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DTSTART;TZID=America/Denver:20231115T100000
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UID:submissions.supercomputing.org_SC23_sess303_rpost139@linklings.com
SUMMARY:Temporal Classification of Allocations for Reduced Memory Usage
DESCRIPTION:Kristi Belcher and David Beckingsale (Lawrence Livermore Natio
 nal Laboratory (LLNL)), Sam Schwartz (University of Oregon), and Marty McF
 adden (Lawrence Livermore National Laboratory (LLNL))\n\nUmpire, a data an
 d memory management API created at LLNL, provides memory pools which enabl
 e less expensive ways to allocate large amounts of memory in HPC environme
 nts. Memory pools commonly contain both allocations that persist for only 
 a portion of the program (temporary) and those that persist for the entire
  program (permanent). However, too much of a mix of both allocation types 
 can lead to pool fragmentation and cause the pool to perform poorly. Umpir
 e created a tool that uses a machine learning model to perform temporal cl
 assifications and categorize allocations as either temporary or permanent.
  We conducted experiments using trace files from two LLNL applications to 
 study how much memory can be saved when those allocations are separated in
 to distinct pools. We found that our ML tool accurately classifies memory 
 allocations and that separating these allocation types into distinct pools
  reduces overall memory usage significantly (up to 29.5%).\n\nTag: Artific
 ial Intelligence/Machine Learning, Architecture and Networks, Heterogeneou
 s Computing, I/O and File Systems, Performance Measurement, Modeling, and 
 Tools, Post-Moore Computing, Programming Frameworks and System Software, Q
 uantum Computing\n\nRegistration Category: Tech Program Reg Pass, Exhibits
  Reg Pass\n\n
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