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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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Delayed Future(Slides from eRum 2018)

June 17, 2018
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Delayed Future(Slides from eRum 2018)

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, 2...

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future 1.8.0: Preparing for a Shiny Future

April 11, 2018
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future 1.8.0: Preparing for a Shiny 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 b...

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Performance: Avoid Coercing Indices To Doubles

April 1, 2018
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Performance: Avoid Coercing Indices To Doubles

x or x? That is the question. Assume that we have a vector $x$ of $n = 100,000$ random values, e.g. __ n x idxs y y typeof(idxs) "integer" __ typeof(idxs + 1) "double" __ typeof(1) "double" Note also that doubles (aka “numerics” in R) take up twice the amount of memory: __ object.size(idxs) 400040 bytes __...

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