Inverse Modeling Nanostructures from X-Ray Scattering Data through Massive Parallelism
Authors: Abhinav Sarje (Lawrence Berkeley National Laboratory), Dinesh Kumar (Lawrence Berkeley National Laboratory), Singanallur Venkatakrishnan (Lawrence Berkeley National Laboratory), Slim Chourou (Lawrence Berkeley National Laboratory), Xiaoye S. Li (Lawrence Berkeley National Laboratory), Alexander Hexemer (Lawrence Berkeley National Laboratory)
Abstract: We consider the problem of reconstructing material nanostructures from grazing-incidence small-angle X-ray scattering (GISAXS) data obtained through experiments at synchrotron light-sources. This is an important tool for characterization of macromolecules and nano-particle systems applicable to applications such as design of energy-relevant nano-devices. Computational analysis of experimentally collected scattering data has been the primary bottleneck in this process.
We exploit the availability of massive parallelism in leadership-class supercomputers with multi-core and graphics processors to realize the compute-intensive reconstruction process. To develop a solution, we employ various optimization algorithms including gradient-based LMVM, derivative-free trust region-based POUNDerS, and particle swarm optimization, and apply these in a massively parallel fashion.
We compare their performance in terms of both quality of solution and computational speed. We demonstrate the effective utilization of up to 8,000 GPU nodes of the Titan supercomputer for inverse modeling of organic-photovoltaics (OPVs) in less than 15 minutes.
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