SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Foresight: Analysis That Matters for Data Reduction

Authors: Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Arvind T. Mohan, Ayan Biswas, John Patchett, Terece L. Turton, David H. Rogers, Daniel Livescu, and James Ahrens (Los Alamos National Laboratory)

Abstract: As the computational power of supercomputers increases, so does simulation size, which in turn produces orders-of-magnitude more data. Because generated data often exceed the simulation’s disk quota, many simulations would stand to benefit from data-reduction techniques to reduce storage requirements. Such techniques include autoencoders, data compression algorithms and sampling. Lossy compression techniques can significantly reduce data size, but such techniques come at the expense of losing information that could result in incorrect post hoc analysis results. To help scientists determine the best compression they can get while keeping their analyses accurate, we have developed Foresight, an analysis framework that enables users to evaluate how different data-reduction techniques will impact their analyses. We use particle data from a cosmology simulation, turbulence data from Direct Numerical Simulation, and asteroid impact data from xRage to demonstrate how Foresight can help scientists determine the best data-reduction technique for their simulations.

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