MulticoreParam-class {BiocParallel} | R Documentation

## 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 = 30L * 24L * 60L * 60L, exportglobals=TRUE,
                   log = FALSE, threshold = "INFO", logdir = NA_character_,
                   resultdir = NA_character_, jobname = "BPJOB",
                   manager.hostname = NA_character_, manager.port = NA_integer_,
                   ...)

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

    multicoreWorkers()

### Arguments

`workers` | `integer(1)` Number of workers. Defaults to all cores available as determined by `detectCores`.
`tasks` | `integer(1)`. The number of tasks per job. `value` must be a scalar integer >= 0L. In this documentation a job is defined as a single call to a function, such as `bplapply`, `bpmapply` etc. A task is the division of the `X` argument into chunks. When `tasks == 0` (default), `X` is divided as evenly as possible over the number of workers. A `tasks` value of > 0 specifies the exact number of tasks. Values can range from 1 (all of `X` to a single worker) to the length of `X` (each element of `X` to a different worker). When the length of `X` is less than the number of workers each element of `X` is sent to a worker and `tasks` is ignored.
`stop.on.error` | `logical(1)` Enable stop on error.
`progressbar` | `logical(1)` Enable progress bar (based on plyr:::progress_text).
`RNGseed` | `integer(1)` Seed for random number generation. When not `NULL`, this value is passed to parallel::clusterSetRNGStream to generate random number streams on each worker.
`timeout` | `numeric(1)` Time (in seconds) allowed for worker to complete a task. This value is passed to base::setTimeLimit() as both the `cpu` and `elapsed` arguments. If the computation exceeds `timeout` an error is thrown with message 'reached elapsed time limit'.
`exportglobals` | `logical(1)` Export `base::options()` from manager to workers? Default `TRUE`.
`log` | `logical(1)` Enable logging.
`threshold` | `character(1)` Logging threshold as defined in `futile.logger`.
`logdir` | `character(1)` Log files directory. When not provided, log messages are returned to stdout.
`resultdir` | `character(1)` Job results directory. When not provided, results are returned as an R object (list) to the workspace.
`jobname` | `character(1)` Job name that is prepended to log and result files. Default is "BPJOB".
`manager.hostname` | `character(1)` Host name of manager node. See 'Global Options', in `SnowParam`.
`manager.port` | `integer(1)` Port on manager with which workers communicate. See 'Global Options' in `SnowParam`.
`...` | Additional arguments passed to `makeCluster`

### Details

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

The default number of workers is determined by `multicoreWorkers()`. On windows, the number of multicore workers is always 1. Otherwise, the default is normally the maximum of 1 and `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 R connections available when the workers start; this is 128 (a hard-coded limit in R) minus the number of open connections as returned by `nrow(showConnections(all=TRUE))`. The option `mc.cores` can be used to specify an arbitrary number of workers, e.g., `options(mc.cores=4L)`; the Bioconductor build system enforces a maximum of 4 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. 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.

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:

  `MulticoreParam` and `SnowParam` use the random number generation support from the parallel package. These params are snow-derived clusters so the arguments for multicore-derived functions such as `mc.set.seed` and `mc.reset.stream` do not apply.

  Random number generation is controlled through the param argument, `RNGseed` which is passed to parallel::clusterSetRNGStream. `clusterSetRNGStream` uses the L'Ecuyer-CMRG random number generator and distributes streams to the members of a cluster. If `RNGseed` is not NULL it serves as the seed to the streams, otherwise the streams are set from the current seed of the master process after selecting the L'Ecuyer generator. See ?`clusterSetRNGStream` for more details.

### 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

- `BatchJobsParam` for computing with cluster schedulers

- `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)
            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 <- 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)

[Package BiocParallel version 1.22.0 Index]