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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Proceedings of the 34th annual ACM/IEEE international symposium on Microarchitecture s. 150 - 159
Hlavní autori:	Aletà, Alex, Codina, Josep M., Sánchez, Jesús, González, Antonio
Médium:	Konferenčný príspevok.. Journal Article
Jazyk:	English
Vydavateľské údaje:	Washington, DC, USA IEEE Computer Society 01.01.2001
Edícia:	ACM Conferences
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!