access the GPU through CUDA libraries and/or CUDA-accelerated programming languages, including C, C++ and Fortran.profiling GPU-accelerated R applications using the CUDA Profiler.įigure 1 shows that there are two ways to apply the computational power of GPUs in R:.calling your own parallel algorithms written in CUDA C/C++ or CUDA Fortran from R and.accelerating R computations using CUDA libraries.In this article, I will introduce the computation model of R with GPU acceleration, focusing on three topics: This way, R users can benefit from R’s high-level, user-friendly interface while achieving high performance. Therefore, R applications stand to benefit from GPU acceleration. R programs tend to process large amounts of data, and often have significant independent data and task parallelism. However, R, like many other high-level languages, is not performance competitive out of the box with lower-level languages like C++, especially for highly data- and computation-intensive applications. Many domain experts and researchers use the R platform and contribute R software, resulting in a large ecosystem of free software packages available through CRAN (the Comprehensive R Archive Network). R is a free software environment for statistical computing and graphics that provides a programming language and built-in libraries of mathematics operations for statistics, data analysis, machine learning and much more.