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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| Published in: | Proceedings of the conference on Design, automation and test in Europe pp. 57 - 62 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
San Jose, CA, USA
EDA Consortium
16.04.2007
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| Series: | ACM Conferences |
| Subjects: |
Computing methodologies
> Artificial intelligence
> Search methodologies
> Heuristic function construction
Theory of computation
> Design and analysis of algorithms
> Approximation algorithms analysis
> Scheduling 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
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| ISBN: | 3981080122, 9783981080124 |
| Online Access: | Get full text |
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| Abstract | 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 formulations suffer prohibitive run times even on medium-sized problems with less than 30 tasks. Our decomposition strategy enhances constraint optimization to robustly handle instances with over 100 tasks. Moreover, the extensibility of constraint formulations permits realistic application and resource constraints, which is a limitation of common heuristic methods for scheduling. The inherent extensibility, coupled with improved run times from a decomposition strategy, posit constraint optimization as a powerful tool for resource constrained scheduling and multiprocessor design space exploration. |
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| AbstractList | 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 formulations suffer prohibitive run times even on medium-sized problems with less than 30 tasks. Our decomposition strategy enhances constraint optimization to robustly handle instances with over 100 tasks. Moreover, the extensibility of constraint formulations permits realistic application and resource constraints, which is a limitation of common heuristic methods for scheduling. The inherent extensibility, coupled with improved run times from a decomposition strategy, posit constraint optimization as a powerful tool for resource constrained scheduling and multiprocessor design space exploration. |
| Author | Satish, Nadathur Keutzer, Kurt Ravindran, Kaushik |
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| ContentType | Conference Proceeding |
| DOI | 10.5555/1266366.1266381 |
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| Snippet | We present a decomposition strategy to speed up constraint optimization for a representative multiprocessor scheduling problem. In the manner of Benders... |
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| SubjectTerms | 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 |
| Title | A decomposition-based constraint optimization approach for statically scheduling task graphs with communication delays to multiprocessors |
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