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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/08/obstacles-to-performance-in-parallel-programming.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>Making your code run faster is often the primary goal when using parallel programming techniques in R, but sometimes the effort of converting your code to use a parallel framework leads only to disappointment, at least initially. Norman Matloff, author of <em><a rel="nofollow" href="https://www.amazon.com/dp/1466587016" target="_blank">Parallel Computing for Data Science: With Examples in R, C++ and CUDA</a>, </em>has shared <a rel="nofollow" href="http://heather.cs.ucdavis.edu/rparbookch2.pdf" target="_blank">chapter 2 of that book online</a>, and it describes some of the issues that can lead to poor performance. They include:</p>
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<li>Communications overhead, particularly an issue with fine-grained parallelism consisting of a very large number of relatively small tasks;</li>
<li>Load balance, where the computing resources aren't contributing equally to the problem;</li>
<li>Impacts from use of RAM and virtual memory, such as cache misses and page faults;</li>
<li>Network effects, such as latency and bandwidth, that impact performance and communication overhead;</li>
<li>Interprocess conflicts and thread scheduling; </li>
<li>Data access and other I/O considerations.</li>
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<p>The chapter is well worth a read for anyone writing parallel code in R (or indeed any programming language). It's also worth checking out Norm Matloff's keynote from the useR!2017 conference, embedded below.</p>
<p><iframe width="450" allowfullscreen="" src="https://channel9.msdn.com/Events/useR-international-R-User-conferences/useR-International-R-User-2017-Conference/KEYNOTE-Parallel-Computation-in-R-What-We-Want-and-How-We-Might-Get-It/player" height="288"></iframe></p>
<p>Norm Matloff: <a rel="nofollow" href="https://matloff.wordpress.com/2017/07/29/understanding-overhead-issues-in-parallel-computation/" target="_blank">Understanding overhead issues in parallel computation</a></p>
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