Abstract
Characterization of a parallel application’s communication patterns can be useful for performance analysis, debugging, and system design. However, obtaining and interpreting a characterization can be difficult. AChax implements an approach that uses search and a library of known communication patterns to automatically characterize communication patterns. Our approach has some limitations that reduce its effectiveness for the patterns and pattern combinations used by some real-world applications. By viewing AChax’s pattern recognition problem as an image recognition problem, it may be possible to use deep learning to address these limitations. In this position paper, we present our current ideas regarding the benefits and challenges of integrating deep learning into AChax and our conclusion that a hybrid approach combining deep learning classification, regression, and the existing AChax approach may be the best long-term solution to the problem of parameterizing recognized communication patterns.
This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
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Notes
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The Garbage pattern is an exception: it only provides a generator method because this pattern’s only purpose is to introduce “noise” into synthetic workloads used in unit testing.
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Acknowledgments
We thank David Poliakoff of Lawrence Livermore National Laboratory for his helpful feedback about this paper and the tools workshop presentation that motivated it.
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under contract number DE-AC05-00OR22725.
This work is supported in part by the US Department of Energy Office of Science SciDAC RAPIDS project under subcontract 4000159855 to the University of Oregon from Oak Ridge National Laboratory.
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Roth, P.C., Huck, K., Gopalakrishnan, G., Wolf, F. (2019). Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges. In: Bhatele, A., Boehme, D., Levine, J., Malony, A., Schulz, M. (eds) Programming and Performance Visualization Tools. ESPT ESPT VPA VPA 2017 2018 2017 2018. Lecture Notes in Computer Science(), vol 11027. Springer, Cham. https://doi.org/10.1007/978-3-030-17872-7_16
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