2004
DOI: 10.1007/978-3-540-30117-2_12
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Monte Carlo Radiative Heat Transfer Simulation on a Reconfigurable Computer

Abstract: Los Alamos National Laboratory, an affirmative actionlequal opportunity employer, is operated by the University of California for the US. Department of Energy under contract W-7405-ENG-36. By acceptance of this article, the publisher recognizes that the US. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or to allow others to do so, for U.S. Government purposes. Los Alamos National Laboratory requests that the publisher identify this arti… Show more

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Cited by 25 publications
(21 citation statements)
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“…Radiative Monte Carlo Simulation: M. Gokhale et al [19] presents an acceleration of Monte Carlo radiative heat transfer simulation on FPGA, with FLP numbers. The simulation traces photons emitted from the surfaces of a 2-D enclosure.…”
Section: Comparison Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…Radiative Monte Carlo Simulation: M. Gokhale et al [19] presents an acceleration of Monte Carlo radiative heat transfer simulation on FPGA, with FLP numbers. The simulation traces photons emitted from the surfaces of a 2-D enclosure.…”
Section: Comparison Resultsmentioning
confidence: 99%
“…Compared with the single-precision FLP design [19], our design doubles the throughput, uses 42% fewer HMULs, and has 16% smaller error in the photon counts, at the expense of consuming over three times more slices and an additional 49 BRAMs.…”
Section: Comparison Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…FPGAs have previously been used for Monte-Carlo simulations [4,5,1,8], but these were all developed from scratch using application-specific HDL. An automated method for compiling simulations into hardware was described in [7], but the actual implementation steps were performed by hand.…”
Section: Related Workmentioning
confidence: 99%
“…Probably for this reason, researchers have avoided applications that are "canonically" double precision floating point, including MD. Recent exceptions include [2,7].…”
Section: Precisionmentioning
confidence: 99%