Graph-partitioning based instruction scheduling for clustered processors

This work presents a novel scheme to schedule loops for clustered microarchitectures. The scheme is based on a preliminary cluster assignment phase implemented through graph partitioning techniques followed by a scheduling phase that integrates register allocation and spill code generation. The grap...

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Bibliographic Details
Published in:	Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture pp. 150 - 159
Main Authors:	Aletà, Alex, Codina, Josep M., Sánchez, Jesús, González, Antonio
Format:	Conference Proceeding Journal Article
Language:	English
Published:	Washington, DC, USA IEEE Computer Society 01.01.2001
Series:	ACM Conferences
Subjects:	Software and its engineering Software and its engineering > Software notations and tools Software and its engineering > Software notations and tools > Compilers 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 > Models of computation Theory of computation > Models of computation > Concurrency Theory of computation > Models of computation > Concurrency > Parallel computing models 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:	0769513697, 9780769513690
ISSN:	1072-4451
Online Access:	Get full text
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