NDLI: Intelligent Task Mapping Using Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Tetzlaff, D. Glesner, S.
Copyright Year	2010
Abstract	Task scheduling and task allocation, which are vital parts of mapping parallel programs to concurrent architectures, must take into account the interprocessor communication, whose overheads have emerged as the major performance limitation in parallel applications. Furthermore, its power consumption is an important research focus which must be addressed. Finding an optimal solution requires information about the runtime behavior, which is not known at compile time. Moreover, the computational complexity leads to heuristic approaches based on conservative assumptions that are unable to exploit all of the program's optimization potential. In this paper, we propose a novel approach to automatically generate architecture- and application-specific heuristics for power- and communication-aware task mapping using machine learning techniques to predict how programs behave at runtime. The key advantage of machine learning techniques is their ability to find relevant information in a high-dimensional space. This yields more precise heuristics than those based on pure static assumptions, as our experimental results show. Because learning is done in an off-line training phase once per architecture, the compile time itself is not extended as in other heuristic approaches like genetic or evolutionary algorithms.
Starting Page	1
Ending Page	4
File Size	208553
Page Count	4
File Format	PDF
ISBN	9781424453917
DOI	10.1109/CISE.2010.5677019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Runtime Processor scheduling Heuristic algorithms Machine learning Computer architecture Resource management
Content Type	Text
Resource Type	Article

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