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Parallel computing with R: A brief review
Author(s) -
Eddelbuettel Dirk
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1515
Subject(s) - computer science , parallel computing , compiler , software , computational statistics , data parallelism , spark (programming language) , parallelism (grammar) , focus (optics) , big data , supercomputer , theoretical computer science , operating system , programming language , physics , machine learning , optics
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