期刊文献+

基于超多核心平台的Knuth39并行化实现及性能分析

Implementation and performance analysis of Knuth39 parallelization based on many integrated core platform
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摘要 针对Knuth39随机数发生器运行速度慢的问题,提出了一种基于超多核心(MIC)平台的Knuth39并行化方法。首先,将Knuth39发生器的随机数序列以固定间隔划分成多个子序列;然后,每个线程从各子序列的起点开始生成随机数;最后,将各个线程生成的随机数序列组合成最终的序列。实验结果表明,并行化后Knuth39通过了Test U01的452项测试,与串行程序相同。同中央处理器(CPU)单线程相比,并行化后MIC平台下的最优加速比可达到15.69倍。所提方法有效地提高了Knuth39发生器的运行速度,并且保证了生成序列的随机性,更加适用于高性能计算领域。 To solve the low running speed problem of Knuth39 random number generator, a Knuth39 parallelization method based on Many Integrated Core( MIC) platform was proposed. Firstly, the random number sequence of Knuth39 generator was divided into subsequences by regular interval. Then, the random numbers were generated by every thread from the corresponding subsequence's starting point. Finally, the random number sequences generated by all threads were combined into the final sequence. The experimental results show that the parallelized Knuth39 generator successfully passed 452 tests of Test U01, the results are the same as those of Knuth39 generator without parallelization. Compared with single thread on Central Processing Unit( CPU), the optimal speed-up ratio on MIC platform is 15. 69 times. The proposed method improves the running speed of Knuth39 generator effectively, ensures the randomness of the generated sequences, and it is more suitable for high performance computing.
出处 《计算机应用》 CSCD 北大核心 2015年第1期58-61,共4页 journal of Computer Applications
基金 陕西省自然科学基础研究计划项目(2013JM8028)
关键词 随机数发生器 Knuth39 并行化 超多核心 TestU01 random number generator Knuth39 parallelization Many Integrated Core(MIC) Test U01
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  • 1SALMON J K, MORAES M A, DROR R O, et al. Parallel random numbers: as easy as 1, 2, 3 [ C]//SC'11 : Proceedings of 2011 In- ternational Conference for High Performance Computing, Networ- king, Storage and Analysis. New York: ACM, 2011: Article No. 16.
  • 2WANG M, GUO F, QU H, et al. Combined random number gener- ators: a review [C]//ICCSN 2011: Proceedings of the 2011 IEEE 3rd International Conference on Communication Software and Net- works. Piscataway: IEEE, 2011:443-447.
  • 3OYA K, KITADA T, TANAKA S. Study on random number genera- tor in Monte Carlo code [J]. Atomic Energy Society, 2011, 10(4) : 301 - 309.
  • 4LEHMER D H. Mathematical methods in large-scale computing u- nits [ C]// Proceedings of the Second Symposium on Large-Scale Digital Calculating Machinery. Cambridge: Harvard University Press, 1951:141-146.
  • 5KNUTH D E. The art of computer pwgramming, volume 2: seminumer- ical algorithms [ M]. 3rd ed. Boston: Addison-Wesley, 1998: 108.
  • 6MARTON K, SUCIU A, PETRICEAN D. A parallel unpredictable random number generator [ C]//Proceedings of the 2011 10th Roe- dunet International Conference: Networking in Education and Re- search. Piscataway: IEEE, 2011:1-5.
  • 7GAO S, PETERSON G D. GASPRNG: GPU accelerated sealable parallel random number generator library [ J]. Computer Physics Communications, 2013, 184(4) : 1241 - 1249.
  • 8BRADLEY T, du TOIT J, GILES M, et al. Parallelisation tech- niques for random number generators [ M]//GPU Computing Gems Emerald Edition. San Francisco: Morgan Kaufmann, 2011:231 - 246.
  • 9BARASH L Y, SHCHUR L N. RNGSSELIB: program library for random number generation, SSE2 realization [ J]. Computer Physics Communications, 2011, 182(7) : 1518 - 1527.
  • 10BARASH L Y, SHCHUR L N. PRAND: GPU accelerated parallel random number generation library: using most reliable algorithms and applying parallelism of modem GPUs and CPUs [ J]. Computer Physics Communications, 2014, 185(4) : 1343 - 1353.

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