Name
Title | ||
|---|---|---|
Using Deep Learning For Automated Communication Pattern Characterization: Little Steps And Big Challenges |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-17872-7_16 | PROGRAMMING AND PERFORMANCE VISUALIZATION TOOLS |
Keywords | DocType | Volume |
Deep learning, Automation, Application characterization | Conference | 11027 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philip C. Roth | 1 | 2 | 0.77 |
| Kevin A. Huck | 2 | 119 | 14.53 |
| Ganesh Gopalakrishnan | 3 | 14 | 4.79 |
| Felix Wolf | 4 | 57 | 12.00 |