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DTSTAMP:20260422T000712Z
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DTSTART;TZID=America/Denver:20231115T103000
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UID:submissions.supercomputing.org_SC23_sess164_pap236@linklings.com
SUMMARY:cuSZp: An Ultra-Fast GPU Error-Bounded Lossy Compression Framework
  with Optimized End-to-End Performance
DESCRIPTION:Yafan Huang (University of Iowa), Sheng Di (Argonne National L
 aboratory (ANL)), Xiaodong Yu (Stevens Institute of Technology), Guanpeng 
 Li (University of Iowa), and Franck Cappello (Argonne National Laboratory 
 (ANL))\n\nModern scientific applications and supercomputing systems are ge
 nerating large amounts of data in various fields, leading to critical chal
 lenges in data storage footprints and communication times. To address this
  issue, error-bounded GPU lossy compression has been widely adopted, since
  it can reduce the volume of data within a customized threshold on data di
 stortion. In this work, we propose an ultra-fast error-bounded GPU lossy c
 ompressor cuSZp. Specifically, cuSZp computes the linear recurrences with 
 hierarchical parallelism to fuse the massive computation into one kernel, 
 drastically improving the end-to-end throughput. In addition, cuSZp adopts
  a block-wise design along with a lightweight fixed-length encoding and bi
 t-shuffle inside each block such that it achieves high compression ratios 
 and data quality. Our experiments on NVIDIA A100 GPU with 6 representative
  scientific datasets demonstrate that cuSZp can achieve an ultra-fast end-
 to-end throughput (95.53x compared with cuSZ) along with a high compressio
 n ratio and high reconstructed data quality.\n\nTag: Accelerators, Data An
 alysis, Visualization, and Storage, Data Compression\n\nRegistration Categ
 ory: Tech Program Reg Pass\n\nReproducibility Badges: Artifact Available, 
 Artifact Functional, Results Reproduced\n\nSession Chair: Kazutomo Yoshii 
 (Argonne National Laboratory (ANL))\n\n
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