A fast, high quality, and reproducible parallel lagged-Fibonacci pseudorandom number generator
Journal of Computational Physics
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
Relaxed Monte Carlo Linear Solver
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Antithetic Monte Carlo Linear Solver
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Efficient Monte Carlo Linear Solver with Chain Reduction and Optimization Using PLFG
HPCN Europe 2001 Proceedings of the 9th International Conference on High-Performance Computing and Networking
Parallelization of prime number generation using message passing interface
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Exploring pseudo- and chaotic random Monte Carlo simulations
Computers & Geosciences
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In this paper, a parallel pseudo-random generator, named PLFG, is presented. PLFG was designed specifically for MIMD parallel programming, implemented using Message Passing Interface (MPI) in C. It is highly scalable and with the default parameters chosen, it provides an astronomical period of at least 229 (223209 - 1). Its scalability and period is essentially limited only by the hardware architecture on which it is running on. An implementation in MPI guarantees portability across the large number of high-performance parallel computers, ranging from clusters of workstations to massively parallel processor machines, supported by MPI. PLFG has been subjected to the 2D Ising model Monte Carlo simulation test with the Wolff algorithm. Results from the test show that the quality of the pseudo-random numbers generated are comparable to that of other more commonly used parallel pseudo-random generator. Timing results show that PLFG is faster than some PPRNGs, and on par with others.