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DTSTAMP:20250626T234542Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241119T120000
DTEND;TZID=America/New_York:20241119T170000
UID:submissions.supercomputing.org_SC24_sess487_post292@linklings.com
SUMMARY:GPU Compression (for Scientific Data) Done Right
DESCRIPTION:Jiannan Tian (Indiana University), Xin Liang (University of Ke
 ntucky), and Sheng Di and Franck Cappello (Argonne National Laboratory (AN
 L))\n\nError-bounded lossy compression is a critical technique for signifi
 cantly reducing scientific data volumes. Compared to CPU-based compressors
 , GPU-based compressors exhibit substantially higher throughputs, fitting 
 better for today's HPC applications. To overcome the data challenge, GPU-b
 ased scientific lossy compressors have been created. Notably, cuSZ has bee
 n proposed as the error-bounded compression framework and has become the d
 esign base of the subsequent work. A plethora of derived work has been pro
 posed, leading to the discussion of optimality considering data quality, c
 ompression ratio, and data processing speed. This paper covers new researc
 h directions: the compressibility study, the new encoding study, and the a
 pplicability study.\n\nRegistration Category: Tech Program Reg Pass, Exhib
 its Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander Universi
 ty, Erlangen-Nuremberg; Erlangen National High Performance Computing Cente
 r); Sally Ellingson (University of Kentucky); and Alan Sussman (University
  of Maryland)\n\n
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