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DTSTART;TZID=America/Denver:20231115T110000
DTEND;TZID=America/Denver:20231115T113000
UID:submissions.supercomputing.org_SC23_sess164_pap179@linklings.com
SUMMARY:AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I
 /O in Adaptive Mesh Refinement Applications
DESCRIPTION:Daoce Wang (Indiana University), Jesus Pulido and Pascal Gross
 et (Los Alamos National Laboratory (LANL)), Jiannan Tian and Sian Jin (Ind
 iana University), Houjun Tang and Jean Sexton (Lawrence Berkeley National 
 Laboratory (LBNL)), Sheng Di (Argonne National Laboratory (ANL)), Kai Zhao
  (Florida State University), Bo Fang (Pacific Northwest National Laborator
 y (PNNL)), Zarija Lukić (Lawrence Berkeley National Laboratory (LBNL)), Fr
 anck Cappello (Argonne National Laboratory (ANL)), James Ahrens (Los Alamo
 s National Laboratory (LANL)), and Dingwen Tao (Indiana University)\n\nAs 
 supercomputers advance toward exascale capabilities, computational intensi
 ty increases significantly, and the volume of data requiring storage and t
 ransmission experiences exponential growth. Adaptive Mesh Refinement (AMR)
  has emerged as an effective solution to address these two challenges. Con
 currently, error-bounded lossy compression is recognized as one of the mos
 t efficient approaches to tackle the latter issue. Despite their respectiv
 e advantages, few attempts have been made to investigate how AMR and error
 -bounded lossy compression can function together. To this end, this study 
 presents a novel in-situ lossy compression framework that employs the HDF5
  filter to improve both I/O costs and boost compression quality for AMR ap
 plications. We implement our solution into the AMReX framework and evaluat
 e on two real-world AMR applications, Nyx and WarpX, on the Summit superco
 mputer. Experiments with 512 cores demonstrate that AMRIC improves the com
 pression ratio by 81x and the I/O performance by 39x over AMReX's original
  compression solution.\n\nTag: Accelerators, Data Analysis, Visualization,
  and Storage, Data Compression\n\nRegistration Category: Tech Program Reg 
 Pass\n\nReproducibility Badges: Artifact Available, Artifact Functional, R
 esults Reproduced\n\nSession Chair: Kazutomo Yoshii (Argonne National Labo
 ratory (ANL))\n\n
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