Abstract
Partitioned Global Address Space (PGAS) programming models, such as OpenSHMEM, are popular methods of parallel programming; however, performance monitoring and analysis tools for these models have remained elusive. In this work, we propose a performance counter extension to the OpenSHMEM interfaces to expose internal communication state as lightweight performance data to tools. We implement our interface in the open source Sandia OpenSHMEM library and demonstrate its mapping to libfabric primitives. Next, we design a simple collector tool to record the behavior of OpenSHMEM processes at execution time. We analyze the Integer Sort (ISx) benchmark and use the resulting data to investigate several common performance issues—including communication schedule, poor overlap, and load imbalance—and visualize the impact of optimizations to correct these issues. Through this study, our tool uncovered a performance bug in this popular benchmark. Finally, by using our tool to guide the application of several pipelining optimizations, we were able to improve the ISx key exchange performance by more than 30%.
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Notes
- 1.
Other names and brands may be claimed as the property of others.
- 2.
Intel and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries.
Benchmark results were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as “Spectre” and “Meltdown”. Implementation of these updates may make these results inapplicable to your device or system.
Software and workloads used in performance tests may have been optimized for performance only on Intel\(^{\textregistered }\) microprocessors. Performance tests, such as SYSmark\(^{\star }\) and MobileMark\(^{\star }\), are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
For more information go to http://www.intel.com/benchmarks.
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Rahman, M.Wu., Ozog, D., Dinan, J. (2019). Lightweight Instrumentation and Analysis Using OpenSHMEM Performance Counters. In: Pophale, S., Imam, N., Aderholdt, F., Gorentla Venkata, M. (eds) OpenSHMEM and Related Technologies. OpenSHMEM in the Era of Extreme Heterogeneity. OpenSHMEM 2018. Lecture Notes in Computer Science(), vol 11283. Springer, Cham. https://doi.org/10.1007/978-3-030-04918-8_12
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