Massively Parallel Random Number Generation

MCML Authors

Christian Böhm

Prof. Dr.

Principal Investigator

* Former Principal Investigator

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.

inproceedings

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IEEE BigData 2020

IEEE International Conference on Big Data. Virtual, Dec 10-13, 2020.

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Authors

C. Böhm • C. Plant

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Links

DOI

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Research Area

 A3 | Computational Models