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future 1.15.0 – Lazy Futures are Now Launched if Queried

November 9, 2019
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future 1.15.0 – Lazy Futures are Now Launched if Queried

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...

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useR! 2019 Slides on Futures

July 12, 2019
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useR! 2019 Slides on Futures

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 Dist...

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startup – run R startup files once per hour, day, week, …

May 26, 2019
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startup – run R startup files once per hour, day, week, …

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...

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SatRday LA 2019 Slides on Futures

May 16, 2019
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SatRday LA 2019 Slides on Futures

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...

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SatRday Paris 2019 Slides on Futures

March 7, 2019
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SatRday Paris 2019 Slides on Futures

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: F...

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Parallelize a For-Loop by Rewriting it as an Lapply Call

January 11, 2019
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Parallelize a For-Loop by Rewriting it as an Lapply Call

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

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Maintenance Updates of Future Backends and doFuture

January 6, 2019
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Maintenance Updates of Future Backends and doFuture

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...

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future 1.9.0 – Output from The Future

July 22, 2018
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future 1.9.0 – Output from 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...

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R.devices – Into the Void

July 20, 2018
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R.devices – Into the Void

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...

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future.apply – Parallelize Any Base R Apply Function

June 22, 2018
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future.apply – Parallelize Any Base R Apply Function

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...

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