Exploring and Benchmarking High Performance & Scientific Computing using R R HPC Packages and Lower level compiled languages A Comparative Study

TL;DR

This paper compares the performance of R and lower-level compiled languages like C/C++ for high-performance and scientific computing, focusing on parallel computing techniques and R packages for HPC.

Contribution

It provides a benchmarking study of R HPC packages against C/C++, highlighting scalability and usability aspects for scientific computing.

Findings

R packages like Rmpi, Rcpp, snow, and snowfall facilitate parallel computing in R.

Benchmark results compare performance metrics of R and C/C++ implementations.

Insights into the suitability of R as a primary language for HPC applications.

Abstract

R is a robust open-source programming language mainly used for statistical computing . Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel/concurrent computing. This paper presents an overview of techniques for parallel computing with R on ACI (a PSU Infrastructure) and benchmark it with C/C++. We review the scalabilty concern of R, and look at the simplicity of using R as a primary language in Coding for HPC. We will look at the various R packages for HPC like Rmpi, Rcpp, snow and snowfall. We utilize a series of algorithms to benchmark and will illustrate each benchmark with a representative graph for ease of understanding. The paper concludes with a better understanding of which language to use when in high performance computing .