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DTSTAMP:20260522T150117Z
LOCATION:D171
DTSTART;TZID=America/Chicago:20181111T155500
DTEND;TZID=America/Chicago:20181111T162000
UID:submissions.supercomputing.org_SC18_sess152_ws_vpa104@linklings.com
SUMMARY:Using Deep Learning for Automated Communication Pattern Characteri
 zation: Little Steps and Big Challenges
DESCRIPTION:Philip C. Roth (Oak Ridge National Laboratory), Kevin Huck (Un
 iversity of Oregon), Ganesh Gopalakrishnan (University of Utah), and Felix
  Wolf (Technical University Darmstadt)\n\nCharacterization of a parallel a
 pplication's communication patterns can be useful for performance analysis
 , debugging, and system design. However, obtaining and interpreting a char
 acterization can be difficult. AChax implements an approach that uses sear
 ch and a library of known communication patterns to automatically characte
 rize 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 i
 nto AChax and our conclusion that a hybrid approach combining deep learnin
 g classification, regression, and the existing AChax approach may be the b
 est long-term solution to the problem of parameterizing recognized communi
 cation patterns.\n\nTag: Data Analytics, Performance, Visualization\n\nReg
 istration Category: Workshop Reg Pass\n\n
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