@inproceedings{Bora-llvm-hpc-2021,
  author = {<NAME> <NAME>},
  title = {{OpenMP aware MHP Analysis for Improved Static Data-Race Detection}},
  year = {2021},
  abstract = {Data races, a major source of bugs in concurrent
  programs, can result in loss of manpower and time as well as data
  loss due to system failures. OpenMP, the de facto shared memory
  parallelism framework used in the HPC community, also suffers
  from data races. To detect race conditions in OpenMP programs
  and improve turnaround time and/or developer productivity, we
  present a data flow analysis based, fast, static data race checker
  in the LLVM compiler framework. Our tool can detect races in
  the presence or absence of explicit barriers, with implicit or
  explicit synchronization. In addition, our tool effectively works
  for the OpenMP target offloading constructs and also supports
  the frequently used OpenMP constructs.
  We formalize and provide a data flow analysis framework to
  perform Phase Interval Analysis (PIA) of OpenMP programs.
  Phase intervals are then used to compute the MHP (and its
  complement NHP) sets for the programs, which, in turn, are
  used to detect data races statically.
  We evaluate our work using multiple OpenMP race detection
  benchmarks and real world applications. Our experiments show
  that the checker is comparable to the state-of-the-art in various
  performance metrics with around 90\% accuracy, almost perfect
  recall, and significantly lower runtime and memory footprint.
  },
  booktitle = {Proceedings of the Seventh Annual Workshop on the LLVM Compiler Infrastructure in HPC},
  numpages = {11},
  location = {St. Louis, Missouri, USA},
  series = {LLVM-HPC '21}
}
