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DTSTAMP:20250626T234541Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241119T120000
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UID:submissions.supercomputing.org_SC24_sess487_post241@linklings.com
SUMMARY:Performance of Inline Compression with Software Caching for Reduci
 ng the Memory Footprint in pySDC
DESCRIPTION:Emily Lattanzio (Clemson University)\n\nThe volume of data req
 uired for high performance computing (HPC) jobs is growing faster than the
  memory storage available to store the required data, leading to performan
 ce bottlenecks. Hence the need for inline data compression, which reduces 
 the amount of allocated memory needed by storing all data in its compresse
 d format and decompressing/recompressing single variables as needed. We ap
 ply inline compression to HPC application pySDC, a framework that solves c
 ollocation problems iteratively using parallel-in-time methods. We introdu
 ce a new version of pySDC that has a compression manager to add inline com
 pression functionality, along with a software cache that stores the decomp
 ressed state of the most frequently used variables. We use lossy compresso
 r ZFP and test our model with varying software cache sizes. Results show t
 hat having no cache has the best compression ratio, but having a cache of 
 size 16 improves the timing while also slightly improving the memory footp
 rint.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n
 \nSession Chairs: Ayesha Afzal (Friedrich-Alexander University, Erlangen-N
 uremberg; Erlangen National High Performance Computing Center); Sally Elli
 ngson (University of Kentucky); and Alan Sussman (University of Maryland)\
 n\n
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