Evaluation of pseudo-random number generation on GPU cards

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudo-random number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.

Original languageEnglish
Article number142
JournalComputation
Volume9
Issue number12
DOIs
Publication statusPublished - Dec 2021

Keywords

  • CUDA
  • Curand
  • GPU
  • PRNG

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science
  • Modelling and Simulation
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Evaluation of pseudo-random number generation on GPU cards'. Together they form a unique fingerprint.

Cite this