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DTSTART:19700308T020000
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DTSTAMP:20260522T150119Z
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:Fangke Ye (Georgia Institute of Technology); Markus Schordan, 
 Chunhua Liao, Pei-Hung Lin, and Ian Karlin (Lawrence Livermore National La
 boratory); and Vivek Sarkar (Georgia Institute of Technology)\n\nAmong the
  most common and hardest to debug types of bugs in concurrent systems are 
 data races. In this paper, we present an approach for verifying that an Op
 enMP 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 
 the applicability of polyhedral analysis for data race detection in HPC ap
 plications by evaluating our approach with the dedicated data race benchma
 rk suite DataRaceBench and the LLNL Proxy Application AMG2013. Our evaluat
 ion 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 
 that 41 of the 114 (36%) loop nests of AMG2013 are data race free. AMG2013
  consists of 75,000 LOC.\n\nTag: Applications, Correctness, Debugging, Flo
 ating Point, MPI, OpenMP, Runtime Systems, Tools, Verification\n\nRegistra
 tion Category: Workshop Reg Pass\n\nSession Chairs: Ignacio Laguna (Lawren
 ce Livermore National Laboratory (LLNL)) and Cindy Rubio-González (Univers
 ity of California, Davis)\n\n
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