David A. Bader
IEEE Fellow
AAAS Fellow
Professor
College of Computing
Georgia Tech
Atlanta, GA 30332


 
 

 

On the Design of Fast Pseudo-Random Number Generators for the Cell Broadband Engine and an Application to Risk Analysis

Numerical simulations in computational physics, biology, and finance, often require the use of high quality and efficient parallel random number generators. We design and optimize several parallel pseudo random number generators on the Cell Broadband Engine, with minimal correlation between the parallel streams: the linear congruential generator (LCG) with 64-bit prime addend and the Mersenne Twister (MT) algorithm. As compared with current Intel and AMD microprocessors, our Cell/B.E. LCG and MT implementations achieve a speedup of 33 and 29, respectively. We also explore two normalization techniques, Gaussian averaging method and Box Mueller Polar/ Cartesian, that transform uniform random numbers to a Gaussian distribution. Using these fast generators we develop a parallel implementation of Value at Risk, a commonly used model for risk assessment in financial markets. To our knowledge we have designed and implemented the fastest parallel pseudo random number generators on the Cell/B.E..

Publication History

Versions of this paper appeared as:
  1. D.A. Bader, A. Chandramowlishwaran, and V. Agarwal, ``On the Design of Fast Pseudo-Random Number Generators for the Cell Broadband Engine and an Application to Risk Analysis,'' The 37th International Conference on Parallel Processing (ICPP 2008), Portland, OR, September 8-12, 2008.

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Last updated: February 16, 2009

 




Computational Biology



Parallel Computing



Combinatorics