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..
