Implementation of GTS

Graph Traverse Scheduling, GTS [ALTB89], was developed as a technique suitable for parallelizing DO loops with cycles on their dependence graph, but actually, GTS is a generalization of many loop restructuring techniques. The improvements achieved with its application in front of other techniques ar...

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Detalles Bibliográficos
Publicado:	1994
Materias:	Memory architecture Scheduling Dependence graphs ITS applications Memory hierarchy Parallel code Re-structuring techniques Restructuring compilers Scalar optimization Shared memory multiprocessor Parallel architectures
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v817LNCS_n_p555_Barrado http://hdl.handle.net/20.500.12110/paper_03029743_v817LNCS_n_p555_Barrado
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_03029743_v817LNCS_n_p555_Barrado
record_format	dspace
spelling	paper:paper_03029743_v817LNCS_n_p555_Barrado2023-06-08T15:28:51Z Implementation of GTS Memory architecture Scheduling Dependence graphs ITS applications Memory hierarchy Parallel code Re-structuring techniques Restructuring compilers Scalar optimization Shared memory multiprocessor Parallel architectures Graph Traverse Scheduling, GTS [ALTB89], was developed as a technique suitable for parallelizing DO loops with cycles on their dependence graph, but actually, GTS is a generalization of many loop restructuring techniques. The improvements achieved with its application in front of other techniques are the reduction of synchronizations, the introduction of scalar optimizations and the better use of the memory hierarchy. The purpose of this work has been the implementation of this new technique for a particular existing shared-memory multiprocessor and its integration on a source-to-source restructuring compiler. We studied the limitations imposed by the architecture to the technique and modified it in order to adapt the generated parallel code to the architecture. Finally, we measured the improvements of the new scheduling comparing the execution time of some example loops obtained by the commercial parallelizer with the execution time obtained by GTS. © Springer-Verlag Berlin Heidelberg 1994. 1994 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v817LNCS_n_p555_Barrado http://hdl.handle.net/20.500.12110/paper_03029743_v817LNCS_n_p555_Barrado
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Memory architecture Scheduling Dependence graphs ITS applications Memory hierarchy Parallel code Re-structuring techniques Restructuring compilers Scalar optimization Shared memory multiprocessor Parallel architectures
spellingShingle	Memory architecture Scheduling Dependence graphs ITS applications Memory hierarchy Parallel code Re-structuring techniques Restructuring compilers Scalar optimization Shared memory multiprocessor Parallel architectures Implementation of GTS
topic_facet	Memory architecture Scheduling Dependence graphs ITS applications Memory hierarchy Parallel code Re-structuring techniques Restructuring compilers Scalar optimization Shared memory multiprocessor Parallel architectures
description	Graph Traverse Scheduling, GTS [ALTB89], was developed as a technique suitable for parallelizing DO loops with cycles on their dependence graph, but actually, GTS is a generalization of many loop restructuring techniques. The improvements achieved with its application in front of other techniques are the reduction of synchronizations, the introduction of scalar optimizations and the better use of the memory hierarchy. The purpose of this work has been the implementation of this new technique for a particular existing shared-memory multiprocessor and its integration on a source-to-source restructuring compiler. We studied the limitations imposed by the architecture to the technique and modified it in order to adapt the generated parallel code to the architecture. Finally, we measured the improvements of the new scheduling comparing the execution time of some example loops obtained by the commercial parallelizer with the execution time obtained by GTS. © Springer-Verlag Berlin Heidelberg 1994.
title	Implementation of GTS
title_short	Implementation of GTS
title_full	Implementation of GTS
title_fullStr	Implementation of GTS
title_full_unstemmed	Implementation of GTS
title_sort	implementation of gts
publishDate	1994
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v817LNCS_n_p555_Barrado http://hdl.handle.net/20.500.12110/paper_03029743_v817LNCS_n_p555_Barrado
_version_	1768542219608784896

Implementation of GTS

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