AutoMOMML: Automatic multi-objective modeling with machine learning

Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild, Laura Carrington, Paul D. Hovland

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

17 Scopus citations

Abstract

In recent years, automatic data-driven modeling with machine learning (ML) has received considerable attention as an alternative to analytical modeling for many modeling tasks. While ad hoc adoption of ML approaches has obtained success, the real potential for automation in data-driven modeling has yet to be achieved. We propose AutoMOMML, an end-to-end, ML-based framework to build predictive models for objectives such as performance, and power. The framework adopts statistical approaches to reduce the modeling complexity and automatically identifies and configures the most suitable learning algorithm to model the required objectives based on hardware and application signatures. The experimental results using hardware counters as application signatures show that the median prediction error of performance, processor power, and DRAM power models are 13 %, 2.3%, and 8%, respectively.

Original languageEnglish
Title of host publicationHigh Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings
EditorsJack Dongarra, Julian M. Kunkel, Pavan Balaji
PublisherSpringer Verlag
Pages219-239
Number of pages21
ISBN (Print)9783319413204
DOIs
StatePublished - 2016
Externally publishedYes
Event31st International Conference on High Performance Computing, ISC High Performance 2016 - Frankfurt, Germany
Duration: Jun 19 2016Jun 23 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9697
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st International Conference on High Performance Computing, ISC High Performance 2016
Country/TerritoryGermany
CityFrankfurt
Period06/19/1606/23/16

Funding

This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research program under contract number DE-AC02-06CH11357.

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