Research Posters Archive

Temporal Classification of Allocations for Reduced Memory Usage

Authors: Kristi Belcher and David Beckingsale (Lawrence Livermore National Laboratory), Sam Schwartz (University of Oregon), and Marty McFadden (Lawrence Livermore National Laboratory)

Abstract: Umpire, a data and memory management API created at LLNL, provides memory pools which enable less expensive ways to allocate large amounts of memory in HPC environments. 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. Umpire created a tool that uses a machine learning model to perform temporal classifications 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 into 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%).

Best Poster Finalist (BP): no

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