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Parallel and Distributed Processing Symposium, International (2012)
Shanghai, China China
May 21, 2012 to May 25, 2012
ISSN: 1530-2075
ISBN: 978-1-4673-0975-2
pp: 1376-1387
Deploying an application onto a target platform for high performance oftentimes demands manual tuning by experts. As machine architecture gets increasingly complex, tuning becomes even more challenging and calls for systematic approaches. In our earlier work we presented a prototype that combines efficiently expert knowledge, static analysis, and runtime observation for bottleneck detection, and employs refactoring and compiler feedback for mitigation. In this study, we develop a software tool that facilitates \emph{fast} searching of bottlenecks and effective mitigation of problems from major dimensions of computing (e.g., computation, communication, and I/O). The impact of our approach is demonstrated by the tuning of the LBMHD code and a Poisson solver code, representing traditional scientific codes, and a graph analysis code in UPC, representing emerging programming paradigms. In the experiments, our framework detects with a single run of the application intricate bottlenecks of memory access, I/O, and communication. Moreover, the automated solution implementation yields significant overall performance improvement on the target platforms. The improvement for LBMHD is up to 45\%, and the speedup for the UPC code is up to 5. These results suggest that our approach is a concrete step towards systematic tuning of high performance computing applications.
Measurement, Tuning, Runtime, Probes, Software, Libraries
Hiroki Murata, David Klepacki, I-hsin Chung, Huifang Wen, Guojing Cong, Yasushi Negishi, "An Efficient Framework for Multi-dimensional Tuning of High Performance Computing Applications", Parallel and Distributed Processing Symposium, International, vol. 00, no. , pp. 1376-1387, 2012, doi:10.1109/IPDPS.2012.124
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