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Performance of inverse atomistic scale fracture modeling on GPGPU architectures

A.P. Iliopoulos, J.G. Michopoulos, S.G. Lambrakos, N. Bernstein
Science Applications International Corporation, c/o Computational Multiphysics, Systems Lab., Center for Computational Material Science, Naval Research Laboratory, Code 6394, Washington, DC 20375, USA
Journal of Computational Science, 2011

@article{Iliopoulos2011,

   title={“PerformanceofinverseatomisticscalefracturemodelingonGPGPUarchitectures”},

   journal={“JournalofComputationalScience”},

   volume={“InPress},

   number={“”},

   pages={“-“},

   year={“2011”},

   note={“”},

   issn={“1877-7503”},

   doi={“DOI:10.1016/j.jocs.2010.12.005”},

   url={“http://www.sciencedirect.com/science/article/B9HC1-522SHTX-1/2/f975ef4b872a09bcfe2f14d32e00b74b”},

   author={“A.P.IliopoulosandJ.G.MichopoulosandS.G.LambrakosandN.Bernstein”},

   keywords={“Performanceanalysis”}

}

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The present work has been motivated by the continuous growth of General Purpose Graphic Processor Unit (GPGPU) technologies as well as the necessity of linking usability with multiscale materials processing and design. The inverse problem of determining the phenomenological interparticle Lenard-Jones potential governing the fracture dynamics of a two dimensional structure under tension, is used to examine the feasibility and efficiency of utilizing GPGPU architectures. The implementation of this inverse problem under a molecular dynamics framework provides verification of this methodology. The main contribution of this paper is a performance evaluation driven sensitivity analysis that is contacted on GPGPU-enabled hardware in order to examine efficiency relative to various combinations of GPGPU and Central Processing Unit (CPU) cores as a function of problem size. In particular, speedup factors are determined relative to various number of core combinations of a quad core processor.
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