Massively Parallel Random Number Generation

Author(s)

Claudia Plant, Christian Böhm

Abstract

Random numbers are of high importance for many applications, e.g. simulation, optimization, and data mining. Unlike in information security, in these applications the demands on the quality of the random numbers are only moderate while the most important issue is the runtime efficiency. We propose in this paper new SIMD (Single Instruction, Multiple Data) and MIMD (Multiple Instructions, Multiple Data) parallel methods for Linear Congruential Generators (LCG), the most widespread class of fast pseudo-random number generators. In particular, we propose algorithms for the well-known 48-bit LCG used in the Java-class Random and in the method drand48() of C++ for processors using AVX (Advanced Vector eXtensions) and OpenMP. Our focus is on consistency with the original methods which facilitates debugging and enables the user to exactly reproduce previous non-parallel experiments in a SIMD and MIMD environment. Our experimental evaluation demonstrates the superiority of our algorithms.

Organisation(s)

Research Network Data Science, Research Group Data Mining and Machine Learning

External organisation(s)

Ludwig-Maximilians-Universität München

Pages

413-419

No. of pages

7

DOI

https://doi.org/10.1109/BigData50022.2020.9377814

Publication date

2020

Peer reviewed

Yes

Austrian Fields of Science 2012

102033 Data mining

Keywords

ASJC Scopus subject areas

Information Systems and Management, Information Systems, Safety, Risk, Reliability and Quality, Computer Networks and Communications

Portal url

https://ucris.univie.ac.at/portal/en/publications/massively-parallel-random-number-generation(7c17aedf-a926-4809-a2ba-39241534f836).html