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 1.9.0 - Unified Parallel and Distributed Processing in R for Everyone - is on CRAN. This is a milestone release:
Standard output is now relayed from futures back to the master R session - regardless of where the futures are processed!
Disclaimer: A future’s output is relayed only after it is resolved and when its value is retrieved by the master R process. In other words, the output is not streamed back in a “live” fashion as it is produced.
R.devices 2.16.0 - Unified Handling of Graphics Devices - is on CRAN. With this release, you can now easily suppress unwanted graphics, e.g. graphics produced by one of those do-everything-in-one-call functions that we all bump into once in a while. To suppress graphics, the R.devices package provides graphics device nulldev(), and function suppressGraphics(), which both send any produced graphics into the void. This works on all operating systems, including Windows.
Got compute?
future.apply 1.0.0 - Apply Function to Elements in Parallel using Futures - is on CRAN. With this milestone release, all* base R apply functions now have corresponding futurized implementations. This makes it easier than ever before to parallelize your existing apply(), lapply(), mapply(), … code - just prepend future_ to an apply call that takes a long time to complete. That’s it! The default is sequential processing but by using plan(multiprocess) it’ll run in parallel.
As promised - though a bit delayed - below are links to my slides and the video of my talk on Future: Parallel & Distributed Processing in R for Everyone that I presented last month at the eRum 2018 conference in Budapest, Hungary (May 14-16, 2018).
The conference was very well organized (thank you everyone involved) with a great lineup of several brilliant workshop sessions, talks, and poster presentations (thanks all).
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.
x[idxs + 1] or x[idxs + 1L]? That is the question.
Assume that we have a vector $x$ of $n = 100,000$ random values, e.g.
> n <- 100000 > x <- rnorm(n) and that we wish to calculate the $n-1$ first-order differences $y=(y_1, y_2, …, y_{n-1})$ where $y_i=x_{i+1} - x_i$. In R, we can calculate this using the following vectorized form:
> idxs <- seq_len(n - 1) > y <- x[idxs + 1] - x[idxs] We can certainly do better if we turn to native code, but is there a more efficient way to implement this using plain R code?
New release: startup 0.10.0 is now on CRAN.
If your R startup files (.Renviron and .Rprofile) get long and windy, or if you want to make parts of them public and other parts private, then you can use the startup package to split them up in separate files and directories under .Renviron.d/ and .Rprofile.d/. For instance, the .Rprofile.d/repos.R file can be solely dedicated to setting in the repos option, which specifies from which web servers R packages are installed from.
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.
Today, its been 20 years since Martin Mächler started the R-help community list. The first post was written by Ross Ihaka on 1997-04-01:
Screenshot of the very first post to the R-help mailing list.
This is a post about R’s memory model. We’re talking R v0.50 beta. I think that the paragraph at the end provides a nice anecdote on the importance not to be overwhelmed by problems ahead:
”(The consumption of one cell per string is perhaps the major memory problem in R - we didn’t design it with large problems in mind.
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.
The startup package makes it easy to control your R startup processes and to share part of your startup settings with others (e.g. as a public Git repository) while keeping secret parts to yourself. Instead of having long and windy .Renviron and .Rprofile startup files, you can split them up into short specific files under corresponding .Renviron.d/ and .Rprofile.d/ directories. For example,
# Environment variables # (one name=value per line) .
A new version of the future.BatchJobs package has been released and is available on CRAN. With a single change of settings, it allows you to switch from running an analysis sequentially on a local machine to running it in parallel on a compute cluster.
Our different futures can easily be resolved on high-performance compute clusters.
Requirements The future.BatchJobs package implements the Future API, as defined by the future package, on top of the API provided by the BatchJobs package.
A new version of the future package has been released and is available on CRAN. With futures, it is easy to write R code once, which later the user can choose to parallelize using whatever resources s/he has available, e.g. a local machine, a set of local notebooks, a set of remote machines, or a high-end compute cluster.
The future provides comfortable and friendly long-distance interactions.
The new version, future 1.
Unless you count DSC 2003 in Vienna, last week’s useR conference at Stanford was my very first time at useR. It was a great event, it was awesome to meet our lovely and vibrant R community in real life, which we otherwise only get know from online interactions, and of course it was very nice to meet old friends and make new ones.
The future is promising.
At the end of the second day, I presented A Future for R (18 min talk; slides below) on how you can use the future package for asynchronous (parallel and distributed) processing using a single unified API regardless of what backend you have available, e.
The matrixStats package provides highly optimized functions for computing common summaries over rows and columns of matrices. In a previous blog post, I showed that, instead of using apply(X, MARGIN = 2, FUN = median), we can speed up calculations dramatically by using colMedians(X). In the most recent release (version 0.50.0), matrixStats has been extended to perform optimized calculations also on a subset of rows and/or columns specified via new arguments rows and cols, e.
Another 1,000 packages were added to CRAN, which took less than 9 months. Today (August 12, 2015), the Comprehensive R Archive Network (CRAN) package page reports:
“Currently, the CRAN package repository features 7002 available packages.”
While the previous 1,000 packages took 355 days, going from 6,000 to 7,000 packages took 286 days - which means that now a new CRAN package is born on average every 6.9 hours (or 3.
If your native code takes more than a few seconds to finish, it is a nice courtesy to the user to check for user interrupts (Ctrl-C) once in a while, say, every 1,000 or 1,000,000 iteration. The C-level API of R provides R_CheckUserInterrupt() for this (see ‘Writing R Extensions’ for more information on this function). Here’s what the code would typically look like:
for (int ii = 0; ii < n; ii++) { /* Some computational expensive code */ if (ii % 1000 == 0) R_CheckUserInterrupt() } This uses the modulo operator % and tests when it is zero, which happens every 1,000 iteration.
