4 research outputs found
OpenMP aware MHP Analysis for Improved Static Data-Race Detection
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. © 2021 IEEE
OpenMP aware MHP Analysis for Improved Static Data-Race Detection
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. © 2021 IEEE
Creating a Dataset for High-Performance Computing Code Translation: A Bridge Between HPC Fortran and C++
In this study, we present a novel dataset for training machine learning
models translating between OpenMP Fortran and C++ code. To ensure reliability
and applicability, the dataset is initially refined using a meticulous code
similarity test. The effectiveness of our dataset is assessed using both
quantitative (CodeBLEU) and qualitative (human evaluation) methods. We
demonstrate how this dataset can significantly improve the translation
capabilities of large-scale language models, with improvements of
for models with no prior coding knowledge and
for models with some coding familiarity. Our work
highlights the potential of this dataset to advance the field of code
translation for high-performance computing. The dataset is available at
https://github.com/bin123apple/Fortran-CPP-HPC-code-translation-datase