Enable multi-core parallel evaluation

Description

This class is used to parameterize single computer multicore parallel evaluation on non-Windows computers. multicoreWorkers() chooses the number of workers.

Usage

## constructor
## ------------------------------------

MulticoreParam(workers = multicoreWorkers(), tasks = 0L,
               stop.on.error = TRUE,
               progressbar = FALSE, RNGseed = NULL,
               timeout = WORKER_TIMEOUT, exportglobals=TRUE,
               log = FALSE, threshold = "INFO", logdir = NA_character_,
               resultdir = NA_character_, jobname = "BPJOB",
               force.GC = FALSE, fallback = TRUE,
               manager.hostname = NA_character_, manager.port = NA_integer_,
               ...)

## detect workers
## ------------------------------------

multicoreWorkers()

Arguments

Details

MulticoreParam is used for shared memory computing. Under the hood the cluster is created with makeCluster(..., type ="FORK") from the parallel package.

See ?BIOCPARALLEL_WORKER_NUMBER to control the default and maximum number of workers.

A FORK transport starts workers with the mcfork function and communicates between master and workers using socket connections. 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 MulticoreParam is created/used in Windows it defaults to SerialParam which is the equivalent of using a single worker.

error handling:

By default all computations are attempted and partial results are returned with any error messages.

• stop.on.error A logical. Stops all jobs as soon as one job fails or wait for all jobs to terminate. When FALSE, the return value is a list of successful results along with error messages as 'conditions'.

• The bpok(x) function returns a logical() vector that is FALSE for any jobs that threw an error. The input x is a list output from a bp*apply function such as bplapply or bpmapply.

logging:

When log = TRUE the futile.logger package is loaded on the workers. All log messages written in the 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 stdout and stderr are returned to the workspace by default. When log = TRUE these are diverted to the log output. Those familiar with the outfile argument to makeCluster can think of log = FALSE as equivalent to outfile = NULL; providing a logdir is the same as providing a name for 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.

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 logdir and resultdir fields in the constructor or with the accessors, bplogdir and bpresultdir.

random number generation:

For MulticoreParam, SnowParam, and SerialParam, random number generation is controlled through the 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 X, and used to seed the random number stream for the application of FUN() to X[[i]]. Thus setting RNGseed = ensures reproducibility across MulticoreParam(), SnowParam(), and SerialParam(), regardless of worker or task number. The default value RNGseed = NULL means that each evaluation of bplapply proceeds independently.

For details of the L'Ecuyer generator, see ?clusterSetRNGStream.

Constructor

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 bpstart is called. Named arguments in ... are passed to makeCluster.

Accessors: Logging and results

In the following code, x is a MulticoreParam object.

bpprogressbar(x), bpprogressbar(x) <- value:

Get or set the value to enable text progress bar. value must be a logical(1).

bpjobname(x), bpjobname(x) <- value:

Get or set the job name.

bpRNGseed(x), bpRNGseed(x) <- value:

Get or set the seed for random number generaton. value must be a numeric(1) or NULL.

bplog(x), bplog(x) <- value:

Get or set the value to enable logging. value must be a logical(1).

bpthreshold(x), bpthreshold(x) <- value:

Get or set the logging threshold. value must be a character(1) string of one of the levels defined in the futile.logger package: “TRACE”, “DEBUG”, “INFO”, “WARN”, “ERROR”, or “FATAL”.

bplogdir(x), bplogdir(x) <- value:

Get or set the directory for the log file. value must be a character(1) path, not a file name. The file is written out as LOGFILE.out. If no logdir is provided and bplog=TRUE log messages are sent to stdout.

bpresultdir(x), bpresultdir(x) <- value:

Get or set the directory for the result files. value must be a 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 resultdir is provided the results are returned to the session as list.

Accessors: Back-end control

In the code below x is a MulticoreParam object. See the ?BiocParallelParam man page for details on these accessors.

• bpworkers(x)

• bpnworkers(x)

• bptasks(x), bptasks(x) <- value

• bpstart(x)

• bpstop(x)

• bpisup(x)

• bpbackend(x), bpbackend(x) <- value

Accessors: Error Handling

In the code below x is a MulticoreParam object. See the ?BiocParallelParam man page for details on these accessors.

• bpstopOnError(x), bpstopOnError(x) <- value

Methods: Evaluation

In the code below BPPARAM is a MulticoreParam object. Full documentation for these functions are on separate man pages: see ?bpmapply, ?bplapply, ?bpvec, ?bpiterate and ?bpaggregate.

• bpmapply(FUN, ..., MoreArgs=NULL, SIMPLIFY=TRUE, USE.NAMES=TRUE, BPPARAM=bpparam())

• bplapply(X, FUN, ..., BPPARAM=bpparam())

• bpvec(X, FUN, ..., AGGREGATE=c, BPPARAM=bpparam())

• bpiterate(ITER, FUN, ..., BPPARAM=bpparam())

• bpaggregate(x, data, FUN, ..., BPPARAM=bpparam())

Methods: Other

In the code below x is a MulticoreParam object.

show(x):

Displays the MulticoreParam object.

Global Options

See the 'Global Options' section of SnowParam for manager host name and port defaults.

Author(s)

Martin Morgan mailto:mtmorgan@fhcrc.org and Valerie Obenchain

See Also

• register for registering parameter classes for use in parallel evaluation.

• SnowParam for computing in distributed memory

• DoparParam for computing with foreach

• 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:
## Not run: 
bplogdir(bpparam) <- tempdir()
bplapply(X, fun, BPPARAM = bpparam)
list.files(bplogdir(bpparam))

## End(Not run)

## -----------------------------------------------------------------------
## 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.
## Not run: 
dir.create(resultdir <- tempfile())
bpparam <- MulticoreParam(2, resultdir = resultdir, stop.on.error = FALSE)
bplapply(X, fun, BPPARAM = bpparam)
list.files(bpresultdir(bpparam))

## End(Not run)

## -----------------------------------------------------------------------
## 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)