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Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges...

by Philip C Roth, Kevin Huck, Ganesh Gopalakrishnan, Felix Wolf
Publication Type
Conference Paper
Journal Name
International Workshop on Extreme-Scale Programming Tools
Publication Date
Page Numbers
265 to 272
Conference Name
Fifth International Workshop on Visual Performance Analysis (VPA18)
Conference Location
Dallas, Texas, United States of America
Conference Sponsor
IEEE Computer Society, Association for Computing Machinery
Conference Date

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.