Skip to Main content Skip to Navigation
Journal articles

Optimal Fine and Medium Grain Parallelism Detection in Polyhedral Reduced Dependence Graphs

Alain Darte 1 Frédéric Vivien 1 
1 REMAP - Regularity and massive parallel computing
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme
Abstract : This paper presents an optimal algorithm for detecting line or medium grain parallelism in nested loops whose dependences are described by an approximation of distance vectors by polyhedra. In particular, this algorithm is optimal for the classical approximation by direction sectors. This result generalizes, to the case of several statements. Wolf and Lam's algorithm which is optimal for a single statement. Our algorithm relies on a dependence uniformization process and on parallelization techniques related to system of uniform recurrence equations. It can also be viewed as a combination of both Allen and Kennedy's algorithm and Wolf and Lam's algorithm.
Complete list of metadata

https://hal.inria.fr/hal-00856886
Contributor : Equipe Roma Connect in order to contact the contributor
Submitted on : Monday, September 2, 2013 - 4:19:08 PM
Last modification on : Wednesday, March 2, 2022 - 1:28:03 PM

Links full text

Identifiers

Collections

Citation

Alain Darte, Frédéric Vivien. Optimal Fine and Medium Grain Parallelism Detection in Polyhedral Reduced Dependence Graphs. International Journal of Parallel Programming, Springer Verlag, 1997, 25 (6), pp.447--496. ⟨10.1023/A:1025168022993⟩. ⟨hal-00856886⟩

Share

Metrics

Record views

47