Accelerating Pseudo-Random Number Generator for MCNP on GPU

Gong, Chunye; Liu, Jie; Chi, Lihua; Hu, Qingfeng; Long, Deng; Zhang, Gong; Simos, Theodore E.; Psihoyios, George; Tsitouras, Ch.

doi:10.1063/1.3497967

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Modern Technologies in Materials, Mechanics and Intelligent Systems sciences and the other computational sciences. One basis of Monte Carlo simulation is random number generation [19]. The Monte Carlo simulation for radio frequency discharges was studied by Settaouti and Settaouti [20].…”

Section: Numerical Solutionsmentioning

confidence: 99%

An Overview of the Research of Plasma Sheath

Zhao

Bao

Gong

2014

AMR

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The plasma sheath can be regarded as a model of a phenomenon of spacecraft reentry or hypersonic vehicles and causes so called radio blackout, which interferes with radio signals. This paper gives a survey on several aspects of plasma sheath, including the characteristic of plasma sheath, experiments to deal with the blackout and numerical solutions. The numerical methods includes WKB, finite-difference time-domain method, particle-in-cell method, CFD based method and Monte Carlo method. Some discussions are also presented.

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Section: Numerical Solutionsmentioning

confidence: 99%

An Overview of the Research of Plasma Sheath

Zhao

Bao

Gong

2014

AMR

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“…Ref. [48] implemented PRNG on GPU for an MC particles transport code and observed up to 8.1× speed-up compared to implementation on CPU with 4 to 6 cores. Furthermore, [53] studied the speed and quality of the proposed xorgenGP PRNG, finding a similar speed to the MTGP32 generator.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Pseudo-Random Number Generation on GPU Cards

et al. 2021

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Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudo-random number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.

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GASPRNG: GPU accelerated scalable parallel random number generator library

Gao

Peterson

2013

Computer Physics Communications

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Accelerating Pseudo-Random Number Generator for MCNP on GPU

Cited by 5 publications

References 7 publications

An Overview of the Research of Plasma Sheath

An Overview of the Research of Plasma Sheath

Evaluation of Pseudo-Random Number Generation on GPU Cards

GASPRNG: GPU accelerated scalable parallel random number generator library

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