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• W2122422333 abstract "Although present X-ray scattering techniques can provide tremendous information on the nano-structural properties of materials that are valuable in the design and fabrication of energy-relevant nano-devices, a primary challenge remains in the analyses of such data. In this paper we describe a high-performance, flexible, and scalable Grazing Incidence Small Angle X-ray Scattering simulation algorithm and codes that we have developed on multi-core/CPU and many-core/GPU clusters. We discuss in detail our implementation, optimization and performance on these platforms. Our results show speedups of ~125x on a Fermi-GPU and ~20x on a Cray-XE6 24-core node, compared to a sequential CPU code, with near linear scaling on multi-node clusters. To our knowledge, this is the first GISAXS simulation code that is flexible to compute scattered light intensities in all spatial directions allowing full reconstruction of GISAXS patterns for any complex structures and with high-resolutions while reducing simulation times from months to minutes." @default.
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• W2122422333 date "2012-11-10" @default.
• W2122422333 modified "2023-09-24" @default.
• W2122422333 title "Massively parallel X-ray scattering simulations" @default.
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• W2122422333 doi "https://doi.org/10.5555/2388996.2389059" @default.
• W2122422333 hasPublicationYear "2012" @default.
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