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DTSTAMP:20260422T000712Z
LOCATION:607
DTSTART;TZID=America/Denver:20231112T172000
DTEND;TZID=America/Denver:20231112T172500
UID:submissions.supercomputing.org_SC23_sess435_ws_pdswwip110@linklings.co
 m
SUMMARY:Compression of Scientific Simulation Data by Stochastic Basis Expa
 nsion – Example on Multiple Computer Systems
DESCRIPTION:Kohei Fujita and Tsuyoshi Ichimura (University of Tokyo, RIKEN
 ); Lalith Maddegedara (University of Tokyo); and Muneo Hori (Japan Agency 
 for Marine-Earth Science and Technology)\n\nTargeting scientific data stor
 age and processing of large-scale complex problems, we consider the introd
 uction of stochastic basis expansions for compressing and restoring data w
 ith low conversion costs by the effective use of high-performance computer
  systems and file systems. Here we target Monte Carlo simulation and use h
 igh-order stochastic basis expansion to reduce data sizes while suppressin
 g the degradation of accuracy. In order to reduce the analysis cost involv
 ed in high-order stochastic expansions, we used a scalable algorithm that 
 can efficiently utilize high-performance computer systems and applied the 
 method to static and dynamic elastic problems. Using the nearly full syste
 m of CPU-based Fugaku (147456 nodes), we show that 1300 trillion degrees-o
 f-freedom storage required in Monte Carlo analysis can be compressed by 35
 -fold to 37 trillion degrees-of-freedom and also show that the same compre
 ssion rate can be attained using 5120 nodes of AMD GPU-based Frontier (54%
  of the system) with high efficiency.\n\nTag: Data Analysis, Visualization
 , and Storage, Data Movement and Memory\n\nRegistration Category: Workshop
  Reg Pass\n\nSession Chairs: Suren Byna (The Ohio State University, Lawren
 ce Berkeley National Laboratory (LBNL)); Jay Lofstead (Sandia National Lab
 oratories, University of New Mexico); Bing Xie (Microsoft Corporation); an
 d Amelie Chi Zhou (Hong Kong Baptist University)\n\n
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