SCHEDULE: NOV 15-20, 2015

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Inverse Modeling Nanostructures from X-Ray Scattering Data through Massive Parallelism

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Abhinav Sarje, Dinesh Kumar, Singanallur Venkatakrishnan, Slim Chourou, Xiaoye S. Li, Alexander Hexemer

ROOM:Level 4 - Lobby

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.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

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

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