PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

• Published in: Computer physics communications 2014-04, Vol.185 (4), p.1343-1353
• Main Authors: Barash, L.Yu, Shchur, L.N.
• Format: Article
• Language: English
• Subjects: GPGPU; Monte Carlo; Pseudorandom numbers; Random number generation; Random numbers; Statistical methods; Streaming SIMD extensions
• ISSN: 0010-4655; 1879-2944
• DOI: 10.1016/j.cpc.2014.01.007

The library PRAND for pseudorandom number generation for modern CPUs and GPUs is presented. It contains both single-threaded and multi-threaded realizations of a number of modern and most reliable generators recently proposed and studied in Barash (2011), Matsumoto and Tishimura (1998), L’Ecuyer (1999,1999), Barash and Shchur (2006) and the efficient SIMD realizations proposed in Barash and Shchur (2011). One of the useful features for using PRAND in parallel simulations is the ability to initialize up to 1019 independent streams. Using massive parallelism of modern GPUs and SIMD parallelism of modern CPUs substantially improves performance of the generators. Program title: PRAND Catalogue identifier: AESB_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AESB_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 45979 No. of bytes in distributed program, including test data, etc.: 23953564 Distribution format: tar.gz Programming language: Cuda C, Fortran. Computer: PC, workstation, laptop, or server with NVIDIA GPU (tested on Tesla X2070, Fermi C2050, GeForce GT540M) and with Intel or AMD processor. Operating system: Linux with CUDA version 5.0 or later. Should also run on MacOs, Windows, or UNIX. RAM: 4 Mbytes Classification: 4.13. Nature of problem: Any calculation requiring uniform pseudorandom number generator, in particular, Monte Carlo calculations. Any calculation or simulation requiring uncorrelated parallel streams of uniform pseudorandom numbers. Solution method: The library contains realization of a number of modern and reliable generators: MT19937, MRG32K3A and LFSR113. Also new realizations of the method based on parallel evolution of an ensemble of transformations of two-dimensional torus are included in the library: GM19, GM29, GM31, GM61, GM55, GQ58.1, GQ58.3 and GQ58.4. The library contains: single-threaded and multi-threaded realizations for GPU, single-threaded realizations for CPU, realizations for CPU based on SSE command set. Also, the library contains the abilities to jump ahead inside RNG sequence and to initialize independent random number streams with block splitting method for each of the RNGs. Restrictions: Nvidia Cuda Toolkit version 5.0 or later should be installed. To use GPU realizations, Nvidia GPU supporting CUDA and the