Testing parallel random number generators

Authors:
Ashok Srinivasan;Michael Mascagni;David Ceperley
Affiliations:
Department of Computer Science, Florida State University, Tallahassee, FL;Department of Computer Sciecne, Florida State University, Tallahassee, FL;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
Parallel Computing
Year:
2003

Citing 12
Cited 13

Random number generators for MIMD parallel processors

Journal of Parallel and Distributed Computing
Pseudo-random trees: multiple independent sequence generators for parallel and branching computations

Journal of Computational Physics
Lagged-Fibonacci random number generators on parallel computers

Parallel Computing
A fast, high quality, and reproducible parallel lagged-Fibonacci pseudorandom number generator

Journal of Computational Physics
Controlling correlations in parallel Monte Carlo

Parallel Computing
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms

The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Bad subsequences of well-known linear congruential pseudorandom number generators

ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Parallel linear congruential generators with prime moduli

Parallel Computing
Algorithm 806: SPRNG: a scalable library for pseudorandom number generation

ACM Transactions on Mathematical Software (TOMS)
Computer Performance Modeling Handbook

Computer Performance Modeling Handbook
Implementation of a portable and reproducible parallel pseudorandom number generator

Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Close-Point Spatial Tests and Their Application to Random Number Generators

Operations Research

Parallel linear congruential generators with Sophie-Germain moduli

Parallel Computing
Parallel Implementation of Stochastic Simulation for Large-scale Cellular Processes

HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
A hardware framework for the fast generation of multiple long-period random number streams

Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Scalable parallel multiple recursive generators of large order

Parallel Computing
A flexible and scalable experimentation layer

Proceedings of the 40th Conference on Winter Simulation
A plug-in-based architecture for random number generation in simulation systems

Proceedings of the 40th Conference on Winter Simulation
A decentralized parallel implementation for parallel tempering algorithm

Parallel Computing
An FPGA implementation of a parallelized MT19937 uniform random number generator

EURASIP Journal on Embedded Systems - FPGA supercomputing platforms, architectures, and techniques for accelerating computationally complex algorithms
Decentralized replica exchange parallel tempering: an efficient implementation of parallel tempering using MPI and SPRNG

ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
On parallel asset-liability management in life insurance: a forward risk-neutral approach

Parallel Computing
Building a multi-FPGA virtualized restricted boltzmann machine architecture using embedded MPI

Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
Trading Computation Time for Synchronization Time in Spatial Distributed Simulation

PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
An agent-based approach to immune modelling

ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Monte Carlo computations are considered easy to parallelize. However, the results can be adversely affected by defects in the parallel pseudorandom number generator used. A parallel pseudorandom number generator must be tested for two types of correlations--(i) intra-stream correlation, as for any sequential generator, and (ii) inter-stream correlation for correlations between random number streams on different processes. Since bounds on these correlations are difficult to prove mathematically, large and thorough empirical tests are necessary. Many of the popular pseudorandom number generators in use today were tested when computational power was much lower, and hence they were evaluated with much smaller test sizes.This paper describes several tests of pseudorandom number generators, both statistical and application-based. We show defects in several popular generators. We describe the implementation of these tests in the SPRING [ACM Trans. Math. Software 26 (2000) 436; SPRNG--scalable parallel random number generators. SPRNG 1.0--http://www.ncsa.uiuc.edu/ Apps/SPRNG; SPRNG 2.0--http://sprng.cs.fsu.edu] test suite and also present results for the tests conducted on the SPRNG generators. These generators have passed some of the largest empirical random number tests.