Most applications in R run pretty quickly. But these days, many R users are faced with huge datasets and/or long-running applications, making parallel computation attractive. 🧵 1/
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If you've done this, you likely have used the 'parallel' package, either directly or via 'foreach' or 'future'. This uses what is called "message-passing" in the parallel computation community. 2/
The other main parallel comp paradigm, "threading," is typically faster, and many consider it clearer. Unfortunately, R does not have native threading. Packages that need great speed, such as data.table, must resort to calling a C/C++ library, OpenMP, say through Rcpp. 3/
Parallel computation is a long time interest of mine (research and a book), so I am pleased to announce a new package, Rthreads, that provides threading for R! It's a full rewrite of my old package, Rdsm, located in http://github.com/matloff/Rthreads. 4/
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