Accelerating Pseudo-Random Number Generator for MCNP on GPU

被引：0

作者：

Gong, Chunye ^{[1
]}

Liu, Jie ^{[1
]}

Chi, Lihua ^{[1
]}

Hu, Qingfeng ^{[1
]}

Deng, Li ^{[2
]}

Gong, Zhenghu ^{[1
]}

机构：

[1] Natl Univ Def Technol, Dept Comp Sci, Changsha, Hunan, Peoples R China

[2] Inst Appl Phy & Computat Math, Beijing, Peoples R China

来源：

NUMERICAL ANALYSIS AND APPLIED MATHEMATICS, VOLS I-III | 2010年 / 1281卷

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

Random Number; PRNG; MCNP; Monte Carlo; GPU;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Pseudo-random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N-Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA's GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.

引用

页码：1335 / +

页数：2

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