BiocParallel 1.31.14
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. Most Task View packages cite or identify one or more of snow , Rmpi, multicore or foreach as relevant parallelization infrastructure. Direct support in R for parallel computing started with release 2.14.0 with inclusion of the parallel package which contains modified versions of multicore and 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 BiocParallelParam object, BiocParallel aims to provide a unified interface to
existing parallel infrastructure where code can be easily executed in
different environments. The 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.
unified interface: BiocParallelParam instances define the method of parallel
evaluation (multi-core, snow cluster, etc.) and computing resources
(number of workers, error handling, cleanup, etc.).
parallel iteration over lists, files and vectorized operations: bplapply, bpmapply and
bpvec provide parallel list iteration and vectorized operations. bpiterate iterates
through files distributing chunks to parallel workers.
cluster scheduling: When the parallel environment is managed by a cluster scheduler through *batchtools, job management and result retrieval are considerably simplified.
support of foreach : The foreach and iterators packages are fully supported. Registration of
the parallel back end uses BiocParallelParam instances.
The BiocParallel package is available at bioconductor.org and can be downloaded via 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()## $MulticoreParam
## class: MulticoreParam
## bpisup: FALSE; bpnworkers: 6; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: TRUE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: FORK
##
## $SnowParam
## class: SnowParam
## bpisup: FALSE; bpnworkers: 6; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: SOCK
##
## $SerialParam
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE; bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NAConfigure your R session to always use a particular back-end configure by setting options
named after the back ends in an .RProfile file, e.g.,
optionsoptions(MulticoreParam=quote(MulticoreParam(workers=4)))When a BiocParallel function is invoked with no 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)## [[1]]
## [1] 1
##
## [[2]]
## [1] 1.4142
##
## [[3]]
## [1] 1.7321
##
## [[4]]
## [1] 2BiocParallelParamBiocParallelParam instances configure different parallel evaluation environments. Creating
or register() ing a ‘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 MulticoreParam which is Unix and Mac only.
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.
MulticoreParam:
Supported on Unix and Mac. On Windows, MulticoreParam dispatches to 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 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 mc.cores (e.g.,getOption("mc.cores") ) or, if that is not set, the number of
cores returned by arallel::detectCores() - 2 ; when number of cores cannot be determined, the
default is 1.
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 MulticoreParam . Forked processes are
not available on Windows. Some environments, e.g., RStudio, do not
work well with forked processes, assuming that 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 this
post for
additional details.
MulticoreParam is based on facilities originally implemented in the multicore package and subsequently the parallel package in base .
SnowParam:
Supported on all platforms.
Evaluate BiocParallel-enabled code across several distinct 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 snow package. Different types of snow ‘back-ends’ are supported, including socket and MPI clusters.
BatchtoolsParam:
Applicable to clusters with formal schedulers.
Evaluate BiocParallel-enabled code by submitting to a cluster scheduler like SGE.
DoparParam:
Supported on all platforms.
Register a parallel back-end supported by the foreach package for use with BiocParallel.
The simplest illustration of creating BiocParallelParam is
serialParam <- SerialParam()
serialParam## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE; bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NAMost parameters have additional arguments influencing behavior, e.g.,
specifying the number of ‘cores’ to use when creating a MulticoreParam instance
multicoreParam <- MulticoreParam(workers = 8)
multicoreParam## class: MulticoreParam
## bpisup: FALSE; bpnworkers: 8; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: TRUE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: FORKArguments are described on the corresponding help page, e.g., ?MulticoreParam..
register()ing BiocParallelParam instancesThe list of registered 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).
registered invoked with no arguments lists all back-ends.
registered()## $MulticoreParam
## class: MulticoreParam
## bpisup: FALSE; bpnworkers: 6; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: TRUE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: FORK
##
## $SnowParam
## class: SnowParam
## bpisup: FALSE; bpnworkers: 6; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: SOCK
##
## $SerialParam
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE; bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NAbpparam returns the default from the top of the list.
bpparam()## class: MulticoreParam
## bpisup: FALSE; bpnworkers: 6; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: TRUE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: FORKAdd a specialized instance with register . When 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())## [1] "BatchtoolsParam" "MulticoreParam" "SnowParam" "SerialParam"bpparam()## class: BatchtoolsParam
## bpisup: FALSE; bpnworkers: 10; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: NA; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: TRUE; bpforceGC: FALSE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: multicore
## template: NA
## registryargs:
## file.dir: /home/phyllis/Documents/BiocParallel/vignettes/file435b5e127d45
## work.dir: getwd()
## packages: character(0)
## namespaces: character(0)
## source: character(0)
## load: character(0)
## make.default: FALSE
## saveregistry: FALSE
## resources:Restore the original registry
for (param in rev(default))
register(param)These are used in common functions, implemented as much as possible for
all back-ends. The functions (see the help pages, e.g., ?bplapply for a full
definition) include
bplapply(X, FUN, ...):
Apply in parallel a function FUN to each element of X. bplapply invokes FUN length(X) times, each
time with a single element of X .
bpmapply(FUN, ...):
Apply in parallel a function to the first, second, etc., elements of each argument in ….
bpiterate(ITER, FUN, ...):
Apply in parallel a function to the output of function ITER. Data chunks are
returned by ITER and distributed to parallel workers along with FUN. Intended
for iteration though an undefined number of data chunks (i.e., records
in a file).
bpvec(X, FUN, ...):
Apply in parallel a function FUN to subsets of X.bpvec invokes function as many
times as there are cores or cluster nodes, with receiving a subset
(typically more than 1 element, in contrast to bplapply) of X .
bpaggregate(x, data, FUN, ...):
Use the formula in X to aggregate data using FUN .
These functions query and control the state of the parallel evaluation environment.
bpisup(x): Query a BiocParallelParam back-end X for its status.
bpworkers; bpnworkers: Query a BiocParallelParam back-end for the number of workers available for parallel
evaluation.
bptasks: Divides a job (e.g., single call to *lapply function) into tasks.
Applicable to MulticoreParam only;DoparParam and BatchtoolsParam have their own approach to dividing a job among
workers.
bpstart(x): Start a parallel back end specified by BiocParallelParam x,, if possible.
bpstop(x): Stop a parallel back end specified by BiocParallelParam x.
Logging and advanced error recovery is available in BiocParallel 1.1.25 and later.
For a more details see the vignette titled “Error Handling and
Logging”:
browseVignettes("BiocParallel")Inter-process (i.e., single machine) locks and counters are supported
using ipclock(), ip cyield(), and friends. Use these to synchronize computation, e.g.,
allowing only a single process to write to a file at a time.
Sample data are BAM files from a transcription profiling experiment available in the RNAse- qData.HNRNPC.bam.chr14 package.
library(RNAseqData.HNRNPC.bam.chr14)
fls <- RNAseqData.HNRNPC.bam.chr14_BAMFILESCommon approaches on a single machine are to use multiple cores in forked processes, or to use clusters of independent processes.
For purely -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 MulticoreParam is an attractive solution for scripts using pure
functionality, robust and complex code often requires use of independent
processes and SnowParam.
MulticoreParamThis 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 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 MulticoreParam instance.
MulticoreParam()## class: MulticoreParam
## bpisup: FALSE; bpnworkers: 6; bptasks: 0; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: FALSE
## bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: TRUE; bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: FORKThe BiocParallel functions, such as bplapply, use information in the MulticoreParam to set up the
appropriate back-end and pass relevant arguments to low-level functions.
> bplapply(fls[1:3], FUN, BPPARAM = MulticoreParam(), param = param)
$ERR127306
[1] 1185
$ERR127307
[1] 1123
$ERR127308
[1] 1241Shared 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 bplapply and FUN did not need to load GenomicAlignmentsfor access
to the readGAlign ments 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 org.* packages. This pseudo-code
library(org.Hs.eg.db)
FUN <- function(x, ...) {
...
mapIds(org.Hs.eg.db, ...)
...
}
bplapply(X, FUN, ..., BPPARAM = MulticoreParam())is likely to fail, because 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())SnowParamBoth Windows and non-Windows machines can use the cluster approach to spawn processes. BiocParallel back-end choices for clusters on a single machine are SnowParam for configuring a Snow cluster or the DoparParam for use with the 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 6.)
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 bplapply with the SnowParam creates the cluster and distributes the work.
bplapply(fls[1:3], FUN, BPPARAM = snow, param = param, gr = gr)## $ERR127306
## [1] 1185
##
## $ERR127307
## [1] 1123
##
## $ERR127308
## [1] 1241The FUN written for the cluster adds some overhead due to the passing of the GRanges and the loading of 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 bplapply() statements are likely to require the same resource, it often
makes sense to create a cluster once using bpstart(). The workers are re-used by
each call to 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()We use the term 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.
On Linux and Mac OS X, a socket cluster is created across machines by supplying machine names as the`workers``argument to a BiocParallelParam instance instead of a number. Each name represents an R process; repeat names indicate multiple workers on the same machine. Create a with SnowParam 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.
> 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"When creating a cluster across Windows machines must be IP addresses (e.g., "140.107.218.57") instead of machine names. ### MPI An MPI cluster across machines is created with mpirun or 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.
rhino01 slots=2
rhino02 slots=2
rhino03 slots=2From the command line, start a single interactive process on the current machine.
mpiexec --np 1 --hostfile hostfile R --vanillaLoad BiocParallel and create an MPI Snow cluster. The number workers of in should match the
number of slots requested in the hostfile. Using a smaller number of
workers uses a subset of the slots.
> library(BiocParallel)
> param <- SnowParam(workers = 6, type = "MPI")Execute FUN 6 times across the workers.
> 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"Batch jobs can be launched with mpiexec and R CMD BATCH. Code to be executed is in ‘Rcode.R’.
mpiexec --hostfile hostfile R CMD BATCH Rcode.RComputer 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.
### Cluster-centric
The idea is to use cluster management software to allocate resources,
and then arrange for an 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 salloc or
sbatch), arrange to start the MPI environment (with orterun) and on
a single node in that universe run an script BiocParallel-MPI.R. The
command is
$ salloc -N 4 orterun -n 1 R -f BiocParallel-MPI.RThe R script might do the following, using MPI for parallel evaluation.
Start by loading necessary packages and defining FUN work to be done
library(BiocParallel)
library(Rmpi)
FUN <- function(i) system("hostname", intern=TRUE)Create a 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:
$ 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.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
salloc (or sbatch).
Notice that workers are spawned within the bplapply function; it might often make
sense to more explicitly manage workers with bpstart and bpstop, e.g.,
param <- bpstart(SnowParam(mpi.universe.size() - 1, "MPI"))
register(param)
xx <- bplapply(1:100, FUN)
bpstop(param)
mpi.quit()A more R-centric approach might start an R script on the head node, and use batchtools to submit jobs from within R the 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))## [1] #!/bin/bash
## [2]
## [3] ## Job Resource Interface Definition
## [4] ##
## [5] ## ntasks [integer(1)]: Number of required tasks,
## [6] ## Set larger than 1 if you want to further parallelize
## [7] ## with MPI within your job.
## [8] ## ncpus [integer(1)]: Number of required cpus per task,
## [9] ## Set larger than 1 if you want to further parallelize
## [10] ## with multicore/parallel within each task.
## [11] ## walltime [integer(1)]: Walltime for this job, in seconds.
## [12] ## Must be at least 60 seconds for Slurm to work properly.
## [13] ## memory [integer(1)]: Memory in megabytes for each cpu.
## [14] ## Must be at least 100 (when I tried lower values my
## [15] ## jobs did not start at all).
## [16] ##
## [17] ## Default resources can be set in your .batchtools.conf.R by defining the variable
## [18] ## 'default.resources' as a named list.
## [19]
## [20] <%
## [21] # relative paths are not handled well by Slurm
## [22] log.file = fs::path_expand(log.file)
## [23] -%>
## [24]
## [25]
## [26] #SBATCH --job-name=<%= job.name %>
## [27] #SBATCH --output=<%= log.file %>
## [28] #SBATCH --error=<%= log.file %>
## [29] #SBATCH --time=<%= ceiling(resources$walltime / 60) %>
## [30] #SBATCH --ntasks=1
## [31] #SBATCH --cpus-per-task=<%= resources$ncpus %>
## [32] #SBATCH --mem-per-cpu=<%= resources$memory %>
## [33] <%= if (!is.null(resources$partition)) sprintf(paste0("#SBATCH --partition='", resources$partition, "'")) %>
## [34] <%= if (array.jobs) sprintf("#SBATCH --array=1-%i", nrow(jobs)) else "" %>
## [35]
## [36] ## Initialize work environment like
## [37] ## source /etc/profile
## [38] ## module add ...
## [39]
## [40] ## Export value of DEBUGME environemnt var to slave
## [41] export DEBUGME=<%= Sys.getenv("DEBUGME") %>
## [42]
## [43] <%= sprintf("export OMP_NUM_THREADS=%i", resources$omp.threads) -%>
## [44] <%= sprintf("export OPENBLAS_NUM_THREADS=%i", resources$blas.threads) -%>
## [45] <%= sprintf("export MKL_NUM_THREADS=%i", resources$blas.threads) -%>
## [46]
## [47] ## Run R:
## [48] ## we merge R output with stdout from SLURM, which gets then logged via --output option
## [49] Rscript -e 'batchtools::doJobCollection("<%= uri %>")'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 bplapply , where the script interacts with the SLURM
scheduler to request a SLURM allocation, run jobs, and retrieve results.
The argument 4 to BatchtoolsParam specifies the number of workers to request from the
scheduler; bplapply divides the 100 jobs among the 4 workers. If BatchtoolsParam had been created
without specifying any workers, then 100 jobs implied by the argument to 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 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.
General strategies exist for handling large genomic data that are well suited to R programs. A manuscript titled Scalable Genomics with R and BioConductor (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.
Developers wishing to use BiocParallel in their own packages should include BiocParallel in the
DESCRIPTION file
Imports: BiocParalleland import the functions they wish to use in the NAMESPACE file, e.g.,
importFrom(BiocParallel, bplapply)Then invoke the desired function in the code, e.g.,
system.time(x <- bplapply(1:3, function(i) { Sys.sleep(i); i }))## user system elapsed
## 0.096 0.067 3.085unlist(x)## [1] 1 2 3This will use the back-end returned by bpparam() ,
by default a MulticoreParam() on Linux / macOS, on Windows, or the user’s preferred
back-end if they have used register().
The MulticoreParam back-end does not require any special configuration or set-up and is therefore the safest option for
developers. Unfortunately, MulticoreParam provides only serial evaluation on Windows.
Developers should document that their function uses BiocParallel functions on the main
page, and should perhaps include in their function signature an argument 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 MulticoreParam , or to write code
that works across Windows, macOS and Linux, no longer need to take
special care to ensure that required packages, data, and functions are
available and loaded on the remote nodes. By default, will export global
variables to the workers due to the default . Nonetheless, a good
practice during development is to use independent processes (via )
rather than relying on forked (via ) processes. For instance, clusters
include the costs of setting up the computational environment (loading
required packages, for instance) that may discourage use of
parallelization when parallelization provides only marginal performance
gains from the computation per se. Likewise, may be more sensitive to
inappropriate calls to shared libraries, revealing errors that are only
transient under .
In bplapply(), the environment of FUN (other than the global environment) is
serialized to the workers. A consequence is that, when FUN is inside a
package name space, other functions available in the name space are
available to FUN on the workers.
If the package is installed on a server used by multiple users, then the default value of cores used can sometimes lead to many more tasks being run than the server has cores if two or more users run a parallel-enabled function simultaneously. A more conservative number of cores than all of them minus 2 may be desirable, so that one user does not take all of the cores unless they explicitly specify so. This can be implemented with environment variables. Setting or for all system users to the number of cores divided by the typical number of concurrent users is a reasonable approach to avoiding this scenario.
sessionInfo()R version 4.2.1 (2022-06-23) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04 LTS
Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=sw_KE.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=sw_KE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=sw_KE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=sw_KE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GenomicAlignments_1.33.1 Rsamtools_2.13.4
[3] Biostrings_2.65.6 XVector_0.37.1
[5] SummarizedExperiment_1.27.3 Biobase_2.57.1
[7] MatrixGenerics_1.9.1 matrixStats_0.62.0
[9] GenomicRanges_1.49.1 GenomeInfoDb_1.33.14
[11] IRanges_2.31.2 S4Vectors_0.35.4
[13] BiocGenerics_0.43.4 RNAseqData.HNRNPC.bam.chr14_0.35.0
[15] BiocParallel_1.31.14 BiocStyle_2.25.0
loaded via a namespace (and not attached):
[1] lattice_0.20-45 prettyunits_1.1.1 snow_0.4-4
[4] digest_0.6.30 R6_2.5.1 backports_1.4.1
[7] evaluate_0.17 zlibbioc_1.43.0 rlang_1.0.6
[10] progress_1.2.2 rstudioapi_0.14 data.table_1.14.4
[13] jquerylib_0.1.4 Matrix_1.5-1 checkmate_2.1.0
[16] rmarkdown_2.17 stringr_1.4.1 RCurl_1.98-1.9
[19] DelayedArray_0.23.2 compiler_4.2.1 xfun_0.34
[22] pkgconfig_2.0.3 htmltools_0.5.3 GenomeInfoDbData_1.2.9
[25] bookdown_0.29 batchtools_0.9.15 codetools_0.2-18
[28] crayon_1.5.2 withr_2.5.0 bitops_1.0-7
[31] rappdirs_0.3.3 grid_4.2.1 jsonlite_1.8.3
[34] lifecycle_1.0.3 magrittr_2.0.3 cli_3.4.1
[37] stringi_1.7.8 cachem_1.0.6 bslib_0.4.0
[40] ellipsis_0.3.2 brew_1.0-8 vctrs_0.4.2
[43] tools_4.2.1 hms_1.1.2 parallel_4.2.1
[46] fastmap_1.1.0 yaml_2.3.6 BiocManager_1.30.18
[49] base64url_1.4 knitr_1.40 sass_0.4.2