On parallel random number generation for accelerating simulations of communication systems

Abstract. Powerful compute clusters and multi-core systems have become widely available in research and industry nowadays. This boost in utilizable computational power tempts people to run compute-intensive tasks on those clusters, either for speed or accuracy reasons. Especially Monte Carlo simulations with their inherent parallelism promise very high speedups. Nevertheless, the quality of Monte Carlo simulations strongly depends on the quality of the employed random numbers. In this work we present a comprehensive analysis of state-of-the-art pseudo random number generators like the MT19937 or the WELL generator used for parallel stream generation in different settings. These random number generators can be realized in hardware as well as in software and help to accelerate the analysis (or simulation) of communications systems. We show that it is possible to generate high-quality parallel random number streams with both generators, as long as some configuration constraints are met. We furthermore depict that distributed simulations with those generator types are viable even to very high degrees of parallelism.

[1]  David Kearney,et al.  An FPGA Implementation of a Parallelized MT19937 Uniform Random Number Generator , 2009, EURASIP J. Embed. Syst..

[2]  Khaled Benkrid,et al.  Mersenne Twister Random Number Generation on FPGA, CPU and GPU , 2009, 2009 NASA/ESA Conference on Adaptive Hardware and Systems.

[3]  Michael Mascagni,et al.  Testing parallel random number generators , 2003, Parallel Comput..

[4]  Mark A. Moraes,et al.  Parallel random numbers: As easy as 1, 2, 3 , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[5]  Abbes Amira,et al.  High Performance FPGA Implementation of the Mersenne Twister , 2008, 4th IEEE International Symposium on Electronic Design, Test and Applications (delta 2008).

[6]  M. Aalabaf-Sabaghi,et al.  Monte Carlo Methods and Models in Finance and Insurance , 2011 .

[7]  Deian Stefan,et al.  A hardware framework for the fast generation of multiple long-period random number streams , 2008, FPGA '08.

[8]  Pierre L'Ecuyer,et al.  Fast random number generators based on linear recurrences modulo 2: overview and comparison , 2005, Proceedings of the Winter Simulation Conference, 2005..

[9]  Pierre L'Ecuyer,et al.  TestU01: A C library for empirical testing of random number generators , 2006, TOMS.

[10]  Pierre L'Ecuyer,et al.  Pseudorandom Number Generators , 2010 .

[11]  Mamadou Kaba Traoré,et al.  Distribution of random streams for simulation practitioners , 2013, Concurr. Comput. Pract. Exp..

[12]  Pierre L'Ecuyer,et al.  Efficient Jump Ahead for 2-Linear Random Number Generators , 2006, INFORMS J. Comput..