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DTSTART;TZID=America/Denver:20231114T100000
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UID:submissions.supercomputing.org_SC23_sess290_drs111@linklings.com
SUMMARY:Scaling HPC Applications through Predictable and Reliable Data Red
 uction Methods
DESCRIPTION:Doctoral Showcase, Posters\n\nSian Jin (Indiana University, Ar
 gonne National Laboratory (ANL))\n\nFor scientists and engineers, large-sc
 ale computer systems are one of the most powerful tools to solve complex h
 igh-performance computing (HPC) and Deep Learning (DL) problems. With the 
 ever-increasing computing power such as the new generation of exascale (on
 e exaflop or a billion billion calculations per second) supercomputers, th
 e gap between computing power and limited storage capacity and I/O bandwid
 th has become a major challenge for scientists and engineers. Large-scale 
 scientific simulations on parallel computers can generate extremely large 
 amounts of data that are highly compute and storage intensive. This study 
 will introduce data reduction techniques as a promising solution to signif
 icantly reduce the data sizes while maintaining high data fidelity for pos
 t-analyses in HPC applications. This study can be categorized into mainly 
 four scenarios: (1) A ratio-quality model that makes lossy compression pre
 dictable; (2) advanced parallel write solution with async-I/O; (3) in-situ
  data reduction for scientific applications; and (4) in-situ data reductio
 n for large-scale machine learning.\n\nTag: Data Compression, I/O and File
  Systems\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pas
 s
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