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DTSTAMP:20260422T000602Z
LOCATION:E Concourse
DTSTART;TZID=America/Denver:20231114T100000
DTEND;TZID=America/Denver:20231114T170000
UID:submissions.supercomputing.org_SC23_sess290_drs111@linklings.com
SUMMARY:Scaling HPC Applications through Predictable and Reliable Data Red
 uction Methods
DESCRIPTION:Sian Jin (Indiana University, Argonne National Laboratory (ANL
 ))\n\nFor scientists and engineers, large-scale computer systems are one o
 f the most powerful tools to solve complex high-performance computing (HPC
 ) and Deep Learning (DL) problems. With the ever-increasing computing powe
 r such as the new generation of exascale (one exaflop or a billion billion
  calculations per second) supercomputers, the gap between computing power 
 and limited storage capacity and I/O bandwidth has become a major challeng
 e for scientists and engineers. Large-scale scientific simulations on para
 llel computers can generate extremely large amounts of data that are highl
 y compute and storage intensive. This study will introduce data reduction 
 techniques as a promising solution to significantly reduce the data sizes 
 while maintaining high data fidelity for post-analyses in HPC applications
 . This study can be categorized into mainly four scenarios: (1) A ratio-qu
 ality model that makes lossy compression predictable; (2) advanced paralle
 l write solution with async-I/O; (3) in-situ data reduction for scientific
  applications; and (4) in-situ data reduction for large-scale machine lear
 ning.\n\nTag: Accelerators, Artificial Intelligence/Machine Learning, Appl
 ications, Cloud Computing, Distributed Computing, Data Analysis, Visualiza
 tion, and Storage, Data Compression, Heterogeneous Computing, I/O and File
  Systems, Quantum Computing, Reproducibility, Security, Software Engineeri
 ng\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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