The data partitioning graph: Extending data and control dependencies for data partitioning

Tsuneo Nakanishi, Kazuki Joe, Hideki Saito, Constantine D. Polychronopoulos, Akira Fukuda, Keijiro Araki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Scalability and cost considerations suggest that distributed and distributed shared memory parallel computers will dominate future parallel architectures. These machines could not be used effectively unless efficient automatic and static solutions to the data partitioning and placement problem become available. Significant progress toward this end has been made in the last few years, but we are still far from having general solutions which are efficient for all classes of applications. In this paper we propose the data partitioning graph (DPG) as an intermediate representation for parallelizing compilers, which augments previous intermediate representations, and provides a framework for carrying out partitioning and placement of not only regular data structures (such as arrays), but also of irregular structures and scalar variables. Although recent approaches to task-graph-based intermediate representations focus on representing data and control dependencies between tasks, they largely ignore the use of program variables by the different tasks. Traditional data partitioning methods usually employ algorithm-dependent techniques, and are considered independently of processor assignments (which ought to be handled simultaneously with data partitioning). Moreover, approaches to data partitioning concentrate exclusively on array structures. By explicitly encapsulating the use of program variables by the task nodes, the DPG provides a framework for handling data partitioning as well as processor assignment in the same context. We also discuss the hierarchical data partitioning graph (HDPG) which encapsulates the hierarchy of the compiled programs and is used to map the hierarchy of computations to massively parallel computers with distributed memory system.

Original languageEnglish
Title of host publicationLanguages and Compilers for Parallel Computing - 7th International Workshop, 1994, Proceedings
EditorsKeshav Pingali, Utpal Banerjee, David Gelernter, Alex Nicolau, David Padua
PublisherSpringer Verlag
Pages170-185
Number of pages16
ISBN (Print)354058868X, 9783540588689
DOIs
Publication statusPublished - Jan 1 1995
Event7th International Workshop on Languages and Compilers for Parallel Computing, 1994 - Ithaca, United States
Duration: Aug 8 1994Aug 10 1994

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume892
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Languages and Compilers for Parallel Computing, 1994
CountryUnited States
CityIthaca
Period8/8/948/10/94

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nakanishi, T., Joe, K., Saito, H., Polychronopoulos, C. D., Fukuda, A., & Araki, K. (1995). The data partitioning graph: Extending data and control dependencies for data partitioning. In K. Pingali, U. Banerjee, D. Gelernter, A. Nicolau, & D. Padua (Eds.), Languages and Compilers for Parallel Computing - 7th International Workshop, 1994, Proceedings (pp. 170-185). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 892). Springer Verlag. https://doi.org/10.1007/bfb0025878