![]() Writing threaded code directly is messy, so higher-level systems have been developed to hide the messy details from the programmer, thus making his/her life far easier. The work of the program is divided among the threads, and due to the simultaneity, we potentially can achieve a big speedup. A key point is that the threads share memory, making it easy for them to cooperate. ![]() This means that several invocations, called threads, of the given program are running simultaneously, typically one thread per core. ![]() Most programs running on multicore systems are threaded. ![]() I’ll assume that you know R well, and have some familiarity with C/C++. This tutorial is adapted from my book, Parallel Computation for Data Science: with Examples in R, C/C++ and CUDA, to be published in June 2015. Use of the latter will be kept to basics, so if you are also new to Rcpp, you’ll learn how to use that too. This blog post will present a short tutorial on OpenMP, including calling OpenMP code from R, using Rcpp. (For Macs, you need the OpenMP-enabled version of Mac’s clang compiler.) The most popular way to program on multicore machines is to use OpenMP, a C/C++ (and FORTRAN) callable system that runs on Linux, Mac and Windows. In addition, large multicore systems can be “rented” on Amazon EC2 and so on. Dual-core is standard, quad-core is easily attainable for the home, and larger systems, say 16-core, are easily within reach of even smaller research projects.
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