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

Philip C. Roth, Kevin Huck, Ganesh Gopalakrishnan, Felix Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProgramming and Performance Visualization Tools - International Workshops, ESPT 2017 and VPA 2017, Revised Selected Papers
EditorsAbhinav Bhatele, David Boehme, Joshua A. Levine, Allen D. Malony, Martin Schulz
PublisherSpringer Verlag
Pages265-272
Number of pages8
ISBN (Print)9783030178710
DOIs
StatePublished - 2019
Event6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th International Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th International Workshop on Visual Performance Analysis, VPA 2018 - Dallas, United States
Duration: Nov 11 2018Nov 16 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11027 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th International Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th International Workshop on Visual Performance Analysis, VPA 2018
Country/TerritoryUnited States
CityDallas
Period11/11/1811/16/18

Funding

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. 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).

Keywords

  • Application characterization
  • Automation
  • Deep learning

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