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Foresight: Analysis That Matters for Data Reduction
Event Type
Paper
Tags
Data Analytics, Compression, and Management
Performance/Productivity Measurement and Evaluation
Resource Management and Scheduling
Registration Categories
TP
TimeThursday, 19 November 202010:30am - 11am EST
LocationTrack 4
DescriptionAs 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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