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DTSTAMP:20230124T170804Z
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DTSTART;TZID=America/Chicago:20221116T083000
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UID:submissions.supercomputing.org_SC22_sess225_spostg120@linklings.com
SUMMARY:Spline-Interpolation-Based Lossy Compression for Scientific Data
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nSpline-Interpolati
 on-Based Lossy Compression for Scientific Data\n\nTian\n\nError-bounded lo
 ssy compression is a critical technique for significantly reducing scienti
 fic data volumes. With ever-emerging heterogeneous high-performance comput
 ing (HPC) architecture, GPU-accelerated error-bounded compressors such as 
 cuSZ have been developed. In order to improve the data quality and the com
 pression ratio while maintaining high throughput, an interpolation-based s
 pline method is introduced, inspired by the existing CPU prototype. In thi
 s work, We present (1) an efficient GPU implementation of the 3D interpola
 tive spline prediction method, (2) a finer-grained data chunking approach 
 using anchor points to leverage the modern GPU architecture, and (3) an in
 -depth analysis of how such anchor point affects the error formation and t
 he compression ratio, and (4) a preliminary result in performance on the s
 tate-of-the-art modern GPUs. Our solution can achieve 1) a higher compress
 ion ratio than the previous default prediction method in cuSZ, and 2) the 
 overall comparable data quality and compression ratio with the CPU prototy
 pe.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
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