In the field of nanoparticle material science, Xray scattering techniques are widely used for characterization of macromolecules and particle systems (ordered, partially-ordered or custom) based on their structural pr...
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ISBN:
(纸本)9781479956180
In the field of nanoparticle material science, Xray scattering techniques are widely used for characterization of macromolecules and particle systems (ordered, partially-ordered or custom) based on their structural properties at the micro-and nano-scales. Numerous applications utilize these, including design and fabrication of energy-relevant nanodevices such as photovoltaic and energy storage devices. Due to its size, analysis of raw data obtained through present ultrafast light beamlines and X-ray scattering detectors has been a primary bottleneck in such characterization processes. To address this hurdle, we are developing high-performance parallel algorithms and codes for analysis of X-ray scattering data for several of the scattering methods, such as the Small Angle X-ray Scattering (SAXS), which we talk about in this paper. As an inverse modeling problem, structural fitting of the raw data obtained through SAXS experiments is a method used for extracting meaningful information on the structural properties of materials. Such fitting processes involve a large number of variable parameters and, hence, require a large amount of computational power. In this paper, we focus on this problem and present a high-performance and scalable parallel solution based on the reverse monte carlo simulation algorithm, on highly-parallel systems such as clusters of multicore CPUs and graphics processors. We have implemented and optimized our algorithm on generic multi-core CPUs as well as the Nvidia GPU architectures with C++ and CUDA. We also present detailed performance results and computational analysis of our code.
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