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Complexity and Performance in Parallel Programming Languages
Geneva, SWITZERLAND April 01-April 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIPS.1997.5829511997 Workshop on High-Level Programmi ...
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Steven P. VanderWiel, Daphna Nathanson, David J. Lilja, "Complexity and Performance in Parallel Programming Languages," High-Level Programming Models and Supportive Environments, International Workshop on, pp. 3, 1997 Workshop on High-Level Programming Models and Supportive Environments (HIPS '97), 1997.
 
 
BibTex
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@article{ 10.1109/HIPS.1997.582951,
author = {Steven P. VanderWiel and Daphna Nathanson and David J. Lilja},
title = {Complexity and Performance in Parallel Programming Languages},
journal ={High-Level Programming Models and Supportive Environments, International Workshop on},
volume = {0},
year = {1997},
isbn = {0-8186-7882-8},
pages = {3},
doi = {http://doi.ieeecomputersociety.org/10.1109/HIPS.1997.582951},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - High-Level Programming Models and Supportive Environments, International Workshop on
TI - Complexity and Performance in Parallel Programming Languages
SN - 0-8186-7882-8
SP
EP
A1 - Steven P. VanderWiel,
A1 - Daphna Nathanson,
A1 - David J. Lilja,
PY - 1997
KW - parallel programming
KW - software metrics
KW - HPF
KW - MPI
KW - PVM.
VL - 0
JA - High-Level Programming Models and Supportive Environments, International Workshop on
ER -
 
Several parallel programming languages, libraries and environments have been developed to ease the task of writing programs for multiprocessors. Proponents of each approach often point out various language features that are designed to provide the programmer with a simple programming interface. However, virtually no data exists that quantitatively evaluates the relative ease of use of different parallel programming languages. The following paper borrows techniques from the software engineering field to quantify the complexity of three predominate programming models: shared memory, message passing and High-Performance Fortran. It is concluded that traditional software complexity metrics are effective indicators of the relative complexity of parallel programming languages. The impact of complexity on run-time performance is also discussed in the context of message-passing versus HPF on an IBM SP2.
Index Terms:
parallel programming, software metrics, HPF, MPI, PVM.
Citation:
Steven P. VanderWiel, Daphna Nathanson, David J. Lilja, "Complexity and Performance in Parallel Programming Languages," hips, pp.3, 1997 Workshop on High-Level Programming Models and Supportive Environments (HIPS '97), 1997
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