A decomposition-based constraint optimization approach for statically scheduling task graphs with communication delays to multiprocessors

We present a decomposition strategy to speed up constraint optimization for a representative multiprocessor scheduling problem. In the manner of Benders decomposition, our technique solves relaxed versions of the problem and iteratively learns constraints to prune the solution space. Typical formula...

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Bibliographic Details
Published in:	Proceedings of the conference on Design, automation and test in Europe pp. 57 - 62
Main Authors:	Satish, Nadathur, Ravindran, Kaushik, Keutzer, Kurt
Format:	Conference Proceeding
Language:	English
Published:	San Jose, CA, USA EDA Consortium 16.04.2007
Series:	ACM Conferences
Subjects:	Computing methodologies Computing methodologies > Artificial intelligence Computing methodologies > Artificial intelligence > Search methodologies Computing methodologies > Artificial intelligence > Search methodologies > Heuristic function construction Theory of computation Theory of computation > Design and analysis of algorithms Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms Theory of computation > Design and analysis of algorithms > Online algorithms Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms Theory of computation > Theory and algorithms for application domains Theory of computation > Theory and algorithms for application domains > Machine learning theory Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
ISBN:	3981080122, 9783981080124
Online Access:	Get full text
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