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Random Number Generation and Simulation on Vector and Parallel Computers
 LECTURE NOTES IN COMPUTER SCIENCE 1470
, 1998
"... Pseudorandom numbers are often required for simulations performed on parallel computers. The requirements for parallel random number generators are more stringent than those for sequential random number generators. As well as passing the usual sequential tests on each processor, a parallel rand ..."
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Cited by 14 (10 self)
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Pseudorandom numbers are often required for simulations performed on parallel computers. The requirements for parallel random number generators are more stringent than those for sequential random number generators. As well as passing the usual sequential tests on each processor, a parallel random number generator must give dierent, independent sequences on each processor. We consider the requirements for a good parallel random number generator, and discuss generators for the uniform and normal distributions. We also describe a new class of generators for the normal distribution (based on a proposal by Wallace). These
Distribution of Lattice Points
, 2006
"... We discuss the lattice structure of congruential random number generators and examine figures of merit. Distribution properties of lattice measures in various dimensions are demonstrated by using large numerical data. Systematic search methods are introduced to diagnose multiplier areas exhibiting g ..."
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We discuss the lattice structure of congruential random number generators and examine figures of merit. Distribution properties of lattice measures in various dimensions are demonstrated by using large numerical data. Systematic search methods are introduced to diagnose multiplier areas exhibiting good, bad and worst lattice structures. We present two formulae to express multipliers producing worst and bad laice points. The conventional criterion of normalised lattice rule is also questioned and it is shown that this measure used with a fixed threshold is not suitable for an effective discrimination of lattice structures. Usage of percentiles represents different dimensions in a fair fashion and provides consistency for different figures of merits.
Performance and Quality of Random Number Generators
"... Abstract — Random number generation continues to be a critical component in much of computational science and the tradeoff between quality and computational performance is a key issue for many numerical simulations. We review the performance and statistical quality of some well known algorithms for ..."
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Abstract — Random number generation continues to be a critical component in much of computational science and the tradeoff between quality and computational performance is a key issue for many numerical simulations. We review the performance and statistical quality of some well known algorithms for generating pseudo random numbers. Graphical Processing Units (GPUs) are a powerful platform for accelerating computational performance of simulations and random numbers can be generated directly within GPU code or from hosting CPU code. We consider an alternative approach using high quality and genuinely “random ” deviates generated using a Quantum device and we report on how such a PCI bus device can be linked to a CPU program. We discuss computational performance and statistical quality tradeoffs of this architectural model for Monte Carlo simulations such as the Ising system. Keywords: quantum random number generation; GPU; CUDA. 1.