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DTSTAMP:20250626T233530Z
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UID:submissions.supercomputing.org_SC24_sess534_post267@linklings.com
SUMMARY:Establishing Best Practices for Applying Inline Compressed Arrays 
 to Improve Performance in HPC
DESCRIPTION:Adam Whitestone (Clemson University)\n\nHPC applications requi
 re massive amounts of memory to process large datasets. While data compres
 sion is used to avoid bottlenecks in transmission and storage, it is still
  necessary to decompress this data into memory to use it. Inline compresse
 d arrays (ICA) is a method which keeps the data compressed in application 
 memory, decompressing blocks of data as needed. The goal is to reduce the 
 memory footprint of big-data applications, allowing them to run on more ab
 undant HPC nodes with less DRAM. This research uses matrix multiplication 
 as a lens for analyzing the effects of various ICA parameters on runtime a
 nd memory usage. We construct a model for a minimum number of compressor c
 alls needed to complete the computation, and show how careful tuning of IC
 A parameters achieves this minimum. Finally, we briefly discuss how our le
 ssons learned impact other computational kernels.\n\nRegistration Category
 : Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal
  (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen National Hi
 gh Performance Computing Center); Sally Ellingson (University of Kentucky)
 ; and Alan Sussman (University of Maryland)\n\n
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