parallelly adverb
par·al·lel·ly | \ ˈpa-rə-le(l)li \ Definition: in a parallel manner future noun
fu·ture | \ ˈfyü-chər \ Definition: existing or occurring at a later time I’ve cleaned up around the house - with the recent release of future 1.20.1, the package gained a dependency on the new parallelly package. Now, if you’re like me and concerned about bloating package dependencies, I’m sure you immediately wondered why I chose to introduce a new dependency.
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.
If you ever need to figure out if a function call in R generated a random number or not, here is a simple trick that you can use in an interactive R session. Add the following to your ~/.Rprofile(*):
if (interactive()) { invisible(addTaskCallback(local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { msg <- "TRACKER: .Random.seed changed" if (requireNamespace("crayon", quietly=TRUE)) msg <- crayon::blurred(msg) message(msg) last <<- curr } TRUE } }), name = "RNG tracker")) } It works by checking whether or not the state of the random number generator (RNG), that is, .
There are new versions of future and future.apply - your friends in the parallelization business - on CRAN. These updates are mostly maintenance updates with bug fixes, some improvements, and preparations for upcoming changes. It’s been some time since I blogged about these packages, so here is the summary of the main updates this far since early 2020:
future:
values() for lists and other containers was renamed to value() to simplify the API [future 1.
Source: Wiktionary.org I presented Progressr: An Inclusive, Unifying API for Progress Updates (15 minutes; 20 slides) at e-Rum 2020, on June 17, 2020:
Abstract HTML (incremental Google Slides; requires online access) PDF (flat slides) Video (starts at 00h49m58s) I am grateful for everyone involved who made e-Rum 2020 possible. I cannot imagine having to cancel the on-site Milano conference that had planned for more than a year and then start over to re-organize and create a fabulous online experience for ~1,500 participants in such short notice.
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.
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;
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!
New release: startup 0.12.0 is now on CRAN. This version introduces support for processing some of the R startup files with a certain frequency, e.g. once per day, once per week, or once per month. See below for two examples.
startup::startup() is cross platform.
The startup package makes it easy to split up a long, complicated .Rprofile startup file into multiple, smaller files in a .Rprofile.d/ folder. For instance, setting R option repos in a separate file ~/.
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.
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(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).
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?
