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UID:submissions.supercomputing.org_SC20_sess171_pap311@linklings.com
SUMMARY:Foresight: Analysis That Matters for Data Reduction
DESCRIPTION:Paper\n\nForesight: Analysis That Matters for Data Reduction\n
 \nGrosset, Biwer, Pulido, Mohan, Biswas...\n\nAs the computational power o
 f supercomputers increases, so does simulation size, which in turn produce
 s orders-of-magnitude more data. Because generated data often exceed the s
 imulation’s disk quota, many simulations would stand to benefit from data-
 reduction techniques to reduce storage requirements. Such techniques inclu
 de autoencoders, data compression algorithms and sampling. Lossy compressi
 on 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 th
 ey can get while keeping their analyses accurate, we have developed Foresi
 ght, an analysis framework that enables users to evaluate how different da
 ta-reduction techniques will impact their analyses. We use particle data f
 rom a cosmology simulation, turbulence data from Direct Numerical Simulati
 on, and asteroid impact data from xRage to demonstrate how Foresight can h
 elp scientists determine the best data-reduction technique for their simul
 ations.\n\nTag: Data Analytics, Compression, and Management, Performance/P
 roductivity Measurement and Evaluation, Resource Management and Scheduling
 \n\nRegistration Category: Tech Program Reg Pass
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