Enhancing the 'parallel' Package.

parallelly: Enhancing the 'parallel' Package

The parallelly package provides functions that enhance the parallel packages. For example, availableCores() gives the number of CPU cores available to your R process as given by R options and environment variables, including those set by job schedulers on high-performance compute (HPC) clusters. If R runs under 'cgroups' or in a Linux container, then their settings are acknowledges too. If nothing else is set, then it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without having to know your local public IP address and configuring the firewall to do port-forwarding to your local computer. The functions and features added to this package are written to be backward compatible with the parallel package, such that they may be incorporated there later. The parallelly package comes with an open invitation for the R Core Team to adopt all or parts of its code into the parallel package.

Feature Comparison 'parallelly' vs 'parallel'

Compatibility with the parallel package

Any cluster created by the parallelly package is fully compatible with the clusters created by the parallel package and can be used by all of parallel's functions for cluster processing, e.g. parallel::clusterEvalQ() and parallel::parLapply(). The parallelly::makeClusterPSOCK() function can be used as a stand-in replacement of the parallel::makePSOCKcluster(), or equivalently, parallel::makeCluster(..., type = "PSOCK").

Most of parallelly functions apply also to clusters created by the parallel package. For example,

cl <- parallel::makeCluster(2)
cl <- parallelly::autoStopCluster(cl)

makes the cluster created by parallel to shut down automatically when R's garbage collector removes the cluster object. This lowers the risk for leaving stray R worker processes running in the background by mistake. Another way to achieve the above in a single call is to use:

cl <- parallelly::makeClusterPSOCK(2, autoStop = TRUE)

availableCores() vs parallel::detectCores()

The availableCores() function is designed as a better, safer alternative to detectCores() of the parallel package. It is designed to be a worry-free solution for developers and end-users to query the number of available cores - a solution that plays nice on multi-tenant systems, in Linux containers, on high-performance compute (HPC) cluster, on CRAN and Bioconductor check servers, and elsewhere.

Did you know that parallel::detectCores() might return NA on some systems, or that parallel::detectCores() - 1 might return 0 on some systems, e.g. old hardware and virtual machines? Because of this, you have to use max(1, parallel::detectCores() - 1, na.rm = TRUE) to get it correct. In contrast, parallelly::availableCores() is guaranteed to return a positive integer, and you can use parallelly::availableCores(omit = 1) to return all but one core and always at least one.

Just like other software tools that "hijacks" all cores by default, R scripts, and packages that defaults to detectCores() number of parallel workers cause lots of suffering for fellow end-users and system administrators. For instance, a shared server with 48 cores will come to a halt already after a few users run parallel processing using detectCores() number of parallel workers. This problem gets worse on machines with many cores because they can host even more concurrent users. If these R users would have used availableCores() instead, then the system administrator can limit the number of cores each user get to, say, two (2), by setting the environment variable R_PARALLELLY_AVAILABLECORES_FALLBACK=2. In contrast, it is not possible to override what parallel::detectCores() returns, cf. PR#17641 - WISH: Make parallel::detectCores() agile to new env var R_DEFAULT_CORES .

Similarly, availableCores() is also agile to CPU limitations set by Unix control groups (cgroups), which is often used by Linux containers (e.g. Docker, Apptainer / Singularity, and Podman) and Kubernetes (K8s) environments. For example, docker run --cpuset-cpus=0-2,8 ... sets the CPU affinity so that the processes can only run on CPUs 0, 1, 2, and 8 on the host system. In this case availableCores() detects this and returns four (4). Another example is docker run --cpu=3.4 ..., which throttles the CPU quota to on average 3.4 CPUs on the host system. In this case availableCores() detects this and returns three (3), because it rounds to the nearest integer. In contrast, parallel::detectCores() completely ignores such cgroups settings and returns the number of CPUs on the host system, which results in CPU overuse and degredated performance. Continous Integration (CI) services (e.g. GitHub Actions, Travis CI, and Appveyor CI) and cloud services (e.g. RStudio Cloud) use these types of cgroups settings under the hood, which means availableCores() respects their CPU allocations.

If running on an HPC cluster with a job scheduler, a script that uses availableCores() will run the number of parallel workers that the job scheduler has assigned to the job. For example, if we submit a Slurm job as sbatch --cpus-per-task=16 ..., then availableCores() returns 16, because it respects the SLURM_* environment variables set by the scheduler. On Son of Grid Engine (SGE), the scheduler sets NSLOTS when submitting using qsub -pe smp 8 ... and availableCores() returns eight (8). See help("availableCores", package = "parallelly") for currently supported job schedulers, which includes 'Fujitsu Technical Computing Suite', 'Load Sharing Facility' (LSF), Simple Linux Utility for Resource Management (Slurm), Sun Grid Engine/Oracle Grid Engine/Son of Grid Engine (SGE), Univa Grid Engine (UGE), and TORQUE/PBS.

Of course, availableCores() respects also R options and environment variables commonly used to specify the number of parallel workers, e.g. R option mc.cores and Bioconductor environment variable BIOCPARALLEL_WORKER_NUMBER. It will also detect when running R CMD check and limit the number of workers to two (2), which is the maximum number of parallel workers allowed by the CRAN Policies. This way you, as a package developer, know that your package will always play by the rules on CRAN and Bioconductor.

If nothing is set that limits the number of cores, then availableCores() falls back to parallel::detectCores() and if that returns NA_integer_ then one (1) is returned.

The below table summarize the benefits:

Backward compatibility with the future package

The functions in this package originate from the future package where we have used and validated them for several years. I moved these functions to this separate package in 2020, because they are also useful outside of the future framework. For backward-compatibility reasons of the future framework, the R options and environment variables that are prefixed with parallelly.* and R_PARALLELLY_* can for the time being also be set with future.* and R_FUTURE_* prefixes.

Roadmap

• [x] Submit parallelly to CRAN, with minimal changes compared to the corresponding functions in the future package (on CRAN as of 2020-10-20)

• [x] Update the future package to import and re-export the functions from the parallelly to maximize backward compatibility in the future framework (future 1.20.1 on CRAN as of 2020-11-03)

• [x] Switch to use 10-15% faster useXDR=FALSE

• [x] Implement same fast parallel setup of parallel PSOCK workers as in parallel (>= 4.0.0)

• [x] After having validated that there is no negative impact on the future framework, allow for changes in the parallelly package, e.g. renaming the R options and environment variable to be parallelly.* and R_PARALLELLY_* while falling back to future.* and R_FUTURE_*

• [ ] Migrate, currently internal, UUID functions and export them, e.g. uuid(), connectionUuid(), and sessionUuid() (https://github.com/HenrikBengtsson/Wishlist-for-R/issues/96). Because R does not have a built-in md5 checksum function that operates on object, these functions require us adding a dependency on the digest package.

• [ ] Add vignettes on how to set up cluster running on local or remote machines, including in Linux containers and on popular cloud services, and vignettes on common problems and how to troubleshoot them

Initially, backward compatibility for the future package is of top priority.

Installation

R package parallelly is available on CRAN and can be installed in R as:

install.packages("parallelly")

Pre-release version

To install the pre-release version that is available in Git branch develop on GitHub, use:

remotes::install_github("HenrikBengtsson/parallelly", ref="develop")

This will install the package from source.