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.
Design: Dan LaBar I presented Future: Simple Async, Parallel & Distributed Processing in R Why and What’s New? at rstudio::conf 2020 in San Francisco, USA, on January 29, 2020. Below are the slides for my talk (17 slides; ~18+2 minutes):
HTML (incremental Google Slides; requires online access) PDF (flat slides) Video with closed captions (official rstudio::conf recording) First of all, a big thank you goes out to Dan LaBar (@embiggenData) for proposing and contributing the original design of the future hex sticker.
Below are the slides for my Future: Simple Parallel and Distributed Processing in R that I presented at the useR! 2019 conference in Toulouse, France on July 9-12, 2019.
My talk (25 slides; ~15+3 minutes):
Title: Future: Simple Parallel and Distributed Processing in R HTML (incremental Google Slides; requires online access) PDF (flat slides) Video (official recording) I want to send out a big thank you to everyone making the useR!
A bit late but here are my slides on Future: Friendly Parallel Processing in R for Everyone that I presented at the satRday LA 2019 conference in Los Angeles, CA, USA on April 6, 2019.
My talk (33 slides; ~45 minutes):
Title: : Friendly Parallel and Distributed Processing in R for Everyone HTML (incremental slides; requires online access) PDF (flat slides) Video (44 min; YouTube; sorry, different page numbers) Thank you all for making this a stellar satRday event.
Below are links to my slides from my talk on Future: Friendly Parallel Processing in R for Everyone that I presented last month at the satRday Paris 2019 conference in Paris, France (February 23, 2019).
My talk (32 slides; ~40 minutes):
Title: Future: Friendly Parallel Processing in R for Everyone HTML (incremental slides; requires online access) PDF (flat slides) A big shout out to the organizers, all the volunteers, and everyone else for making it a great satRday.
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.
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(multisession) 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).
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.
