In-Situ Data Reduction for AMR-Based Cosmology Simulations
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
In-Person Only
TP
XO / EX
TimeThursday, 18 November 20218:30am - 5pm CST
LocationSecond Floor Atrium
DescriptionLarge-scale cosmology simulations generate large amounts of data for post-analysis, resulting in I/O and storage bottlenecks. This work investigates an effective in-situ error-bounded lossy compression for Nyx, an adaptive mesh refinement (AMR) based cosmology application. Our contribution is threefold: (1) We explore the best-fit in-situ error-bounded lossy compressor (including SZ and TTHRESH) for Nyx considering both compression ratio and post-analysis quality. (2) We propose an approach to adaptively optimize the compressor for different AMR levels based on our developed metric and data characteristics. (3) Our evaluation shows that our approach can improve the compression ratio by 1.7X over the baseline (i.e., from 66 to 116) with the same post-analysis quality.
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