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Empirical study design in the area of High-Performance Computing (2005)
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| Venue: | HPC), in 'Proceedings of the 4th International Symposium on Empirical Software Engineering |
| Citations: | 10 - 4 self |
BibTeX
@INPROCEEDINGS{Shull05empiricalstudy,
author = {Forrest Shull and Jeffrey Carver and Lorin Hochstein and Victor Basili},
title = {Empirical study design in the area of High-Performance Computing},
booktitle = {HPC), in 'Proceedings of the 4th International Symposium on Empirical Software Engineering},
year = {2005}
}
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Abstract
The development of High-Performance Computing (HPC) programs is crucial to progress in many fields of scientific endeavor. We have run initial studies of the productivity of HPC developers and of techniques for improving that productivity, which have not previously been the subject of significant study. Because of key differences between development for HPC and for more conventional software engineering applications, this work has required the tailoring of experimental designs and protocols. A major contribution of our work is to begin to quantify the code development process in a specialized area that has previously not been extensively studied. Specifically, we present an analysis of the domain of High-Performance Computing for the aspects that would impact experimental design; show how those aspects are reflected in experimental design for this specific area; and demonstrate how we are using such experimental designs to build up a body of knowledge specific to the domain. Results to date build confidence in our approach by showing that there are no significant differences across studies comparing subjects with similar experience tackling similar problems, while there are significant differences in performance and effort among the different parallel models applied. Keywords: Developer productivity, High-Performance Computing, parallel programming, empirical study
Keyphrases
high-performance computing experimental design empirical study design significant difference significant study scientific endeavor knowledge specific specialized area many field initial study specific area hpc developer empirical study major contribution similar experience similar problem code development process developer productivity different parallel model parallel programming conventional software engineering application key difference
