Improving Error-Bounded Lossy Compression for Cosmological N-Body Simulation

Authors: Sihuan Li (University of California, Riverside), Sheng Di (Argonne National Laboratory), Xin Liang (University of California, Riverside), Zizhong Chen (University of California, Riverside), Franck Cappello (Argonne National Laboratory)

Abstract: Cosmological simulations may produce extremely large amount of data, such that its successful run depends on large storage capacity and huge I/O bandwidth, especially in the exascale computing scale. Effective error-bounded lossy compressors with both high compression ratios and low data distortion can significantly reduce the total data size while guaranteeing the data valid for post-analysis. In this poster, we propose a novel, efficient compression model for cosmological N-body simulation framework, by combining the advantages of both space-based compression and time-based compression. The evaluation with a well-known cosmological simulation code shows that our proposed solution can get much higher compression quality than other existing state-of-the-art compressors, with comparable compression/decompression rates.

Best Poster Finalist (BP): no

Poster: pdf
Poster summary: PDF
Reproducibility Description Appendix: PDF


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