Exploiting Distributed-Memory and Shared-Memory Parallelism on Clusters of SMPs with Data Parallel Programs

Clusters of SMPs are hybrid-parallel architectures that combine the main concepts of distributed-memory and shared-memory parallel machines. Although SMP clusters are widely used in the high performance computing community, there exists no single programming paradigm that allows exploiting the hiera...

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Veröffentlicht in:International journal of parallel programming Jg. 31; H. 1; S. 3 - 19
Hauptverfasser: Benkner, Siegfried, Sipkova, Viera
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer Nature B.V 01.02.2003
Schlagworte:
Arrays
Compilers
Distributed shared memory
Parallel processing
Programming languages
Semantics
Studies
ISSN:0885-7458, 1573-7640
Online-Zugang:Volltext
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Zusammenfassung:Clusters of SMPs are hybrid-parallel architectures that combine the main concepts of distributed-memory and shared-memory parallel machines. Although SMP clusters are widely used in the high performance computing community, there exists no single programming paradigm that allows exploiting the hierarchical structure of these machines. Extensions to the data parallel programming language HPF and associated compilation techniques for optimizing HPF programs on clusters of SMPs are presented. The proposed extensions enable programmers to control key aspects of distributed-memory and shared-memory parallelization at a high-level of abstraction. Based on these language extensions, a compiler can adopt a hybrid parallelization strategy which closely reflects the hierarchical structure of SMP clusters by automatically exploiting shared-memory parallelism based on OpenMP within cluster nodes and distributed-memory parallelism utilizing MPI across nodes.
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ISSN:0885-7458
1573-7640
DOI:10.1023/A:1021782118861