Parallelization of reaction dynamics codes using P-GRADE: A case study

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3 Citations (Scopus)

Abstract

P-GRADE, a graphical tool and programming environment was used to parallelize atomic level reaction dynamics codes. In the reported case study a classical trajectory code written in FORTRAN has been parallelized. The selected level was coarse grain parallelization. PGRADE allowed us to use automatic schemes, out of which the task farm was selected. The FORTRAN code was separated into an input/output and a working section. The former, enhanced by a data transfer section operates on the master, the latter on the slaves. Small sections for data transfer were written in C language. The P-GRADE environment offers a user-friendly way of monitoring the efficiency of the parallelization. On a 20-processor NPACI Rocks cluster the speed-up is 99 percent proportional to the number of processors.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAntonio Lagana, Marina L. Gavrilova, Vipin Kumar, Youngsong Mun, C.J. Kenneth Tan, Osvaldo Gervasi
PublisherSpringer Verlag
Pages290-299
Number of pages10
ISBN (Print)3540220569, 9783540220565
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3044
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Bencsura, Á., & Lendvay, G. (2004). Parallelization of reaction dynamics codes using P-GRADE: A case study. In A. Lagana, M. L. Gavrilova, V. Kumar, Y. Mun, C. J. Kenneth Tan, & O. Gervasi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 290-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3044). Springer Verlag. https://doi.org/10.1007/978-3-540-24709-8_31