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DTSTAMP:20250626T233531Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241121T100000
DTEND;TZID=America/New_York:20241121T170000
UID:submissions.supercomputing.org_SC24_sess534_post199@linklings.com
SUMMARY:An Error-Bounded Lossy Compression Method with Bit-Adaptive Quanti
 zation for Particle Data
DESCRIPTION:Congrong Ren (The Ohio State University), Sheng Di (Argonne Na
 tional Laboratory (ANL)), Longtao Zhang and Kai Zhao (Florida State Univer
 sity), and Hanqi Guo (The Ohio State University)\n\nWe present error-bound
 ed lossy compression tailored for particle datasets from diverse scientifi
 c applications in cosmology, fluid dynamics, and fusion energy sciences. A
 s today's high-performance computing capabilities advance, these datasets 
 often reach trillions of points, posing significant analysis and storage c
 hallenges. While error-bounded lossy compression makes it possible to repr
 esent floating-point values with strict pointwise accuracy guarantees, the
  lack of correlations in particle data's storage ordering often limits the
  compression ratio. Inspired by quantization-encoding schemes in SZ lossy 
 compressors, we dynamically determine the number of bits to encode particl
 es of the dataset to increase the compression ratio. Specifically, we util
 ize a k-d tree to partition particles into subregions and generate "bit bo
 xes" centered at particles for each subregion to encode their positions. T
 hese bit boxes ensure error control while reducing the bit count used for 
 compression. We evaluate our method against state-of-the-art compressors o
 n cosmology, fluid dynamics, and fusion plasma datasets.\n\nRegistration C
 ategory: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesh
 a Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen Nati
 onal High Performance Computing Center); Sally Ellingson (University of Ke
 ntucky); and Alan Sussman (University of Maryland)\n\n
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