doFuture 1.0.0 is on CRAN since March 2023. It introduces a new foreach operator %dofuture%, which makes it even easier to use foreach() to parallelize via the future ecosystem. This new operator is designed to be an alternative to the existing %dopar% operator for foreach() - an alternative that works in similar ways but better. If you already use foreach() together with futures, or plan on doing so, I recommend using %dofuture% instead of %dopar%.
doFuture 1.0.0 is on CRAN since March 2023. It introduces a new foreach operator %dofuture%, which makes it even easier to use foreach() to parallelize via the future ecosystem. This new operator is designed to be an alternative to the existing %dopar% operator for foreach() - an alternative that works in similar ways but better. If you already use foreach() together with futures, or plan on doing so, I recommend using %dofuture% instead of %dopar%.
Each time we use R to analyze data, we rely on the assumption that functions used produce correct results. If we can’t make this assumption, we have to spend a lot of time validating every nitty detail. Luckily, we don’t have to do this. There are many reasons for why we can comfortably use R for our analyses and some of them are unique to R. Here are some I could think of while writing this blog post - I’m sure I forgot something:
Parallel ‘Digital Rain’ by Jahobr
After two-and-a-half months, future 1.19.1 is now on CRAN. As usual, there are some bug fixes and minor improvements here and there (NEWS), including things needed by the next version of furrr. For those of you who use Slurm or LSF/OpenLava as a scheduler on your high-performance compute (HPC) cluster, future::availableCores() will now do a better job respecting the CPU resources that those schedulers allocate for your R jobs.
No dogs were harmed while making this release
future 1.15.0 is now on CRAN, accompanied by a recent, related update of future.callr 0.5.0. The main update is a change to the Future API:
resolved() will now also launch lazy futures
Although this change does not look much to the world, I’d like to think of this as part of a young person slowly finding themselves. This change in behavior helps us in cases where we create lazy futures upfront;
A commonly asked question in the R community is:
How can I parallelize the following for-loop?
The answer almost always involves rewriting the for (...) { ... } loop into something that looks like a y <- lapply(...) call. If you can achieve that, you can parallelize it via for instance y <- future.apply::future_lapply(...) or y <- foreach::foreach() %dopar% { ... }.
For some for-loops it is straightforward to rewrite the code to make use of lapply() instead, whereas in other cases it can be a bit more complicated, especially if the for-loop updates multiple variables in each iteration.
New versions of the following future backends are available on CRAN:
future.callr - parallelization via callr, i.e. on the local machine future.batchtools - parallelization via batchtools, i.e. on a compute cluster with job schedulers (SLURM, SGE, Torque/PBS, etc.) but also on the local machine future.BatchJobs - (maintained for legacy reasons) parallelization via BatchJobs, which is the predecessor of batchtools These releases fix a few small bugs and inconsistencies that were identified with help of the future.
future 1.8.0 is available on CRAN.
This release lays the foundation for being able to capture outputs from futures, perform automated timing and memory benchmarking (profiling) on futures, and more. These features are not yet available out of the box, but thanks to this release we will be able to make some headway on many of the feature requests related to this - hopefully already by the next release.
The future package defines the Future API, which is a unified, generic, friendly API for parallel processing. The Future API follows the principle of write code once and run anywhere - the developer chooses what to parallelize and the user how and where.
The nature of a future is such that it lends itself to be used with several of the existing map-reduce frameworks already available in R. In this post, I’ll give an example of how to apply a function over a set of elements concurrently using plain sequential R, the parallel package, the future package alone, as well as future in combination of the foreach, the plyr, and the purrr packages.
doFuture 0.4.0 is available on CRAN. The doFuture package provides a universal foreach adaptor enabling any future backend to be used with the foreach() %dopar% { ... } construct. As shown below, this will allow foreach() to parallelize on not only multiple cores, multiple background R sessions, and ad-hoc clusters, but also cloud-based clusters and high performance compute (HPC) environments.
1,300+ R packages on CRAN and Bioconductor depend, directly or indirectly, on foreach for their parallel processing.
future 1.3.0 is available on CRAN. With futures, it is easy to write R code once, which the user can choose to evaluate in parallel using whatever resources s/he has available, e.g. a local machine, a set of local machines, a set of remote machines, a high-end compute cluster (via future.BatchJobs and soon also future.batchtools), or in the cloud (e.g. via googleComputeEngineR).
Futures makes it easy to harness any resources at hand.
