Pseudorandom Numbers Generation for Monte Carlo Simulations on GPUs: OpenCL Approach

Author:

Demchik Vadim

Publisher

Springer International Publishing

Reference47 articles.

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2. Ferrenberg, A.M., Landau, D.P., Wong, Y.J.: Monte Carlo simulations: hidden errors from “good” random number generators. Phys. Rev. Lett. 69, 3382–3384 (1992)

3. Janke, W.: Pseudo random numbers: generation and quality checks. In: Grotendorst, J., Marx, D., Muramatsu, A. (eds.) Quantum Simulations of Complex Many-Body Systems, vol. 10, p. 447. John von Neumann Institute for Computing, Julich (2002)

4. Salmon, J., Moraes, M., Dror, R., Shaw, D.: Parallel random numbers: as easy as 1, 2, 3. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–12. http://www.deshawresearch.com/resources_random123.html (2011). Cited 6 Feb 2014

5. L’Ecuyer, P.: Uniform random number generators. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science, pp. 1625–1630. Springer, Berlin/Heidelberg (2011)

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1. An OpenCL library for parallel random number generators;The Journal of Supercomputing;2019-01-17

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