31,256 research outputs found
Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units
Basic uniform pseudo-random number generators are implemented on ATI Graphics
Processing Units (GPU). The performance results of the realized generators
(multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR,
RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The
obtained speed-up factor is hundreds of times in comparison with CPU. RANLUX
generator is found to be the most appropriate for using on GPU in Monte Carlo
simulations. The brief review of the pseudo-random number generators used in
modern software packages for Monte Carlo simulations in high-energy physics is
present.Comment: 31 pages, 9 figures, 3 table
Analysis of Random Number Generators Using Monte Carlo Simulation
Revisions are almost entirely in the introduction and conclusion. Results are
unchanged, however the comments and recommendations on different generators
were changed, and more references were added.Comment: Email: [email protected] 16 pages, Latex with 1 postscript figure.
NPAC technical report SCCS-52
An experimental exploration of Marsaglia's xorshift generators, scrambled
Marsaglia proposed recently xorshift generators as a class of very fast,
good-quality pseudorandom number generators. Subsequent analysis by Panneton
and L'Ecuyer has lowered the expectations raised by Marsaglia's paper, showing
several weaknesses of such generators, verified experimentally using the
TestU01 suite. Nonetheless, many of the weaknesses of xorshift generators fade
away if their result is scrambled by a non-linear operation (as originally
suggested by Marsaglia). In this paper we explore the space of possible
generators obtained by multiplying the result of a xorshift generator by a
suitable constant. We sample generators at 100 equispaced points of their state
space and obtain detailed statistics that lead us to choices of parameters that
improve on the current ones. We then explore for the first time the space of
high-dimensional xorshift generators, following another suggestion in
Marsaglia's paper, finding choices of parameters providing periods of length
and . The resulting generators are of extremely
high quality, faster than current similar alternatives, and generate
long-period sequences passing strong statistical tests using only eight logical
operations, one addition and one multiplication by a constant
- …