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BEGIN:VEVENT
DTSTAMP:20260422T000613Z
LOCATION:405-406-407
DTSTART;TZID=America/Denver:20231115T103000
DTEND;TZID=America/Denver:20231115T120000
UID:submissions.supercomputing.org_SC23_sess164@linklings.com
SUMMARY:Data Compression
DESCRIPTION:AMRIC: A Novel In Situ Lossy Compression Framework for Efficie
 nt I/O in Adaptive Mesh Refinement Applications\n\nAs supercomputers advan
 ce toward exascale capabilities, computational intensity increases signifi
 cantly, and the volume of data requiring storage and transmission experien
 ces exponential growth. Adaptive Mesh Refinement (AMR) has emerged as an e
 ffective solution to address these two challenges. Conc...\n\n\nDaoce Wang
  (Indiana University), Jesus Pulido and Pascal Grosset (Los Alamos Nationa
 l Laboratory (LANL)), Jiannan Tian and Sian Jin (Indiana University), Houj
 un Tang and Jean Sexton (Lawrence Berkeley National Laboratory (LBNL)), Sh
 eng Di (Argonne National Laboratory (ANL)), Kai Zhao (Florida State Univer
 sity), Bo Fang (Pacific Northwest National Laboratory (PNNL)), Zarija Luki
 ć (Lawrence Berkeley National Laboratory (LBNL)), Franck Cappello (Argonne
  National Laboratory (ANL)), James Ahrens (Los Alamos National Laboratory 
 (LANL)), and Dingwen Tao (Indiana University)\n---------------------\ncuSZ
 p: An Ultra-Fast GPU Error-Bounded Lossy Compression Framework with Optimi
 zed End-to-End Performance\n\nModern scientific applications and supercomp
 uting systems are generating large amounts of data in various fields, lead
 ing to critical challenges in data storage footprints and communication ti
 mes. To address this issue, error-bounded GPU lossy compression has been w
 idely adopted, since it can reduce...\n\n\nYafan Huang (University of Iowa
 ), Sheng Di (Argonne National Laboratory (ANL)), Xiaodong Yu (Stevens Inst
 itute of Technology), Guanpeng Li (University of Iowa), and Franck Cappell
 o (Argonne National Laboratory (ANL))\n---------------------\nADT-FSE: A N
 ew Encoder for SZ\n\nSZ is a lossy floating-point data compressor that exc
 els in compression ratio and throughput for high-performance computing (HP
 C), time series databases, and deep learning applications. However, SZ per
 forms poorly for small chunks and has slow decompression. We pinpoint the 
 Huffman tree in the quant...\n\n\nTao Lu (DapuStor Corporation); Yu Zhong,
  Zibin Sun, Xiang Chen, You Zhou, and Fei Wu (Huazhong University of Scien
 ce & Technology); and Ying Yang, Yunxin Huang, and Yafei Yang (DapuStor Co
 rporation)\n\nTag: Accelerators, Data Analysis, Visualization, and Storage
 , Data Compression\n\nRegistration Category: Tech Program Reg Pass\n\nRepr
 oducibility Badges: Artifact Available, Artifact Functional, Results Repro
 duced\n\nSession Chair: Kazutomo Yoshii (Argonne National Laboratory (ANL)
 )
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