Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Maximally equidistributed combined Tausworthe generators
Mathematics of Computation
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
Trading Computation Time for Synchronization Time in Spatial Distributed Simulation
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
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Parallel computing has been touted as the pinnacle of high performance digital computing by many. However, many problems remain intractable using deterministic algorithms. Randomized algorithms which are, in some cases, less efficient than their deterministic counterpart for smaller problem sizes, can overturn the intractability of various large scale problems. These algorithms, however, require a source of randomness. Pseudo-random number generators were created for many of these purposes. When performing computations on parallel machines, an additional criterion for randomized algorithms to be worthwhile is the availability of a parallel pseudo-random number generator. This paper presents an efficient algorithm for parallel pseudo-random number generation.