Abstract
We present a new set of tools for the language-centric performance analysis and debugging of OpenMP programs that allows programmers to relate dynamic information from parallel execution to OpenMP constructs. Users can visualize execution traces, examine aggregate metrics on parallel loops and tasks, such as load imbalance or synchronization overhead, and obtain detailed information on specific events, such as the partitioning of a loop’s iteration space, its distribution to workers according to the scheduling policy and fine-grain synchronization. Our work is based on the Aftermath performance analysis tool and a ready-to-use, instrumented version of the LLVM/clang OpenMP run-time with negligible overhead for tracing. By analyzing the performance of the MG application of the NPB suite, we show that language-centric performance analysis in general and our tools in particular can help improve the performance of large-scale OpenMP applications significantly.
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Notes
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As reported by the numactl command line tool of libnuma, invoked with the –hardware option.
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Acknowledgments
Our work was partly supported by the grants EU FET-HPC ExaNoDe H2020-671578, Eurolab-4-HPC H2020-671610, UK EPSRC EP/M004880/1, and France Nano 2017 DEMA. A. Pop is funded by a Royal Academy of Engineering Uni-versity Research Fellowship.
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Drebes, A., Bréjon, JB., Pop, A., Heydemann, K., Cohen, A. (2016). Language-Centric Performance Analysis of OpenMP Programs with Aftermath. In: Maruyama, N., de Supinski, B., Wahib, M. (eds) OpenMP: Memory, Devices, and Tasks. IWOMP 2016. Lecture Notes in Computer Science(), vol 9903. Springer, Cham. https://doi.org/10.1007/978-3-319-45550-1_17
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