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Even though R was not developed for high performance computing (HPC), its popularity with scientists from a variety of disciplines, including engineering, mathematics, statistics, bioinformatics, etc. makes it an essential tool on HPC installations dedicated to academic research. Features such as C extensions, byte-compiled code and parallelisation allow for reasonable performance in single-node jobs. Thanks to R’s modular nature, users can customize the R functions available to them by installing packages from the Comprehensive R Archive Network ([https://cran.r-project.org/ CRAN]) into their home directories. | Even though R was not developed for high performance computing (HPC), its popularity with scientists from a variety of disciplines, including engineering, mathematics, statistics, bioinformatics, etc. makes it an essential tool on HPC installations dedicated to academic research. Features such as C extensions, byte-compiled code and parallelisation allow for reasonable performance in single-node jobs. Thanks to R’s modular nature, users can customize the R functions available to them by installing packages from the Comprehensive R Archive Network ([https://cran.r-project.org/ CRAN]) into their home directories. | ||
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Compute Canada user Julie Fortin has written a blog post, [https://medium.com/the-nature-of-food/how-to-run-your-r-script-with-compute-canada-c325c0ab2973 "How to run your R script with Compute Canada"], which you might find useful. | Compute Canada user Julie Fortin has written a blog post, [https://medium.com/the-nature-of-food/how-to-run-your-r-script-with-compute-canada-c325c0ab2973 "How to run your R script with Compute Canada"], which you might find useful. | ||