The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Techniques for empirical testing of parallel random number generators
ICS '98 Proceedings of the 12th international conference on Supercomputing
Discrete Mathematics
Software for uniform random number generation: distinguishing the good and the bad
Proceedings of the 33nd conference on Winter simulation
Interfaces and Implementations of Random Number Generators for JAVA Grande Appllications
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
A system of high-dimensional, efficient, long-cycle and portable uniform random number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
An Accelerator for Physics Simulations
Computing in Science and Engineering
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
ICPP '08 Proceedings of the 2008 37th International Conference on Parallel Processing
A fast high quality pseudo random number generator for nVidia CUDA
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
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Generating quality random numbers is a performance-critical application for many scientific simulations. Modern processing acceleration techniques such as: graphical co-processing units(GPUs), multi-core conventional CPUs; special purpose multi-core CPUs; and parallel computing approaches such as multi-threading on shared memory or message passing on clusters, all offer ways to speed up random number generation (RNG). Providing fast generators that are also portable across hardware and software platforms is non-trivial however, particularly since many of the powerful devices available at present do not yet support full 64-bit operations upon which many good RNG algorithms rely. We report performance data for a range of common RNG algorithms on devices including: GPUs; CellBE; multicore CPUs; and hybrids, and discuss algorithmic and implementation issues.