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DTSTART:19700308T020000
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DTSTAMP:20181221T160728Z
LOCATION:D171
DTSTART;TZID=America/Chicago:20181112T121000
DTEND;TZID=America/Chicago:20181112T123000
UID:submissions.supercomputing.org_SC18_sess150_ws_corr107@linklings.com
SUMMARY:Using Polyhedral Analysis to Verify OpenMP Applications Are Data R
 ace Free
DESCRIPTION:Workshop\nCorrectness, Debugging, OpenMP, Tools, Verification,
  Workshop Reg Pass\n\nUsing Polyhedral Analysis to Verify OpenMP Applicati
 ons Are Data Race Free\n\nYe, Schordan, Liao, Lin, Karlin...\n\nAmong the 
 most common and hardest to debug types of bugs in concurrent systems are d
 ata races. In this paper, we present an approach for verifying that an Ope
 nMP program is data race free. We use polyhedral analysis to verify those 
 parts of the program where we detect parallel affine loop nests. We show t
 he applicability of polyhedral analysis for data race detection in HPC app
 lications by evaluating our approach with the dedicated data race benchmar
 k suite DataRaceBench and the LLNL Proxy Application AMG2013. Our evaluati
 on shows that polyhedral analysis can classify 40% of the DataRaceBench 1.
 2.0 benchmarks as either data race free or having data races, and verify t
 hat 41 of the 114 (36%) loop nests of AMG2013 are data race free. AMG2013 
 consists of 75,000 LOC.
URL:https://sc18.supercomputing.org/presentation/?id=ws_corr107&sess=sess1
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