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DTSTART;TZID=America/Denver:20191118T110000
DTEND;TZID=America/Denver:20191118T113000
UID:submissions.supercomputing.org_SC19_sess118_ws_corr108@linklings.com
SUMMARY:Exploring Regression of Data Race Detection Tools Using DataRaceBe
 nch
DESCRIPTION:Workshop\n\nExploring Regression of Data Race Detection Tools 
 Using DataRaceBench\n\nLin, Liao, Schordan, Karlin\n\nDataRaceBench  is  a
 n  OpenMP  benchmark  suite designed  to  systematically  and  quantitativ
 ely  evaluate  data  race detection tools. It has been used by several res
 earch and development groups to measure the quality of their tools. In thi
 s paper we explore how to evaluate the regression of data race detection t
 ools in the presence of observed tool errors. We define major factors rela
 ted  to  generating  consistent,  reproducible,  and  comparable evaluatio
 n results and a detailed evaluation process with a set of configuration an
 d execution rules. We also outline differences in the  evaluation  of  dyn
 amic  and  static  data  race  detection  tools.On top of the evaluation r
 esults, we further explore and suggest different  ways  to  process  and  
 present  the  data,  with  focus  on how to consider tool errors. Using Da
 taRaceBench we can show a regression for several popular data race detecti
 on tools in recent release  cycles.\n\nTag: Workshop Reg Pass, Correctness
 , Debugging, OpenMP, Reliability, Verification\n\nRegistration Category: W
 orkshop Reg Pass, Correctness, Debugging, OpenMP, Reliability, Verificatio
 n
URL:https://sc19.supercomputing.org/presentation/?id=ws_corr108&sess=sess1
 18
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