Workload decomposition strategies for hierarchical distributed‐shared memory parallel systems and their implementation with integration of high‐level parallel languages | Zendy

Briguglio Sergio | Zendy; Di Martino Beniamino | Zendy; Vlad Gregorio | Zendy

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Workload decomposition strategies for hierarchical distributed‐shared memory parallel systems and their implementation with integration of high‐level parallel languages

Author(s) -

Briguglio Sergio,

Di Martino Beniamino,

Vlad Gregorio

Publication year - 2002

Publication title -

concurrency and computation: practice and experience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.309

H-Index - 67

eISSN - 1532-0634

pISSN - 1532-0626

DOI - 10.1002/cpe.702

Subject(s) - computer science , workload , parallel computing , decomposition , porting , distributed memory , fortran , node (physics) , shared memory , parallel programming model , high level programming language , programming paradigm , distributed computing , programming language , operating system , ecology , structural engineering , software , biology , engineering

In this paper we address the issue of workload decomposition in programming hierarchical distributed‐shared memory parallel systems. The workload decomposition we have devised consists of a two‐stage procedure: a higher‐level decomposition among the computational nodes; and a lower‐level one among the processors of each computational node. By focusing on porting of a case study particle‐in‐cell application, we have implemented the described work decomposition without large programming effort by using and integrating the high‐level language extensions High‐Performance Fortran and OpenMP. Copyright © 2002 John Wiley & Sons, Ltd.

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