Optimal fine and medium grain parallelism detection in polyhedral reduced dependence graphs

Author

Darte, Alain ; Vivien, Frédéric

Author_Institution

Lab. LIP, Ecole Normale Superieure de Lyon, France

fYear

1996

fDate

35339

Firstpage

281

Lastpage

291

Abstract

This paper proposes an optimal algorithm for detecting fine or medium grain parallelism in nested loops whose dependences are described by an approximation of distance vectors by polyhedra. In particular it is optimal for direction vectors, which generalizes Wolf and Lam´s algorithm (1991) to the case of several statements. It relies on a dependence uniformization process and an parallelization techniques related to system of uniform recurrence equations

Keywords

computational geometry; parallel algorithms; parallel architectures; direction vectors; distance vectors; nested loops; optimal algorithm; optimal fine grain parallelism detection; optimal medium grain parallelism detection; parallelization techniques; polyhedra; polyhedral reduced dependence graphs; uniform recurrence equations; Approximation algorithms; Difference equations; Linear programming; Linear systems; Scheduling algorithm; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel Architectures and Compilation Techniques, 1996., Proceedings of the 1996 Conference on

Conference_Location

Boston, MA

ISSN

1089-795X

Print_ISBN

0-8186-7633-7

Type

conf

DOI

10.1109/PACT.1996.552676

Filename

552676

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3244735