Skip to main content

News about Scitation

In December 2016 Scitation will launch with a new design, enhanced navigation and a much improved user experience.

To ensure a smooth transition, from today, we are temporarily stopping new account registration and single article purchases. If you already have an account you can continue to use the site as normal.

For help or more information please visit our FAQs.

banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
/content/aip/journal/adva/6/4/10.1063/1.4945731
1.
1.D.P. Kroese, T. Brereton, T. Taimre, and Z.I. Botev, Wiley Interdiscip. Rev. Comput. Stat. 6, 386 (2014).
http://dx.doi.org/10.1002/wics.1314
2.
2.W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice (1998).
3.
3.J.A. Anderson, E. Jankowski, T.L. Grubb, M. Engel, and S.C. Glotzer, J. Comput. Phys. 254, 27 (2013).
http://dx.doi.org/10.1016/j.jcp.2013.07.023
4.
4.J.-S. Wu, S.-Y. Chou, U.-M. Lee, Y.-L. Shao, and Y.-Y. Lian, J. Fluids Eng. 127, 1161 (2005).
http://dx.doi.org/10.1115/1.2062807
5.
5.J.A. Anderson, C.D. Lorenz, and A. Travesset, J. Comput. Phys. 227, 5342 (2008).
http://dx.doi.org/10.1016/j.jcp.2008.01.047
6.
6.List of random number generators. '' [Online]. see: http://en.wikipedia.org/wiki/List_of_random_number_generators.
7.
7.S. Harase, Math. Comput. Simul. 100, 103 (2014).
http://dx.doi.org/10.1016/j.matcom.2014.02.002
8.
8.P. L’Ecuyer and T.H. Andres, Math. Comput. Simul. 44, 99 (1997).
http://dx.doi.org/10.1016/S0378-4754(97)00052-9
9.
9.P.D. Coddington and S. Ko, in Proc. 12th Int. Conf. Supercomput. - ICS ’98 (ACM Press, New York, New York, USA, 1998), pp. 282288.
http://dx.doi.org/10.1145/277830.277895
10.
10.M. Mascagni, SIAM News 32, 221 (1999).
11.
11.M.J. Goldsworthy, Comput. Fluids 94, 58 (2014).
http://dx.doi.org/10.1016/j.compfluid.2014.01.033
12.
12.Y. Lutsyshyn, Comput. Phys. Commun. 187, 162 (2015).
http://dx.doi.org/10.1016/j.cpc.2014.09.016
13.
13.B. Block, P. Virnau, and T. Preis, Comput. Phys. Commun. 1549 (2010).
14.
14.V. Kindratenko, Comput. Sci. Eng. 14, 8 (2012).
http://dx.doi.org/10.1109/MCSE.2012.55
15.
15.CUDA CURAND Library, Nvidia CUDA Toolkits. '' [Online]. see: http://developer.nvidia.com/cuda-toolkit-65.
16.
16.“PGI | Support | Release Information.” [Online]. see: https://www.pgroup.com/support/release.htm.
17.
17.Nvidia. '' [Online]. see: http://www.nvidia.com.
18.
18.Tuning a Monte Carlo Algorithm on GPUs. '' [Online]. see: http://www.pgroup.com/lit/articles/insider/v2n1a4.htm.
19.
19.G. Marsaglia, J. Stat. Softw. 8, 1 (2003).
20.
20.H. Nguyen, GPU Gems 3, 1st ed. (Addison-Wesley Professional, 2007).
21.
21.National Institute of Standards and Technology. '' [Online]. see: http://www.csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf.
22.
22.W.J. ENT. (1998).
23.
23.S.K. Miller and P. K. W., Commun. ACM 1192 (1988).
http://aip.metastore.ingenta.com/content/aip/journal/adva/6/4/10.1063/1.4945731
Loading
/content/aip/journal/adva/6/4/10.1063/1.4945731
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aip/journal/adva/6/4/10.1063/1.4945731
2016-04-05
2016-12-07

Abstract

The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number generation with good statistical properties on GPU. In this study, a GPU-based parallel pseudo random number generator (GPPRNG) have been proposed to use in high performance computing systems. According to the type of GPU memory usage, GPU scheme is divided into two work modes including GLOBAL_MODE and SHARED_MODE. To generate parallel random numbers based on the independent sequence method, the combination of middle-square method and chaotic map along with the Xorshift PRNG have been employed. Implementation of our developed PPRNG on a single GPU showed a speedup of 150x and 470x (with respect to the speed of PRNG on a single CPU core) for GLOBAL_MODE and SHARED_MODE, respectively. To evaluate the accuracy of our developed GPPRNG, its performance was compared to that of some other commercially available PPRNGs such as MATLAB, FORTRAN and Miller-Park algorithm through employing the specific standard tests. The results of this comparison showed that the developed GPPRNG in this study can be used as a fast and accurate tool for computational science applications.

Loading

Full text loading...

/deliver/fulltext/aip/journal/adva/6/4/1.4945731.html;jsessionid=5aanrEuytTIhJjlS1im3ggpX.x-aip-live-06?itemId=/content/aip/journal/adva/6/4/10.1063/1.4945731&mimeType=html&fmt=ahah&containerItemId=content/aip/journal/adva
true
true

Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
/content/realmedia?fmt=ahah&adPositionList=
&advertTargetUrl=//oascentral.aip.org/RealMedia/ads/&sitePageValue=aipadvances.aip.org/6/4/10.1063/1.4945731&pageURL=http://scitation.aip.org/content/aip/journal/adva/6/4/10.1063/1.4945731'
Right1,Right2,Right3,