Workload decomposition strategies for shared memory parallel systems with OpenMP

Authors:
Beniamino Di Martino;Sergio Briguglio;Gregorio Vlad;Giuliana Fogaccia
Affiliations:
Dip. Ingegneria dell'Informazione, Second University of Naples, Italy. E-mail: beniamino.dimartino@unina.it;Associazione Euratom-ENEA sulla Fusione, Frascati, Rome, Italy. E-mail: {briguglio,vlad,fogaccia}@frascati.enea.it;Associazione Euratom-ENEA sulla Fusione, Frascati, Rome, Italy. E-mail: {briguglio,vlad,fogaccia}@frascati.enea.it;Associazione Euratom-ENEA sulla Fusione, Frascati, Rome, Italy. E-mail: {briguglio,vlad,fogaccia}@frascati.enea.it
Venue:
Scientific Programming
Year:
2001

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Abstract

A crucial issue in parallel programming (both for distributed and shared memory architectures) is work decomposition. Work decomposition task can be accomplished without large programming effort with use of high-level parallel programming languages, such as OpenMP. Anyway particular care must still be payed on achieving performance goals. In this paper we introduce and compare two decomposition strategies, in the framework of shared memory systems, as applied to a case study particle in cell application. A number of different implementations of them, based on the OpenMP language, are discussed with regard to time efficiency, memory occupancy, and program restructuring effort.