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Hardware accelerated scalable parallel random number generators for monte carlo methods

  • University of Tennessee
  • University of Tennessee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Monte Carlo methods often demand the generation of many random numbers to provide statistically meaningful results. Because generating random numbers is time consuming and error-prone, the Scalable Parallel Random Number Generators (SPRNG) library is widely used for Monte Carlo simulation. SPRNG supports fast, scalable random number generation with good statistical properties. In order to accelerate SPRNG, we develop a hardware accelerated version of SPRNG that produces identical results. To demonstrate HASPRNG for Reconfigurable Computing (RC) applications, we develop a Monte Carlo π-estimator for the Cray XD1 and XUP platforms. The RC MC π-estimator shows 8.1 times speedup over the 2.2GHz AMD Opteron processor in the Cray XD1.

Original languageEnglish
Title of host publication2008 IEEE International 51st Midwest Symposium on Circuits and Systems, MWSCAS
Pages177-180
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International 51st Midwest Symposium on Circuits and Systems, MWSCAS - Knoxville, TN, United States
Duration: Aug 10 2008Aug 13 2008

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference2008 IEEE International 51st Midwest Symposium on Circuits and Systems, MWSCAS
Country/TerritoryUnited States
CityKnoxville, TN
Period08/10/0808/13/08

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