Parallel Computing With R: A Brief Review

TL;DR

This paper reviews various parallel computing approaches compatible with R, including CPU-level, process-parallel, message-passing, and big data technologies, highlighting future integration prospects.

Contribution

It provides a comprehensive overview of existing parallel computing methods for R and discusses upcoming package developments integrating these approaches.

Findings

OpenMP and Intel TBB enable CPU-level parallelism in R

Process-parallel and message-passing methods expand R's capabilities

Big data tools like Spark, Docker, and Kubernetes facilitate scalable data analysis

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.