clrng: A tool set for parallel random numbergeneration on GPUs in R

TL;DR

The paper presents clrng, an R package that enables fast, parallel random number generation on GPUs using OpenCL, improving R's computational speed and reproducibility for simulations.

Contribution

Introduces clrng, a novel R package integrating GPU-based parallel RNG with reproducibility features, enhancing statistical computing in R.

Findings

Significantly speeds up random number generation in R using GPU parallelism.

Ensures reproducible and independent streams of random numbers across sessions.

Flexible and portable, suitable for various applications beyond RNG.

Abstract

We introduce the R package clrng which leverages the gpuR package and is able to generate random numbers in parallel on a Graphics Processing Unit (GPU) with the clRNG (OpenCL) library. Parallel processing with GPU's can speed up computationally intensive tasks, which when combined with R, it can largely improve R's downsides in terms of slow speed, memory usage and computation mode. clrng enables reproducible research by setting random initial seeds for streams on GPU and CPU, and can thus accelerate several types of statistical simulation and modelling. The random number generator in clrng guarantees independent parallel samples even when R is used interactively in an ad-hoc manner, with sessions being interrupted and restored. This package is portable and flexible, developers can use its random number generation kernel for various other purposes and applications.