipcid} \alias{ipcremove} \alias{ipcyield} \alias{ipcvalue} \alias{ipcreset} \title{Inter-process locks and counters} \description{ Functions documented on this page enable locks and counters between processes on the \emph{same} computer. Use \code{ipcid()} to generate a unique mutex or counter identifier. A mutex or counter with the same \code{id}, including those in different processes, share the same state. \code{ipcremove()} removes external state associated with mutex or counters created with \code{id}. \code{ipclock()} blocks until the lock is obtained. \code{ipctrylock()} tries to obtain the lock, returning immediately if it is not available. \code{ipcunlock()} releases the lock. \code{ipclocked()} queries the lock to determine whether it is currently held. \code{ipcyield()} returns the current counter, and increments the value for subsequent calls. \code{ipcvalue()} returns the current counter without incrementing. \code{ipcreset()} sets the counter to \code{n}, such that the next call to \code{ipcyield()} or \code{ipcvalue()} returns \code{n}. } \usage{ ## Utilities ipcid(id) ipcremove(id) ## Locks ipclock(id) ipctrylock(id) ipcunlock(id) ipclocked(id) ## Counters ipcyield(id) ipcvalue(id) ipcreset(id, n = 1) } \arguments{ \item{id}{character(1) identifier string for mutex or counter. \code{ipcid()} ensures that the identifier is universally unique.} \item{n}{integer(1) value from which \code{ipcyield()} will increment.} } \value{ Locks: \code{ipclock()} creates a named lock, returning \code{TRUE} on success. \code{trylock()} returns \code{TRUE} if the lock is obtained, \code{FALSE} otherwise. \code{ipcunlock()} returns \code{TRUE} on success, \code{FALSE} (e.g., because there is nothing to unlock) otherwise. \code{ipclocked()} returns \code{TRUE} when \code{id} is locked, and \code{FALSE} otherwise. Counters: \code{ipcyield()} returns an integer(1) value representing the next number in sequence. The first value returned is 1. \code{ipcvalue()} returns the value to be returned by the next call to \code{ipcyield()}, without incrementing the counter. If the counter is no longer available, \code{ipcyield()} returns \code{NA}. \code{ipcreset()} returns \code{n}, invisibly. Utilities: \code{ipcid()} returns a character(1) unique identifier, with \code{id} (if not missing) prepended. \code{ipcremove()} returns (invisibly) \code{TRUE} if external resources were released or \code{FALSE} if not (e.g., because the resources has already been released). } \examples{ ipcid() ## Locks id <- ipcid() ipclock(id) ipctrylock(id) ipcunlock(id) ipctrylock(id) ipclocked(id) ipcremove(id) id <- ipcid() system.time({ ## about 1s, .2s for each process instead of .2s if no lock result <- bplapply(1:2, function(i, id) { BiocParallel::ipclock(id) Sys.sleep(.2) time <- Sys.time() BiocParallel::ipcunlock(id) time }, id) }) ipcremove(id) diff(sort(unlist(result, use.names=FALSE))) ## Counters id <- ipcid() ipcyield(id) ipcyield(id) ipcvalue(id) ipcyield(id) ipcreset(id, 10) ipcvalue(id) ipcyield(id) ipcremove(id) id <- ipcid() result <- bplapply(1:2, function(i, id) { BiocParallel::ipcyield(id) }, id) ipcremove(id) sort(unlist(result, use.names=FALSE)) } BiocParallel/man/register.Rd0000644000175200017520000001024614136050065017016 0ustar00biocbuildbiocbuild\name{register} \alias{register} \alias{registered} \alias{bpparam} \title{Maintain a global registry of available back-end Params} \description{ Use functions on this page to add to or query a registry of back-ends, including the default for use when no \code{BPPARAM} object is provided to functions. } \usage{ register(BPPARAM, default=TRUE) registered(bpparamClass) bpparam(bpparamClass) } \arguments{ \item{BPPARAM}{ An instance of a \code{BiocParallelParam} class, e.g., \code{\link{MulticoreParam}}, \code{\link{SnowParam}}, \code{\link{DoparParam}}. } \item{default}{ Make this the default \code{BiocParallelParam} for subsequent evaluations? If \code{FALSE}, the argument is placed at the lowest priority position. } \item{bpparamClass}{ When present, the text name of the \code{BiocParallelParam} class (e.g., \dQuote{MulticoreParam}) to be retrieved from the registry. When absent, a list of all registered instances is returned. } } \details{ The registry is a list of back-ends with configuration parameters for parallel evaluation. The first list entry is the default and is used by \code{BiocParallel} functions when no \code{BPPARAM} argument is supplied. At load time the registry is populated with default backends. On Windows these are \code{SnowParam} and \code{SerialParam} and on non-Windows \code{MulticoreParam}, \code{SnowParam} and \code{SerialParam}. When \code{snowWorkers()} or \code{multicoreWorkers} returns a single core, only \code{SerialParm} is registered. The \code{\link{BiocParallelParam}} objects are constructed from global options of the corresponding name, or from the default constructor (e.g., \code{SnowParam()}) if no option is specified. The user can set customizations during start-up (e.g., in an \code{.Rprofile} file) with, for instance, \code{options(MulticoreParam=quote(MulticoreParam(workers=8)))}. The act of \dQuote{registering} a back-end modifies the existing \code{\link{BiocParallelParam}} in the list; only one param of each type can be present in the registry. When \code{default=TRUE}, the newly registered param is moved to the top of the list thereby making it the default. When \code{default=FALSE}, the param is modified `in place` vs being moved to the top. \code{bpparam()}, invoked with no arguments, returns the default \code{\link{BiocParallelParam}} instance from the registry. When called with the text name of a \code{bpparamClass}, the global options are consulted first, e.g., \code{options(MulticoreParam=MulticoreParam())} and then the value of \code{registered(bpparamClass)}. } \value{ \code{register} returns, invisibly, a list of registered back-ends. \code{registered} returns the back-end of type \code{bpparamClass} or, if \code{bpparamClass} is missing, a list of all registered back-ends. \code{bpparam} returns the back-end of type \code{bpparamClass} or, } \author{ Martin Morgan \url{mailto:mtmorgan@fhcrc.org}. } \seealso{ \code{\link{BiocParallelParam}} for possible values of \code{BPPARAM}. } \examples{ ## ---------------------------------------------------------------------- ## The registry ## ---------------------------------------------------------------------- ## The default registry. default <- registered() default ## When default = TRUE the last param registered becomes the new default. snowparam <- SnowParam(workers = 3, type = "SOCK") register(snowparam, default = TRUE) registered() ## Retrieve the default back-end, bpparam() ## or a specific BiocParallelParam. bpparam("SnowParam") ## restore original registry -- push the defaults in reverse order for (param in rev(default)) register(param) ## ---------------------------------------------------------------------- ## Specifying a back-end for evaluation ## ---------------------------------------------------------------------- ## The back-end of choice is given as the BPPARAM argument to ## the BiocParallel functions. None, one, or multiple back-ends can be ## used. bplapply(1:6, sqrt, BPPARAM = MulticoreParam(3)) ## When not specified, the default from the registry is used. bplapply(1:6, sqrt) } \keyword{manip} BiocParallel/src/0000755000175200017520000000000014154342106014714 5ustar00biocbuildbiocbuildBiocParallel/src/Makevars.in0000644000175200017520000000002214136050065017007 0ustar00biocbuildbiocbuildPKG_LIBS = @LIBS@ BiocParallel/src/Makevars.ucrt0000644000175200017520000000002214136050065017356 0ustar00biocbuildbiocbuildPKG_LIBS=-lbcrypt BiocParallel/src/ipcmutex.cpp0000644000175200017520000001055114136050065017260 0ustar00biocbuildbiocbuild#define BOOST_NO_AUTO_PTR #include #include static boost::uuids::random_generator uuid_generator; std::string uuid_generate() { return boost::uuids::to_string(uuid_generator()); } #include #include using namespace boost::interprocess; class IpcMutex { protected: managed_shared_memory *shm; private: interprocess_mutex *mtx; bool *locked; public: IpcMutex(const char *id) { shm = new managed_shared_memory{open_or_create, id, 1024}; mtx = shm->find_or_construct("mtx")(); locked = shm->find_or_construct("locked")(); } ~IpcMutex() { delete shm; } bool is_locked() { return *locked; } bool lock() { mtx->lock(); *locked = true; return *locked; } bool try_lock() { *locked = mtx->try_lock(); return *locked; } bool unlock() { mtx->unlock(); *locked = false; return *locked; } }; class IpcCounter : IpcMutex { private: int *i; public: IpcCounter(const char *id) : IpcMutex(id) { i = shm->find_or_construct("i")(); } ~IpcCounter() {} int value() { return *i + 1; } int reset(int n) { lock(); *i = n - 1; unlock(); return n; } int yield() { int result; lock(); result = ++(*i); unlock(); return result; } }; #include // internal const char *ipc_id(SEXP id_sexp) { bool test = IS_SCALAR(id_sexp, STRSXP) && (R_NaString != STRING_ELT(id_sexp, 0)); if (!test) Rf_error("'id' must be character(1) and not NA"); return CHAR(STRING_ELT(id_sexp, 0)); } int ipc_n(SEXP n_sexp) { PROTECT(n_sexp = Rf_coerceVector(n_sexp, INTSXP)); bool test = IS_SCALAR(n_sexp, INTSXP) && (R_NaInt != Rf_asInteger(n_sexp)); if (!test) Rf_error("'n' cannot be coerced to integer(1) and not NA"); int n = INTEGER(n_sexp)[0]; UNPROTECT(1); return n; } // utilities SEXP ipc_remove(SEXP id_sexp) { const char *id = ipc_id(id_sexp); bool status = shared_memory_object::remove(id); return Rf_ScalarLogical(status); } // uuid SEXP ipc_uuid() { std::string uuid = uuid_generate(); return Rf_mkString(uuid.c_str()); } // mutex SEXP ipc_locked(SEXP id_sexp) { IpcMutex mutex = IpcMutex(ipc_id(id_sexp)); bool status = mutex.is_locked(); return Rf_ScalarLogical(status); } SEXP ipc_lock(SEXP id_sexp) { IpcMutex mutex = IpcMutex(ipc_id(id_sexp)); mutex.lock(); return Rf_ScalarLogical(true); } SEXP ipc_try_lock(SEXP id_sexp) { IpcMutex mutex = IpcMutex(ipc_id(id_sexp)); bool status = mutex.try_lock(); return Rf_ScalarLogical(status); } SEXP ipc_unlock(SEXP id_sexp) { IpcMutex mutex = IpcMutex(ipc_id(id_sexp)); bool status = mutex.unlock(); return Rf_ScalarLogical(status); } // count SEXP ipc_value(SEXP id_sexp) { IpcCounter cnt = IpcCounter(ipc_id(id_sexp)); return Rf_ScalarInteger(cnt.value()); } SEXP ipc_reset(SEXP id_sexp, SEXP n_sexp) { IpcCounter cnt = IpcCounter(ipc_id(id_sexp)); int n = ipc_n(n_sexp); return Rf_ScalarInteger(cnt.reset(n)); } SEXP ipc_yield(SEXP id_sexp) { IpcCounter cnt = IpcCounter(ipc_id(id_sexp)); return Rf_ScalarInteger(cnt.yield()); } // expose to R #include extern "C" { static const R_CallMethodDef callMethods[] = { // uuid {".ipc_uuid", (DL_FUNC) & ipc_uuid, 0}, // lock {".ipc_lock", (DL_FUNC) & ipc_lock, 1}, {".ipc_try_lock", (DL_FUNC) & ipc_try_lock, 1}, {".ipc_unlock", (DL_FUNC) & ipc_unlock, 1}, {".ipc_locked", (DL_FUNC) & ipc_locked, 1}, // counter {".ipc_yield", (DL_FUNC) & ipc_yield, 1}, {".ipc_value", (DL_FUNC) & ipc_value, 1}, {".ipc_reset", (DL_FUNC) & ipc_reset, 2}, // cleanup {".ipc_remove", (DL_FUNC) & ipc_remove, 1}, {NULL, NULL, 0} }; void R_init_BiocParallel(DllInfo *info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); R_useDynamicSymbols(info, FALSE); } void R_unload_BiocParallel(DllInfo *info) { (void) info; } } BiocParallel/tests/0000755000175200017520000000000014136050065015267 5ustar00biocbuildbiocbuildBiocParallel/tests/test.R0000644000175200017520000000005314136050065016367 0ustar00biocbuildbiocbuildBiocGenerics:::testPackage("BiocParallel") BiocParallel/vignettes/0000755000175200017520000000000014154342106016135 5ustar00biocbuildbiocbuildBiocParallel/vignettes/BiocParallel_BatchtoolsParam.Rnw0000644000175200017520000002231514136050065024324 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{2. Introduction to BatchtoolsParam} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ <>= suppressPackageStartupMessages({ library(BiocParallel) }) @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Introduction to \emph{BatchtoolsParam}} \author{ Nitesh Turaga\footnote{\url{Nitesh.Turaga@RoswellPark.org}}, Martin Morgan\footnote{\url{Martin.Morgan@RoswellPark.org}} } \date{Edited: March 22, 2018; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The \Rcode{BatchtoolsParam} class is an interface to the \CRANpkg{batchtools} package from within \BiocParallel{}. This aims to replace BatchjobsParam as \BiocParallel{}'s class for computing on a high performance cluster such as SGE, TORQUE, LSF, SLURM, OpenLava. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Quick start} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This example demonstrates the easiest way to launch a 100000 jobs using batchtools. The first step involves creating a \Rcode{BatchtoolsParam} class. You can compute using 'bplapply' and then the result is stored. <>= library(BiocParallel) ## Pi approximation piApprox <- function(n) { nums <- matrix(runif(2 * n), ncol = 2) d <- sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } piApprox(1000) ## Apply piApprox over param <- BatchtoolsParam() result <- bplapply(rep(10e5, 10), piApprox, BPPARAM=param) mean(unlist(result)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\emph{BatchtoolsParam} interface} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The \Rcode{BatchtoolsParam} interface allows you to replace the BatchJobsParam interface, and allow more intuitive usage of your high performance cluster with \BiocParallel{}. The \Rcode{BatchtoolsParam} class allows the user to specify many arguments to customize their jobs. Applicable to clusters with formal schedulers. \begin{itemize} \item{\Rcode{workers}} The number of workers used by the job. \item{\Rcode{cluster}} We currently support, SGE, SLURM, LSF, TORQUE and OpenLava. The 'cluster' argument is supported only if the R environment knows how to find the job scheduler. Each cluster type uses a template to pass the job to the scheduler. If the template is not given we use the default templates as given in the 'batchtools' package. The cluster can be accessed by 'bpbackend(param)'. \item{\Rcode{registryargs}} The 'registryargs' argument takes a list of arguments to create a new job registry for you \Rcode{BatchtoolsParam}. The job registry is a data.table which stores all the required information to process your jobs. The arguments we support for registryargs are: \begin{description} \item{\Rcode{file.dir}} Path where all files of the registry are saved. Note that some templates do not handle relative paths well. If nothing is given, a temporary directory will be used in your current working directory. \item{\Rcode{work.dir}} Working directory for R process for running jobs. \item{\Rcode{packages}} Packages that will be loaded on each node. \item{\Rcode{namespaces}} Namespaces that will be loaded on each node. \item{\Rcode{source}} Files that are sourced before executing a job. \item{\Rcode{load}} Files that are loaded before executing a job. \end{description} <<>>= registryargs <- batchtoolsRegistryargs( file.dir = "mytempreg", work.dir = getwd(), packages = character(0L), namespaces = character(0L), source = character(0L), load = character(0L) ) param <- BatchtoolsParam(registryargs = registryargs) param @ \item{\Rcode{resources}} A named list of key-value pairs to be subsituted into the template file; see \Rcode{?batchtools::submitJobs}. \item{\Rcode{template}} The template argument is unique to the \Rcode{BatchtoolsParam} class. It is required by the job scheduler. It defines how the jobs are submitted to the job scheduler. If the template is not given and the cluster is chosen, a default template is selected from the batchtools package. \item{\Rcode{log}} The log option is logical, TRUE/FALSE. If it is set to TRUE, then the logs which are in the registry are copied to directory given by the user using the \Rcode{logdir} argument. \item{\Rcode{logdir}} Path to the logs. It is given only if \Rcode{log=TRUE}. \item{\Rcode{resultdir}} Path to the directory is given when the job has files to be saved in a directory. \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Defining templates} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The job submission template controls how the job is processed by the job scheduler on the cluster. Obviously, the format of the template will differ depending on the type of job scheduler. Let's look at the default SLURM template as an example: <<>>= fname <- batchtoolsTemplate("slurm") cat(readLines(fname), sep="\n") @ The \Rcode{<\%= =>} blocks are automatically replaced by the values of the elements in the \Rcode{resources} argument in the \Rcode{BatchtoolsParam} constructor. Failing to specify critical parameters properly (e.g., wall time or memory limits too low) will cause jobs to crash, usually rather cryptically. We suggest setting parameters explicitly to provide robustness to changes to system defaults. Note that the \Rcode{<\%= =>} blocks themselves do not usually need to be modified in the template. The part of the template that is most likely to require explicit customization is the last line containing the call to \Rcode{Rscript}. A more customized call may be necessary if the R installation is not standard, e.g., if multiple versions of R have been installed on a cluster. For example, one might use instead: \begin{verbatim} echo 'batchtools::doJobCollection("<%= uri %>")' |\ ArbitraryRcommand --no-save --no-echo \end{verbatim} If such customization is necessary, we suggest making a local copy of the template, modifying it as required, and then constructing a \Rcode{BiocParallelParam} object with the modified template using the \Rcode{template} argument. However, we find that the default templates accessible with \Rcode{batchtoolsTemplate} are satisfactory in most cases. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Use cases} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% As an example for a BatchtoolParam job being run on an SGE cluster, we use the same \Rcode{piApprox} function as defined earlier. The example runs the function on 5 workers and submits 100 jobs to the SGE cluster. Example of SGE with minimal code: <>= library(BiocParallel) ## Pi approximation piApprox <- function(n) { nums <- matrix(runif(2 * n), ncol = 2) d <- sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } template <- system.file( package = "BiocParallel", "unitTests", "test_script", "test-sge-template.tmpl" ) param <- BatchtoolsParam(workers=5, cluster="sge", template=template) ## Run parallel job result <- bplapply(rep(10e5, 100), piApprox, BPPARAM=param) @ Example of SGE demonstrating some of \Rcode{BatchtoolsParam} methods. <>= library(BiocParallel) ## Pi approximation piApprox <- function(n) { nums <- matrix(runif(2 * n), ncol = 2) d <- sqrt(nums[, 1]^2 + nums[, 2]^2) 4 * mean(d <= 1) } template <- system.file( package = "BiocParallel", "unitTests", "test_script", "test-sge-template.tmpl" ) param <- BatchtoolsParam(workers=5, cluster="sge", template=template) ## start param bpstart(param) ## Display param param ## To show the registered backend bpbackend(param) ## Register the param register(param) ## Check the registered param registered() ## Run parallel job result <- bplapply(rep(10e5, 100), piApprox) bpstop(param) @ \section{\Rcode{sessionInfo()}} <>= toLatex(sessionInfo()) @ \end{document} BiocParallel/vignettes/Errors_Logs_And_Debugging.Rnw0000644000175200017520000005031514136050065023626 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{3. Errors, Logs and Debugging} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Errors, Logs and Debugging in \BiocParallel} \author{Valerie Obenchain and Martin Morgan} \date{Edited: December 16, 2015; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This vignette is part of the \BiocParallel{} package and focuses on error handling and logging. A section at the end demonstrates how the two can be used together as part of an effective debugging routine. \BiocParallel{} provides a unified interface to the parallel infrastructure in several packages including \CRANpkg{snow}, \CRANpkg{parallel}, \CRANpkg{batchtools} and \CRANpkg{foreach}. When implementing error handling in \BiocParallel{} the primary goals were to enable the return of partial results when an error is thrown (vs just the error) and to establish logging on the workers. In cases where error handling existed, such as \CRANpkg{batchtools} and \CRANpkg{foreach}, those behaviors were preserved. Clusters created with \CRANpkg{snow} and \CRANpkg{parallel} now have flexible error handling and logging available through \Rcode{SnowParam} and \Rcode{MulticoreParam} objects. In this document the term ``job'' is used to describe a single call to a bp*apply function (e.g., the \Rcode{X} in \Rcode{bplapply}). A ``job'' consists of one or more ``tasks'', where each ``task'' is run separately on a worker. The \Rpackage{BiocParallel} package is available at bioconductor.org and can be downloaded via \Rcode{BiocManager::install}: <>= if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("BiocParallel") @ Load the package: <>= library(BiocParallel) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Error Handling} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Messages and warnings} \BiocParallel{} captures messages and warnings in each job, returning the output to the manager and reporting these to the user after the completion of the entire operation. Thus <>= res <- bplapply(1:2, function(i) { message(i); Sys.sleep(3) }) @ %% reports messages only after the entire \Rcode{bplapply()} is complete. It may be desired to output messages immediatly. Do this using \Rcode{sink()}, as in the following example: <>= res <- bplapply(1:2, function(i) { sink(NULL, type = "message") message(i) Sys.sleep(3) }) @ %% This could be confusing when multiple workers write messages at the same time --the messages will be interleaved in an arbitrary way -- or when the workers are not all running on the same computer (e.g., with \Rcode{SnowParam()} or \Rcode{BatchjobsParaam()}) so should not be used in package code. \subsection{Catching errors} By default, \BiocParallel{} attempts all computations and returns any warnings and errors along with successful results. The \Rcode{stop.on.error} field controls if the job is terminated as soon as one task throws an error. This is useful when debugging or when running large jobs (many tasks) and you want to be notified of an error before all runs complete. \Rcode{stop.on.error} is \Rcode{TRUE} by default. <>= param <- SnowParam() param @ The field can be set when constructing the param or modified with the \Rcode{bpstopOnError} accessor. <>= param <- SnowParam(2, stop.on.error = TRUE) param bpstopOnError(param) <- FALSE @ In this example \Rcode{X} is length 6. By default, the elements of \Rcode{X} are divided as evenly as possible over the number of workers and run in chunks. The number of tasks is set equal to the length of \Rcode{X} which forces each element of \Rcode{X} to be executed separately (6 tasks). <>= X <- list(1, "2", 3, 4, 5, 6) param <- SnowParam(3, tasks = length(X), stop.on.error = TRUE) @ Tasks 1, 2, and 3 are assigned to the three workers, and are evaluated. Task 2 fails, stopping further computation. All successfully completed tasks are returned as the `result' attribute. Usually, this means that the results of tasks 1, 2, and 3 will be returned. <>= res <- tryCatch({ bplapply(X, sqrt, BPPARAM = param) }, error=identity) res attr(res, "result") @ Using \Rcode{stop.on.error=FALSE}, all tasks are evaluated. <>= X <- list("1", 2, 3, 4, 5, 6) param <- SnowParam(3, tasks = length(X), stop.on.error = FALSE) res <- tryCatch({ bplapply(X, sqrt, BPPARAM = param) }, error=identity) res attr(res, "result") @ \Rcode{bptry()} is a convenient way of trying to evaluate a \Rcode{bpapply}-like expression, returning the evaluated results without signalling an error. <>= bptry({ bplapply(X, sqrt, BPPARAM=param) }) @ In the next example the elements of \Rcode{X} are grouped instead of run separately. The default value for \Rcode{tasks} is 0 which means 'X' is split as evenly as possible across the number of workers. There are 3 workers so the first task consists of list(1, 2), the second is list("3", 4) and the third is list(5, 6). <>= X <- list(1, 2, "3", 4, 5, 6) param <- SnowParam(3, stop.on.error = TRUE) @ The output shows an error in when evaluating the third element, but also that the fourth element, in the same chunk as 3, was not evaluated. All elements are evaluated because they were assigned to workers before the first error occurred. <>= bptry(bplapply(X, sqrt, BPPARAM = param)) @ Side Note: Results are collected from workers as they finish which is not necessarily the same order in which they were loaded. Depending on how tasks are divided it is possible that the task with the error completes after all others so essentially all workers complete before the job is stopped. In this situation the output includes all results along with the error message and it may appear that \Rcode{stop.on.error=TRUE} did not stop the job soon enough. This is just a heads up that the usefulness of \Rcode{stop.on.error=TRUE} may vary with run time and distribution of tasks over workers. \subsection{Identify failures with \Rcode{bpok()}} The \Rcode{bpok()} function is a quick way to determine which (if any) tasks failed. In this example we use \Rcode{bptry()} to retrieve the partially evaluated expression, including the failed elements. <>= param <- SnowParam(2, stop.on.error=FALSE) result <- bptry(bplapply(list(1, "2", 3), sqrt, BPPARAM=param)) @ \Rcode{bpok} returns TRUE if the task was successful. <>= bpok(result) @ Once errors are identified with \Rcode{bpok} the traceback can be retrieved with the \Rcode{attr} function. This is possible because errors are returned as \Rcode{condition} objects with the traceback as an attribute. <>= tail(attr(result[[which(!bpok(result))]], "traceback")) @ \subsection{Rerun failed tasks with \Rcode{BPREDO}} Tasks can fail due to hardware problems or bugs in the input data. The \BiocParallel{} functions support a \Rcode{BPREDO} (re-do) argument for recomputing only the tasks that failed. A list of partial results and errors is supplied to \Rcode{BPREDO} in a second call to the function. The failed elements are identified, recomputed and inserted into the original results. The bug in this example is the second element of 'X' which is a character when it should be numeric. <>= X <- list(1, "2", 3) param <- SnowParam(2, stop.on.error=FALSE) result <- bptry(bplapply(X, sqrt, BPPARAM=param)) result @ First fix the input data. <>= X.redo <- list(1, 2, 3) @ Repeat the call to \Rcode{bplapply} this time supplying the partial results as \Rcode{BPREDO}. Only the failed calculations are computed, in the present case requiring only one worker. <>= bplapply(X.redo, sqrt, BPREDO=result, BPPARAM=param) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Logging} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% NOTE: Logging as described in this section is supported for SnowParam, MulticoreParam and SerialParam. \subsection{Parameters} Logging in \BiocParallel{} is controlled by 3 fields in the \Rcode{BiocParallelParam}: \begin{verbatim} log: TRUE or FALSE logdir: location to write log file threshold: one of "TRACE", "DEBUG", "INFO", "WARN", "ERROR", "FATAL" \end{verbatim} When \Rcode{log = TRUE} the \CRANpkg{futile.logger} package is loaded on each worker. \BiocParallel{} uses a custom script on the workers to collect log messages as well as additional statistics such as gc, runtime and node information. Output to stderr and stdout is also captured. By default \Rcode{log} is FALSE and \Rcode{threshold} is {\it INFO}. <>= param <- SnowParam(stop.on.error=FALSE) param @ Turn logging on and set the threshold to {\it TRACE}. <>= bplog(param) <- TRUE bpthreshold(param) <- "TRACE" param @ \subsection{Setting a threshold} All thresholds defined in \CRANpkg{futile.logger} are supported: {\it FATAL}, {\it ERROR}, {\it WARN}, {\it INFO}, {\it DEBUG} and {\it TRACE}. All messages greater than or equal to the severity of the threshold are shown. For example, a threshold of {\it INFO} will print all messages tagged as {\it FATAL}, {\it ERROR}, {\it WARN} and {\it INFO}. Because the default threshold is {\it INFO} it catches the {\it ERROR}-level message thrown when attempting the square root of a character ("2"). <>= tryCatch({ bplapply(list(1, "2", 3), sqrt, BPPARAM = param) }, error=function(e) invisible(e)) @ All user-supplied messages written in the \CRANpkg{futile.logger} syntax are also captured. This function performs argument checking and includes a couple of {\it WARN} and {\it DEBUG}-level messages. <>= FUN <- function(i) { futile.logger::flog.debug(paste("value of 'i':", i)) if (!length(i)) { futile.logger::flog.warn("'i' has length 0") NA } else if (!is(i, "numeric")) { futile.logger::flog.debug("coercing 'i' to numeric") as.numeric(i) } else { i } } @ Turn logging on and set the threshold to {\it WARN}. <>= param <- SnowParam(2, log = TRUE, threshold = "WARN", stop.on.error=FALSE) result <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) simplify2array(result) @ Changing the threshold to {\it DEBUG} catches both {\it WARN} and {\it DEBUG} messages. <>= param <- SnowParam(2, log = TRUE, threshold = "DEBUG", stop.on.error=FALSE) result <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) simplify2array(result) @ \subsection{Log files} When \Rcode{log == TRUE}, log messages are written to the console by default. If \Rcode{logdir} is given the output is written out to files, one per task. File names are prefixed with the name in \Rcode{bpjobname(BPPARAM)}; default is 'BPJOB'. \begin{verbatim} param <- SnowParam(2, log = TRUE, threshold = "DEBUG", logdir = tempdir()) res <- bplapply(list(1, "2", integer()), FUN, BPPARAM = param) ## loading futile.logger on workers list.files(bplogdir(param)) ## [1] "BPJOB.task1.log" "BPJOB.task2.log" \end{verbatim} Read in BPJOB.task2.log: \begin{verbatim} readLines(paste0(bplogdir(param), "/BPJOB.task2.log")) ## [1] "############### LOG OUTPUT ###############" ## [2] "Task: 2" ## [3] "Node: 2" ## [4] "Timestamp: 2015-07-08 09:03:59" ## [5] "Success: TRUE" ## [6] "Task duration: " ## [7] " user system elapsed " ## [8] " 0.009 0.000 0.011 " ## [9] "Memory use (gc): " ## [10] " used (Mb) gc trigger (Mb) max used (Mb)" ## [11] "Ncells 325664 17.4 592000 31.7 393522 21.1" ## [12] "Vcells 436181 3.4 1023718 7.9 530425 4.1" ## [13] "Log messages:" ## [14] "DEBUG [2015-07-08 09:03:59] value of 'i': 2" ## [15] "INFO [2015-07-08 09:03:59] coercing to numeric" ## [16] "DEBUG [2015-07-08 09:03:59] value of 'i': " ## [17] "WARN [2015-07-08 09:03:59] 'i' is missing" ## [18] "" ## [19] "stderr and stdout:" ## [20] "character(0)" \end{verbatim} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Worker timeout} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% NOTE: \Rcode{timeout} is supported for SnowParam and MulticoreParam. For long running jobs or untested code it can be useful to set a time limit. The \Rcode{timeout} field is the time, in seconds, allowed for each worker to complete a task; default is \Rcode{Inf}. If the task takes longer than \Rcode{timeout} a timeout error is returned. Time can be changed during param construction with the \Rcode{timeout} arg, <>= param <- SnowParam(timeout = 20, stop.on.error=FALSE) param @ or with the \Rcode{bptimeout} setter: <>= param <- SnowParam(timeout = 2, stop.on.error=FALSE) fun <- function(i) { Sys.sleep(i) i } bptry(bplapply(1:3, fun, BPPARAM = param)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Debugging} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Effective debugging strategies vary by problem and often involve a combination of error handling and logging techniques. In general, when debugging \R{}-generated errors the traceback is often the best place to start followed by adding debug messages to the worker function. When trouble shooting unexpected behavior (i.e., not a formal error or warning) adding debug messages or switching to \Rcode{SerialParam} are good approaches. Below is an overview of these different strategies. \subsection{Accessing the traceback} The traceback is a good place to start when tracking down \R{}-generated errors. Because the function is executed on the workers it's not accessible for interactive debugging with functions such as \Rcode{trace} or \Rcode{debug}. The traceback provides a snapshot of the state of the worker at the time the error was thrown. This function takes the square root of the absolute value of a vector. <>= fun1 <- function(x) { v <- abs(x) sapply(1:length(v), function(i) sqrt(v[i])) } @ Calling ``fun1'' with a character throws an error: \begin{verbatim} param <- SnowParam(stop.on.error=FALSE) result <- bptry({ bplapply(list(c(1,3), 5, "6"), fun1, BPPARAM = param) }, error=identity) result ## [[1]] ## [1] 1.000000 1.732051 ## ## [[2]] ## [1] 2.236068 ## ## [[3]] ## ## traceback() available as 'attr(x, "traceback")' \end{verbatim} Identify which elements failed with \Rcode{bpok}: \begin{verbatim} bpok(result) ## [1] TRUE TRUE FALSE \end{verbatim} The error (i.e., third element of ``res'') is a \Rcode{condition} object: \begin{verbatim} is(result[[3]], "condition") ## [1] TRUE \end{verbatim} The traceback is an attribute of the \Rcode{condition} and can be accessed with the \Rcode{attr} function. \begin{verbatim} noquote(tail(attr(result[[3]], "traceback"))) ## [1] call <- sapply(sys.calls(), deparse) ## [2] e <- structure(e, class = c("remote_error", "condition"), ## [3] traceback = capture.output(traceback(call))) ## [4] invokeRestart("abort", e) ## [5] }, "non-numeric argument to mathematical function", quote(abs(x))) ## [6] 1: h(simpleError(msg, call)) \end{verbatim} \subsection{Adding debug messages} When a \Rcode{numeric()} is passed to ``fun1'' no formal error is thrown but the length of the second list element is 2 when it should be 1. \begin{verbatim} bplapply(list(c(1,3), numeric(), 6), fun1, BPPARAM = param) ## [[1]] ## [1] 1.000000 1.732051 ## ## [[2]] ## [[2]][[1]] ## [1] NA ## ## [[2]][[2]] ## numeric(0) ## ## [[3]] ## [1] 2.44949 \end{verbatim} Without a formal error we have no traceback so we'll add a few debug messages. The \CRANpkg{futile.logger} syntax tags messages with different levels of severity. A message created with \Rcode{flog.debug} will only print if the threshold is {\it DEBUG} or lower. So in this case it will catch both INFO and DEBUG messages. ``fun2'' has debug statements that show the value of `x', length of `v' and the index `i'. <>= fun2 <- function(x) { v <- abs(x) futile.logger::flog.debug( paste0("'x' = ", paste(x, collapse=","), ": length(v) = ", length(v)) ) sapply(1:length(v), function(i) { futile.logger::flog.info(paste0("'i' = ", i)) sqrt(v[i]) }) } @ Create a param that logs at a threshold level of {\it DEBUG}. <>= param <- SnowParam(3, log = TRUE, threshold = "DEBUG") @ The debug messages reveal the problem occurs when `x' is \Rcode{numeric()}. The index for \Rcode{sapply} is along `v' which in this case has length 0. This forces `i' to take values of `1' and `0' giving an output of length 2 for the second element (i.e., \Rcode{NA} and \Rcode{numeric(0)}). <>= res <- bplapply(list(c(1,3), numeric(), 6), fun2, BPPARAM = param) res @ ``fun2'' can be fixed by using \Rcode{seq\_along(v)} to create the index instead of \Rcode{1:length(v)}. \subsection{Local debugging with \Rcode{SerialParam}} Errors that occur on parallel workers can be difficult to debug. Often the traceback sent back from the workers is too much to parse or not informative. We are also limited in that our interactive strategies of \Rcode{browser} and \Rcode{trace} are not available. One option for further debugging is to run the code in serial with \Rcode{SerialParam}. This removes the ``parallel'' component and is the same as running a straight \Rcode{*apply} function. This approach may not help if the problem was hardware related but can be very useful when the bug is in the \R{} code. We use the now familiar square root example with a bug in the second element of \Rcode{X}. <>= res <- bptry({ bplapply(list(1, "2", 3), sqrt, BPPARAM = SnowParam(3, stop.on.error=FALSE)) }) result @ \Rcode{sqrt} is an internal function. The problem is likely with our data going into the function and not the \Rcode{sqrt} function itself. We can write a small wrapper around \Rcode{sqrt} so we can see the input. <>= fun3 <- function(i) sqrt(i) @ Debug the new function: \begin{verbatim} debug(fun3) \end{verbatim} We want to recompute only elements that failed and for that we use the \Rcode{BPREDO} argument. The BPPARAM has been changed to \Rcode{SerialParam} so the job is run in the local workspace in serial. \begin{verbatim} > bplapply(list(1, "2", 3), fun3, BPREDO = result, BPPARAM = SerialParam()) Resuming previous calculation ... debugging in: FUN(...) debug: sqrt(i) Browse[2]> objects() [1] "i" Browse[2]> i [1] "2" Browse[2]> \end{verbatim} The local browsing allowed us to see the problem input was the character "2". %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= toLatex(sessionInfo()) @ \end{document} BiocParallel/vignettes/Introduction_To_BiocParallel.Rnw0000644000175200017520000007356314136050065024377 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{1. Introduction to BiocParallel} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ <>= suppressPackageStartupMessages({ library(BiocParallel) library(VariantAnnotation) library(GenomicAlignments) library(RNAseqData.HNRNPC.bam.chr14) library(TxDb.Hsapiens.UCSC.hg19.knownGene) }) @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Introduction to \BiocParallel} \author{Valerie Obenchain, Vincent Carey, Michael Lawrence, Martin Morgan\footnote{\url{Martin.Morgan@RoswellPark.org}}} \date{Edited: January 23, 2021; Compiled: \today} \begin{document} \maketitle \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Numerous approaches are available for parallel computing in \R{}. The CRAN Task View for high performance and parallel computing provides useful high-level summaries and package categorization. \url{http://cran.r-project.org/web/views/HighPerformanceComputing.html} Most Task View packages cite or identify one or more of \CRANpkg{snow}, \CRANpkg{Rmpi}, \CRANpkg{multicore} or \CRANpkg{foreach} as relevant parallelization infrastructure. Direct support in \R{} for parallel computing started with release 2.14.0 with inclusion of the \CRANpkg{parallel} package which contains modified versions of \CRANpkg{multicore} and \CRANpkg{snow}. A basic objective of \BiocParallel{} is to reduce the complexity faced when developing and using software that performs parallel computations. With the introduction of the \Rcode{BiocParallelParam} object, \BiocParallel{} aims to provide a unified interface to existing parallel infrastructure where code can be easily executed in different environments. The \Rcode{BiocParallelParam} specifies the environment of choice as well as computing resources and is invoked by `registration` or passed as an argument to the \BiocParallel{} functions. \BiocParallel{} offers the following conveniences over the `roll your own` approach to parallel programming. \begin{itemize} \setlength{\itemsep}{5pt} \item{unified interface:}{ \Rcode{BiocParallelParam} instances define the method of parallel evaluation (multi-core, snow cluster, etc.) and computing resources (number of workers, error handling, cleanup, etc.). } \item{parallel iteration over lists, files and vectorized operations:}{ \Rcode{bplapply}, \Rcode{bpmapply} and \Rcode{bpvec} provide parallel list iteration and vectorized operations. \Rcode{bpiterate} iterates through files distributing chunks to parallel workers. } \item{cluster scheduling:}{ When the parallel environment is managed by a cluster scheduler through \CRANpkg{batchtools}, job management and result retrieval are considerably simplified. } \item{support of \Rcode{foreach}:}{ The \CRANpkg{foreach} and \CRANpkg{iterators} packages are fully supported. Registration of the parallel back end uses \Rcode{BiocParallelParam} instances. } \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Quick start} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The \Rpackage{BiocParallel} package is available at bioconductor.org and can be downloaded via \Rcode{BiocManager}: <>= if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("BiocParallel") @ Load \BiocParallel{}. <>= library(BiocParallel) @ The test function simply returns the square root of ``x''. <>= FUN <- function(x) { round(sqrt(x), 4) } @ Functions in \BiocParallel use the registered back-ends for parallel evaluation. The default is the top entry of the registry list. <>= registered() @ %% Configure your R session to always use a particular back-end configure by setting options named after the back ends in an \Rcode{.Rprofile} file, e.g., <>= options(MulticoreParam=quote(MulticoreParam(workers=4))) @ When a \BiocParallel{} function is invoked with no \Rcode{BPPARAM} argument the default back-end is used. <>= bplapply(1:4, FUN) @ Environment specific back-ends can be defined for any of the registry entries. This example uses a 2-worker SOCK cluster. <>= param <- SnowParam(workers = 2, type = "SOCK") bplapply(1:4, FUN, BPPARAM = param) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{The \BiocParallel{} Interface} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Classes} \subsubsection{\Rcode{BiocParallelParam}} \Rcode{BiocParallelParam} instances configure different parallel evaluation environments. Creating or \Rcode{register()}ing a `\Rcode{Param}` allows the same code to be used in different parallel environments without a code re-write. Params listed are supported on all of Unix, Mac and Windows except \Rcode{MulticoreParam} which is Unix and Mac only. \begin{itemize} \setlength{\itemsep}{5pt} \item{\Rcode{SerialParam}: } Supported on all platforms. Evaluate \BiocParallel-enabled code with parallel evaluation disabled. This approach is useful when writing new scripts and trying to debug code. \item{\Rcode{MulticoreParam}: } Supported on Unix and Mac. On Windows, \Rcode{MulticoreParam} dispatches to \Rcode{SerialParam}. Evaluate \BiocParallel-enabled code using multiple cores on a single computer. When available, this is the most efficient and least troublesome way to parallelize code. Windows does not support multi-core evaluation (the \Rcode{MulticoreParam} object can be used, but evaluation is serial). On other operating systems, the default number of workers equals the value of the global option \Rcode{mc.cores} (e.g., \Rcode{getOption("mc.cores")}) or, if that is not set, the number of cores returned by \Rcode{parallel::detectCores() - 2}; when number of cores cannot be determined, the default is 1. \Rcode{MulticoreParam} uses 'forked' processes with 'copy-on-change' semantics -- memory is only copied when it is changed. This makes it very efficient to invoke compared to other back-ends. There are several important caveats to using \Rcode{MulticoreParam}. Forked processes are not available on Windows. Some environments, e.g., \emph{RStudio}, do not work well with forked processes, assuming that \R{} code evaluation is single-threaded. Some external resources, e.g., access to files or data bases, maintain state in a way that assumes the resource is accessed only by a single thread. A subtle cost is that \R's garbage collector runs periodically, and 'marks' memory as in use. This effectively triggers a copy of the marked memory. \R's generational garbage collector is triggered at difficult-to-predict times; the effect in a long-running forked process is that the memory is eventually copied. See \href{https://support.bioconductor.org/p/70196/#70509}{this post} for additional details. \Rcode{MulticoreParam} is based on facilities originally implemented in the \CRANpkg{multicore} package and subsequently the \CRANpkg{parallel} package in base \R{}. \item{\Rcode{SnowParam}: } Supported on all platforms. Evaluate \BiocParallel-enabled code across several distinct \R{} instances, on one or several computers. This is a straightforward approach for executing parallel code on one or several computers, and is based on facilities originally implemented in the \CRANpkg{snow} package. Different types of \CRANpkg{snow} `back-ends' are supported, including socket and MPI clusters. \item{\Rcode{BatchtoolsParam}: } Applicable to clusters with formal schedulers. Evaluate \BiocParallel-enabled code by submitting to a cluster scheduler like SGE. \item{\Rcode{DoparParam}: } Supported on all platforms. Register a parallel back-end supported by the \CRANpkg{foreach} package for use with \BiocParallel. \end{itemize} The simplest illustration of creating \Rcode{BiocParallelParam} is <>= serialParam <- SerialParam() serialParam @ Most parameters have additional arguments influencing behavior, e.g., specifying the number of `cores' to use when creating a \Rcode{MulticoreParam} instance <>= multicoreParam <- MulticoreParam(workers = 8) multicoreParam @ Arguments are described on the corresponding help page, e.g., \Rcode{?MulticoreParam}. \subsubsection{\Rcode{register()}ing \Rcode{BiocParallelParam} instances} The list of registered \Rcode{BiocParallelParam} instances represents the user's preferences for different types of back-ends. Individual algorithms may specify a preferred back-end, and different back-ends maybe chosen when parallel evaluation is nested. The registry behaves like a `stack' in that the last entry registered is added to the top of the list and becomes the ``next used`` (i.e., the default). \Rcode{registered} invoked with no arguments lists all back-ends. <>= registered() @ \Rcode{bpparam} returns the default from the top of the list. <>= bpparam() @ Add a specialized instance with \Rcode{register}. When \Rcode{default} is TRUE, the new instance becomes the default. <>= default <- registered() register(BatchtoolsParam(workers = 10), default = TRUE) @ BatchtoolsParam has been moved to the top of the list and is now the default. <>= names(registered()) bpparam() @ Restore the original registry <>= for (param in rev(default)) register(param) @ \subsection{Functions} \subsubsection{Parallel looping, vectorized and aggregate operations} These are used in common functions, implemented as much as possible for all back-ends. The functions (see the help pages, e.g., \Rcode{?bplapply} for a full definition) include \begin{description} \item{\Rcode{bplapply(X, FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to each element of \Rcode{X}. \Rcode{bplapply} invokes \Rcode{FUN} \Rcode{length(X)} times, each time with a single element of \Rcode{X}. \item{\Rcode{bpmapply(FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to the first, second, etc., elements of each argument in \ldots. \item{\Rcode{bpiterate(ITER, FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to the output of function \Rcode{ITER}. Data chunks are returned by \Rcode{ITER} and distributed to parallel workers along with \Rcode{FUN}. Intended for iteration though an undefined number of data chunks (i.e., records in a file). \item{\Rcode{bpvec(X, FUN, ...)}: } Apply in parallel a function \Rcode{FUN} to subsets of \Rcode{X}. \Rcode{bpvec} invokes function \Rcode{FUN} as many times as there are cores or cluster nodes, with \Rcode{FUN} receiving a subset (typically more than 1 element, in contrast to \Rcode{bplapply}) of \Rcode{X}. \item{\Rcode{bpaggregate(x, data, FUN, ...)}: } Use the formula in \Rcode{x} to aggregate \Rcode{data} using \Rcode{FUN}. \end{description} \subsubsection{Parallel evaluation environment} These functions query and control the state of the parallel evaluation environment. \begin{description} \item{\Rcode{bpisup(x)}: } Query a \Rcode{BiocParallelParam} back-end \Rcode{x} for its status. \item{\Rcode{bpworkers}; \Rcode{bpnworkers}: } Query a \Rcode{BiocParallelParam} back-end for the number of workers available for parallel evaluation. \item{\Rcode{bptasks}: } Divides a job (e.g., single call to *lapply function) into tasks. Applicable to \Rcode{MulticoreParam} only; \Rcode{DoparParam} and \Rcode{BatchtoolsParam} have their own approach to dividing a job among workers. \item{\Rcode{bpstart(x)}: } Start a parallel back end specified by \Rcode{BiocParallelParam} \Rcode{x}, if possible. \item{\Rcode{bpstop(x)}: } Stop a parallel back end specified by \Rcode{BiocParallelParam} \Rcode{x}. \end{description} \subsubsection{Error handling and logging} Logging and advanced error recovery is available in \Rcode{BiocParallel} 1.1.25 and later. For a more details see the vignette titled "Error Handling and Logging": <>= browseVignettes("BiocParallel") @ \subsubsection{Locks and counters} Inter-process (i.e., single machine) locks and counters are supported using \Rcode{ipclock()}, \Rcode{ipcyield()}, and friends. Use these to synchronize computation, e.g., allowing only a single process to write to a file at a time. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Use cases} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Sample data are BAM files from a transcription profiling experiment available in the \Rpackage{RNAseqData.HNRNPC.bam.chr14} package. <>= library(RNAseqData.HNRNPC.bam.chr14) fls <- RNAseqData.HNRNPC.bam.chr14_BAMFILES @ \subsection{Single machine} Common approaches on a single machine are to use multiple cores in forked processes, or to use clusters of independent processes. For purely \R-based computations on non-Windows computers, there are substantial benefits, such as shared memory, to be had using forked processes. However, this approach is not portable across platforms, and fails when code uses functionality, e.g., file or data base access, that assumes only a single thread is accessing the resource. While use of forked processes with \Rcode{MulticoreParam} is an attractive solution for scripts using pure \R{} functionality, robust and complex code often requires use of independent processes and \Rcode{SnowParam}. \subsubsection{Forked processes with \Rcode{MulticoreParam}} This example counts overlaps between BAM files and a defined set of ranges. First create a GRanges with regions of interest (in practice this could be large). <>= library(GenomicAlignments) ## for GenomicRanges and readGAlignments() gr <- GRanges("chr14", IRanges((1000:3999)*5000, width=1000)) @ A \Rclass{ScanBamParam} defines regions to extract from the files. <>= param <- ScanBamParam(which=range(gr)) @ FUN counts overlaps between the ranges in `gr` and the files. <>= FUN <- function(fl, param) { gal <- readGAlignments(fl, param = param) sum(countOverlaps(gr, gal)) } @ All parameters necessary for running a job in a multi-core environment are specified in the \Rclass{MulticoreParam} instance. <>= MulticoreParam() @ The \BiocParallel{} functions, such as \Rfunction{bplapply}, use information in the \Rclass{MulticoreParam} to set up the appropriate back-end and pass relevant arguments to low-level functions. \begin{verbatim} > bplapply(fls[1:3], FUN, BPPARAM = MulticoreParam(), param = param) $ERR127306 [1] 1185 $ERR127307 [1] 1123 $ERR127308 [1] 1241 \end{verbatim} Shared memory environments eliminate the need to pass large data between workers or load common packages. Note that in this code the GRanges data was not passed to all workers in \Rcode{bplapply} and FUN did not need to load \Biocpkg{GenomicAlignments} for access to the \Rcode{readGAlignments} function. Problems with forked processes occur when code implementating functionality used by the workers is not written in anticipation of use by forked processes. One example is the database connection underlying Bioconductor's \Rcode{org.*} packages. This psudoe-code <>= library(org.Hs.eg.db) FUN <- function(x, ...) { ... mapIds(org.Hs.eg.db, ...) ... } bplapply(X, FUN, ..., BPPARAM = MulticoreParam()) @ %% is likely to fail, because \Rcode{library(org.Hs.eg.db)} opens a database connection that is accessed by multiple processes. A solution is to ensure that the database is opened independently in each process <>= FUN <- function(x, ...) { library(org.Hs.eg.db) ... mapIds(org.Hs.eg.db, ...) ... } bplapply(X, FUN, ..., BPPARAM = MulticoreParam()) @ \subsubsection{Clusters of independent processes with \Rcode{SnowParam}} Both Windows and non-Windows machines can use the cluster approach to spawn processes. \BiocParallel{} back-end choices for clusters on a single machine are \Rclass{SnowParam} for configuring a Snow cluster or the \Rclass{DoparParam} for use with the \Rpackage{foreach} package. To re-run the counting example, FUN needs to modified such that `gr` is passed as a formal argument and required libraries are loaded on each worker. (In general, this is not necessary for functions defined in a package name space, see Section~\ref{sec:developers}.) <>= FUN <- function(fl, param, gr) { suppressPackageStartupMessages({ library(GenomicAlignments) }) gal <- readGAlignments(fl, param = param) sum(countOverlaps(gr, gal)) } @ Define a 2-worker SOCK Snow cluster. <>= snow <- SnowParam(workers = 2, type = "SOCK") @ A call to \Rcode{bplapply} with the \Rclass{SnowParam} creates the cluster and distributes the work. <>= bplapply(fls[1:3], FUN, BPPARAM = snow, param = param, gr = gr) @ The FUN written for the cluster adds some overhead due to the passing of the GRanges and the loading of \Biocpkg{GenomicAlignments} on each worker. This approach, however, has the advantage that it works on most platforms and does not require a coding change when switching between windows and non-windows machines. If several \Rcode{bplapply()} statements are likely to require the same resource, it often makes sense to create a cluster once using \Rcode{bpstart()}. The workers are re-used by each call to \Rcode{bplapply()}, so they do not have to re-load packages, etc. <>= register(SnowParam()) # default evaluation bpstart() # start the cluster ... bplapply(X, FUN1, ...) ... bplapply(X, FUN2, ...) # re-use workers ... bpstop() @ \subsection{\emph{Ad hoc} cluster of multiple machines} We use the term \emph{ad hoc} cluster to define a group of machines that can communicate with each other and to which the user has password-less log-in access. This example uses a group of compute machines ("the rhinos") on the FHCRC network. \subsubsection{Sockets} On Linux and Mac OS X, a socket cluster is created across machines by supplying machine names as the \Rcode{workers} argument to a \Rclass{BiocParallelParam} instance instead of a number. Each name represents an \R{} process; repeat names indicate multiple workers on the same machine. Create a \Rclass{SnowParam} with 2 cpus from `rhino01` and 1 from `rhino02`. <>= hosts <- c("rhino01", "rhino01", "rhino02") param <- SnowParam(workers = hosts, type = "SOCK") @ Execute FUN 4 times across the workers. \begin{verbatim} > FUN <- function(i) system("hostname", intern=TRUE) > bplapply(1:4, FUN, BPPARAM = param) [[1]] [1] "rhino01" [[2]] [1] "rhino01" [[3]] [1] "rhino02" [[4]] [1] "rhino01" \end{verbatim} When creating a cluster across Windows machines \Rcode{workers} must be IP addresses (e.g., "140.107.218.57") instead of machine names. \subsubsection{MPI} An MPI cluster across machines is created with \emph{mpirun} or \emph{mpiexec} from the command line or a script. A list of machine names provided as the -hostfile argument defines the mpi universe. The hostfile requests 2 processors on 3 different machines. \begin{verbatim} rhino01 slots=2 rhino02 slots=2 rhino03 slots=2 \end{verbatim} From the command line, start a single interactive \R{} process on the current machine. \begin{verbatim} mpiexec --np 1 --hostfile hostfile R --vanilla \end{verbatim} Load \BiocParallel{} and create an MPI Snow cluster. The number of \Rcode{workers} in \Rclass{SnowParam} should match the number of slots requested in the hostfile. Using a smaller number of workers uses a subset of the slots. \begin{verbatim} > library(BiocParallel) > param <- SnowParam(workers = 6, type = "MPI") \end{verbatim} Execute FUN 6 times across the workers. \begin{verbatim} > FUN <- function(i) system("hostname", intern=TRUE) > bplapply(1:6, FUN, BPPARAM = param) bplapply(1:6, FUN, BPPARAM = param) 6 slaves are spawned successfully. 0 failed. [[1]] [1] "rhino01" [[2]] [1] "rhino02" [[3]] [1] "rhino02" [[4]] [1] "rhino03" [[5]] [1] "rhino03" [[6]] [1] "rhino01" \end{verbatim} Batch jobs can be launched with mpiexec and R CMD BATCH. Code to be executed is in `Rcode.R`. \begin{verbatim} mpiexec --hostfile hostfile R CMD BATCH Rcode.R \end{verbatim} \subsection{Clusters with schedulers} Computer clusters are far from standardized, so the following may require significant adaptation; it is written from experience here at FHCRC, where we have a large cluster managed via SLURM. Nodes on the cluster have shared disks and common system images, minimizing complexity about making data resources available to individual nodes. There are two simple models for use of the cluster, Cluster-centric and R-centric. \subsubsection{Cluster-centric} The idea is to use cluster management software to allocate resources, and then arrange for an \R{} script to be evaluated in the context of allocated resources. NOTE: Depending on your cluster configuration it may be necessary to add a line to the template file instructing workers to use the version of R on the master / head node. Otherwise the default R on the worker nodes will be used. For SLURM, we might request space for 4 tasks (with \verb+salloc+ or \verb+sbatch+), arrange to start the MPI environment (with \verb+orterun+) and on a single node in that universe run an \R{} script \verb+BiocParallel-MPI.R+. The command is \begin{verbatim} $ salloc -N 4 orterun -n 1 R -f BiocParallel-MPI.R \end{verbatim} The \R{} script might do the following, using MPI for parallel evaluation. Start by loading necessary packages and defining \Rcode{FUN} work to be done <>= library(BiocParallel) library(Rmpi) FUN <- function(i) system("hostname", intern=TRUE) @ %% Create a \Rclass{SnowParam} instance with the number of nodes equal to the size of the MPI universe minus 1 (let one node dispatch jobs to workers), and register this instance as the default <>= param <- SnowParam(mpi.universe.size() - 1, "MPI") register(param) @ %% Evaluate the work in parallel, process the results, clean up, and quit <>= xx <- bplapply(1:100, FUN) table(unlist(xx)) mpi.quit() @ %% The entire session is as follows: \begin{knitrout} \definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe} \begin{verbatim} $ salloc -N 4 orterun -n 1 R --vanilla -f BiocParallel-MPI.R salloc: Job is in held state, pending scheduler release salloc: Pending job allocation 6762292 salloc: job 6762292 queued and waiting for resources salloc: job 6762292 has been allocated resources salloc: Granted job allocation 6762292 ## ... > FUN <- function(i) system("hostname", intern=TRUE) > > library(BiocParallel) > library(Rmpi) > param <- SnowParam(mpi.universe.size() - 1, "MPI") > register(param) > xx <- bplapply(1:100, FUN) 4 slaves are spawned successfully. 0 failed. > table(unlist(xx)) gizmof13 gizmof71 gizmof86 gizmof88 25 25 25 25 > > mpi.quit() salloc: Relinquishing job allocation 6762292 salloc: Job allocation 6762292 has been revoked. \end{verbatim} \end{kframe} \end{knitrout} One advantage of this approach is that the responsibility for managing the cluster lies firmly with the cluster management software -- if one wants more nodes, or needs special resources, then adjust parameters to \verb+salloc+ (or \verb+sbatch+). Notice that workers are spawned within the \Rcode{bplapply} function; it might often make sense to more explicitly manage workers with \Rfunction{bpstart} and \Rfunction{bpstop}, e.g., <>= param <- bpstart(SnowParam(mpi.universe.size() - 1, "MPI")) register(param) xx <- bplapply(1:100, FUN) bpstop(param) mpi.quit() @ \subsubsection{R-centric} A more \R-centric approach might start an \R{} script on the head node, and use \Rpackage{batchtools} to submit jobs from within the \R{} session. One way of doing this is to create a file containing a template for the job submission step, e.g., for SLURM; a starting point might be found at <>= tmpl <- system.file(package="batchtools", "templates", "slurm-simple.tmpl") noquote(readLines(tmpl)) @ The \R{} script, run interactively or from the command line, might then look like <>= ## define work to be done FUN <- function(i) system("hostname", intern=TRUE) library(BiocParallel) ## register SLURM cluster instructions from the template file param <- BatchtoolsParam(workers=5, cluster="slurm", template=tmpl) register(param) ## do work xx <- bplapply(1:100, FUN) table(unlist(xx)) @ %% The code runs on the head node until \Rcode{bplapply}, where the \R{} script interacts with the SLURM scheduler to request a SLURM allocation, run jobs, and retrieve results. The argument \Rcode{4} to \Rcode{BatchtoolsParam} specifies the number of workers to request from the scheduler; \Rcode{bplapply} divides the 100 jobs among the 4 workers. If \Rcode{BatchtoolsParam} had been created without specifying any workers, then 100 jobs implied by the argument to \Rcode{bplapply} would be associated with 100 tasks submitted to the scheduler. Because cluster tasks are running in independent \R{} instances, and often on physically separate machines, a convenient `best practice' is to write \Rcode{FUN} in a `functional programming' manner, such that all data required for the function is passed in as arguments or (for large data) loaded implicitly or explicitly (e.g., via an \R{} library) from disk. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Analyzing genomic data in \Bioconductor{}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% General strategies exist for handling large genomic data that are well suited to \R{} programs. A manuscript titled \emph{Scalable Genomics with \R{} and \Bioconductor{}} (\url{http://arxiv.org/abs/1409.2864}) by Michael Lawrence and Martin Morgan, reviews several of these approaches and demonstrate implementation with \Bioconductor{} packages. Problem areas include scalable processing, summarization and visualization. The techniques presented include restricting queries, compressing data, iterating, and parallel computing. Ideas are presented in an approachable fashion within a framework of common use cases. This is a benificial read for anyone anyone tackling genomics problems in \R{}. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{For developers} \label{sec:developers} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Developers wishing to use \BiocParallel{} in their own packages should include \BiocParallel{} in the \texttt{DESCRIPTION} file \begin{verbatim} Imports: BiocParallel \end{verbatim} and import the functions they wish to use in the \texttt{NAMESPACE} file, e.g., \begin{verbatim} importFrom(BiocParallel, bplapply) \end{verbatim} Then invoke the desired function in the code, e.g., <>= system.time(x <- bplapply(1:3, function(i) { Sys.sleep(i); i })) unlist(x) @ %% This will use the back-end returned by \Rcode{bpparam()}, by default a \Rcode{MulticoreParam()} instance or the user's preferred back-end if they have used \Rcode{register()}. The \Rcode{MulticoreParam} back-end does not require any special configuration or set-up and is therefore the safest option for developers. Unfortunately, \Rcode{MulticoreParam} provides only serial evaluation on Windows. Developers should document that their function uses \BiocParallel{} functions on the man page, and should perhaps include in their function signature an argument \Rcode{BPPARAM=bpparam()}. Developers should NOT use `register()` in package code -- this sets a preference that influences use of `bplapply()` and friends in all packages, not just their package. Developers wishing to invoke back-ends other than \Rcode{MulticoreParam}, or to write code that works across Windows, macOS and Linux, need to take special care to ensure that required packages, data, and functions are available and loaded on the remote nodes. In \Rcode{bplapply()}, the environment of \Rcode{FUN} (other than the global environment) is serialized to the workers. A consequence is that, when \Rcode{FUN} is inside a package name space, other functions available in the name space are available to \Rcode{FUN} on the workers. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rcode{sessionInfo()}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% <>= toLatex(sessionInfo()) @ \end{document} BiocParallel/vignettes/Random_Numbers.Rnw0000644000175200017520000002450214136050065021543 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{4. Random Numbers in BiocParallel} %\VignetteKeywords{parallel, Infrastructure} %\VignettePackage{BiocParallel} %\VignetteEngine{knitr::knitr} \documentclass{article} <>= BiocStyle::latex() @ <>= suppressPackageStartupMessages({ library(BiocParallel) }) @ \newcommand{\BiocParallel}{\Biocpkg{BiocParallel}} \title{Random Numbers in BiocParallel} \author{Martin Morgan\footnote{\url{Martin.Morgan@RoswellPark.org}}} \date{Edited: 7 September, 2021; Compiled: \today} \begin{document} \maketitle \tableofcontents \section{Scope} \BiocParallel{} enables use of random number streams in a reproducible manner. This document applies to the following \Rcode{*Param()}: \begin{itemize} \item \Rcode{SerialParam()}: sequential evaluation in a single \emph{R} process. \item \Rcode{SnowParam()}: parallel evaluation in multiple independent \emph{R} processes. \item \Rcode{MulticoreParam())}: parallel evaluation in \emph{R} sessions running in forked threads. Not available on Windows. \end{itemize} The \Rcode{*Param()} can be used for evaluation with: \begin{itemize} \item \Rcode{bplapply()}: \Rcode{lapply()}-like application of a user-supplied function \Rcode{FUN} to a vector or list of elements \Rcode{X}. \item \Rcode{bpiterate()}: apply a user-supplied function \Rcode{FUN} to an unknown number of elements resulting from successive calls to a user-supplied function \Rcode{ITER}. \end{itemize} The reproducible random number implementation also supports: \begin{itemize} \item \Rcode{bptry()} and the \Rcode{BPREDO=} argument, for re-evaluation of elements that fail (e.g., because of a bug in \Rcode{FUN}). \end{itemize} \section{Essentials} \subsection{Use of \Rcode{bplapply()} and \Rcode{RNGseed=}} Attach \BiocParallel{} and ensure that the version is greater than 1.27.5 <>= library(BiocParallel) stopifnot( packageVersion("BiocParallel") > "1.27.5" ) @ For reproducible calculation, use the \Rcode{RNGseed=} argument in any of the \Rcode{*Param()} constructors. <>= result1 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) result1 @ Repeating the calculation with the same value for \Rcode{RNGseed=} results in the same result; a different random number seed results in different results. <>= result2 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) stopifnot( identical(result1, result2) ) result3 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 200)) result3 stopifnot( !identical(result1, result3) ) @ Results are invariant across \Rcode{*Param()} <>= result4 <- bplapply(1:3, runif, BPPARAM = SnowParam(RNGseed = 100)) stopifnot( identical(result1, result4) ) if (!identical(.Platform$OS.type, "windows")) { result5 <- bplapply(1:3, runif, BPPARAM = MulticoreParam(RNGseed = 100)) stopifnot( identical(result1, result5) ) } @ Parallel backends can adjust the number of \Rcode{workers} (processes performing the evaluation) and \Rcode{tasks} (how elements of \Rcode{X} are distributed between workers). Results are invariant to these parameters. This is illustrated with \Rcode{SnowParam()}, but applies also to \Rcode{MulticoreParam()}. <>= result6 <- bplapply(1:3, runif, BPPARAM = SnowParam(workers = 2, RNGseed = 100)) result7 <- bplapply(1:3, runif, BPPARAM = SnowParam(workers = 3, RNGseed = 100)) result8 <- bplapply( 1:3, runif, BPPARAM = SnowParam(workers = 2, tasks = 3, RNGseed = 100) ) stopifnot( identical(result1, result6), identical(result1, result7), identical(result1, result8) ) @ Subsequent sections illustrate results with \Rcode{SerialParam()}, but identical results are obtained with \Rcode{SnowParam()} and \Rcode{MulticoreParam()}. \subsection{Use with \Rcode{bpiterate()}} \Rcode{bpiterate()} allows parallel processing of a 'stream' of data as a series of tasks, with a task consisting of a portion of the overall data. It is useful when the data size is not known or easily partitioned into elements of a vector or list. A real use case might involve iterating through a BAM file, where a task represents successive records (perhaps 100,000 per task) in the file. Here we illustrate with a simple example -- iterating through a vector \Rcode{x = 1:3} <>= ITER_FUN_FACTORY <- function() { x <- 1:3 i <- 0L function() { i <<- i + 1L if (i > length(x)) return(NULL) x[[i]] } } @ \Rcode{ITER\_FUN\_FACTORY()} is used to create a function that, on each invocation, returns the next task (here, an element of \Rcode{x}; in a real example, perhaps 100000 records from a BAM file). When there are no more tasks, the function returns \Rcode{NULL} <>= ITER <- ITER_FUN_FACTORY() ITER() ITER() ITER() ITER() @ In our simple example, \Rcode{bpiterate()} is performing the same computations as \Rcode{bplapply()} so the results, including the random number streams used by each task in \Rcode{bpiterate()}, are the same <>= result9 <- bpiterate( ITER_FUN_FACTORY(), runif, BPPARAM = SerialParam(RNGseed = 100) ) stopifnot( identical(result1, result9) ) @ \subsection{Use with \Rcode{bptry()}} \Rcode{bptry()} in conjunction with the \Rcode{BPREDO=} argument to \Rcode{bplapply()} or \Rcode{bpiterate()} allows for graceful recovery from errors. Here a buggy \Rcode{FUN1()} produces an error for the second element. \Rcode{bptry()} allows evaluation to continue for other elements of \Rcode{X}, despite the error. This is shown in the result. <>= FUN1 <- function(i) { if (identical(i, 2L)) { ## error when evaluating the second element stop("i == 2") } else runif(i) } result10 <- bptry(bplapply( 1:3, FUN1, BPPARAM = SerialParam(RNGseed = 100, stop.on.error = FALSE) )) result10 @ \Rcode{FUN2()} illustrates the flexibility of \Rcode{bptry()} by fixing the bug when \Rcode{i == 2}, but also generating incorrect results if invoked for previously correct values. The identity of the result to the original computation shows that only the error task is re-computed, and that the random number stream used by the task is identical to the original stream. <>= FUN2 <- function(i) { if (identical(i, 2L)) { ## the random number stream should be in the same state as the ## first time through the loop, and rnorm(i) should return ## same result as FUN runif(i) } else { ## if this branch is used, then we are incorrectly updating ## already calculated elements -- '0' in the output would ## indicate this error 0 } } result11 <- bplapply( 1:3, FUN2, BPREDO = result10, BPPARAM = SerialParam(RNGseed = 100, stop.on.error = FALSE) ) stopifnot( identical(result1, result11) ) @ \subsection{Relationship between \Rcode{RNGseed=} and \Rcode{set.seed()}} The global random number stream (influenced by \Rcode{set.seed()}) is ignored by \BiocParallel{}, and \BiocParallel{} does NOT increment the global stream. <>= set.seed(200) value <- runif(1) set.seed(200) result12 <- bplapply(1:3, runif, BPPARAM = SerialParam(RNGseed = 100)) stopifnot( identical(result1, result12), identical(value, runif(1)) ) @ When \Rcode{RNGseed=} is not used, an internal stream (not accessible to the user) is used and \BiocParallel{} does NOT increment the global stream. <>= set.seed(100) value <- runif(1) set.seed(100) result13 <- bplapply(1:3, runif, BPPARAM = SerialParam()) stopifnot( !identical(result1, result13), identical(value, runif(1)) ) @ \subsection{\Rcode{bpstart()} and random number streams} In all of the examples so far \Rcode{*Param()} objects are passed to \Rcode{bplapply()} or \Rcode{bpiterate()} in the 'stopped' state. Internally, \Rcode{bplapply()} and \Rcode{bpiterate()} invoke \Rcode{bpstart()} to establish the computational environment (e.g., starting workers for \Rcode{SnowParam()}). \Rcode{bpstart()} can be called explicitly, e.g., to allow workers to be used across calls to \Rcode{bplapply()}. The cluster random number stream is initiated with \Rcode{bpstart()}. Thus <>= param <- bpstart(SerialParam(RNGseed = 100)) result16 <- bplapply(1:3, runif, BPPARAM = param) bpstop(param) stopifnot( identical(result1, result16) ) @ This allows a second call to \Rcode{bplapply} to represent a continuation of a random number computation -- the second call to \Rcode{bplapply()} results in different random number streams for each element of \Rcode{X}. <>= param <- bpstart(SerialParam(RNGseed = 100)) result16 <- bplapply(1:3, runif, BPPARAM = param) result17 <- bplapply(1:3, runif, BPPARAM = param) bpstop(param) stopifnot( identical(result1, result16), !identical(result1, result17) ) @ \subsection{Relationship between \Rcode{bplapply()} and \Rcode{lapply()}} The results from \Rcode{bplapply()} are different from the results from \Rcode{lapply()}, even with the same random number seed. This is because correctly implemented parallel random streams require use of a particular random number generator invoked in specific ways for each element of \Rcode{X}, as outlined in the Implementation notes section. <>= set.seed(100) result20 <- lapply(1:3, runif) stopifnot( !identical(result1, result20) ) @ \section{Implementation notes} The implementation uses the L'Ecuyer-CMRG random number generator (see \Rcode{?RNGkind} and \Rcode{?parallel::clusterSetRNGStream} for additional details). This random number generates independent streams and substreams of random numbers. In \BiocParallel{}, each call to \Rcode{bpstart()} creates a new stream from the L'Ecuyer-CMRG generator. Each element in \Rcode{bplapply()} or \Rcode{bpiterate()} creates a new substream. Each application of \Rcode{FUN} is therefore using the L'Ecuyer-CMRG random number generator, with a substream that is independent of the substreams of all other elements. Within the user-supplied \Rcode{FUN} of \Rcode{bplapply()} or \Rcode{bpiterate()}, it is a mistake to use \Rcode{RNGkind()} to set a different random number generator, or to use \Rcode{set.seed()}. This would in principle compromise the independence of the streams across elements. \section{\Rcode{sessionInfo()}} <>= sessionInfo() @ \end{document}