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Parallel computing with R: A brief review

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Abstract Parallel computing has established itself as another standard method for applied research and data analysis. The R system, being internally constrained to mostly singly‐threaded operations, can nevertheless be used along with different parallel computing approaches. This brief review covers OpenMP and Intel TBB at the CPU‐ and compiler level, moves to process‐parallel approaches before discussing message‐passing parallelism and big data technologies for parallel processing such as Spark, Docker and Kubernetes before concluding with a focus on the future package integrating many of these approaches. This article is categorized under: Algorithms and Computational Methods > Methods for High Performance Computing Software for Computational Statistics > Software/Statistical Software Software for Computational Statistics > High Performance Software

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Software for Computational Statistics > High Performance Software
Software for Computational Statistics > Software/Statistical Software
Algorithms and Computational Methods > Methods for High Performance Computing

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