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DTSTAMP:20210402T160544Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201119T083000
DTEND;TZID=America/New_York:20201119T170000
UID:submissions.supercomputing.org_SC20_sess337_rpost137@linklings.com
SUMMARY:MiniVite + Metall: A Case Study of Accelerating Graph Analytics Us
 ing Persistent Memory Allocator
DESCRIPTION:Posters, Research Posters\n\nMiniVite + Metall: A Case Study o
 f Accelerating Graph Analytics Using Persistent Memory Allocator\n\nIwabuc
 hi, Ghosh, Pearce, Halappanavar, Gokhale\n\nA parallel graph generation ha
 s additional computation and communication overheads that often exceed the
  execution time for solving the original problem for which the graph was a
 ctually generated. Substantial performance improvements and cost reduction
 s have occurred in persistent memory technology. The promise of persistent
  memory is that, once constructed, data structures can be re-analyzed and 
 updated beyond the lifetime of a single execution.\n\nminiVite is a distri
 buted-memory graph community detection/clustering mini-application, which 
 supports in-memory random geometric graph generation. We observed signific
 ant overhead at scale in the parallel graph generation part. To optionally
  store the generated graph and provide options to reload it in a successiv
 e run, we employ a persistent memory allocator, Metall. Our experiments de
 monstrate improvements of up to 85x and 65x on NERSC Cori and OLCF Summit 
 supercomputers, respectively (using 128-1024 processes), by trading off th
 e computation/communication attributed to graph generation with I/O.\n\nRe
 gistration Category: Tech Program Reg Pass, Exhibits Reg Pass
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