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Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10524)

Abstract

Because of the vast volume of data being produced by today’s scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. However, for large-scale cosmology simulation, such as the Hardware/Hybrid Accelerated Cosmology Code (HACC), where memory overhead constraints restrict compression to only one snapshot at a time, the lossy compression ratio is extremely limited because of the fairly low spatial coherence and high irregularity of the data. In this work, we propose a pattern-matching (similarity searching) technique to optimize the prediction accuracy and compression ratio of SZ lossy compressor on the HACC data sets. We evaluate our proposed method with different configurations and compare it with state-of-the-art lossy compressors. Experiments show that our proposed optimization approach can improve the prediction accuracy and reduce the compressed size of quantization codes compared with SZ. We present several lessons useful for future research involving pattern-matching techniques for lossy compression.

Keywords

Lossy Compression Methods Cosmology Simulations Quantization Codes Search Buffer Look-ahead Buffer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of CaliforniaRiversideUSA
  2. 2.Argonne National LaboratoryLemontUSA
  3. 3.University of Illinois at Urbana-ChampaignChampaignUSA

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