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28th Hawaii International Conference on System Sciences (HICSS'95)
Hawaii, USA
January 04-January 07
ISBN: 0-8186-6935-7
ASCII Text
x 
 
M.J. Clement, M.J. Quinn, "Multivariate statistical techniques for parallel performance prediction," Hawaii International Conference on System Sciences, pp. 446, 28th Hawaii International Conference on System Sciences (HICSS'95), 1995.
 
 
BibTex
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@article{ 10.1109/HICSS.1995.375512,
author = {M.J. Clement and M.J. Quinn},
title = {Multivariate statistical techniques for parallel performance prediction},
journal ={Hawaii International Conference on System Sciences},
volume = {0},
year = {1995},
issn = {1060-3425},
pages = {446},
doi = {http://doi.ieeecomputersociety.org/10.1109/HICSS.1995.375512},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Hawaii International Conference on System Sciences
TI - Multivariate statistical techniques for parallel performance prediction
SN - 1060-3425
SP
EP
A1 - M.J. Clement,
A1 - M.J. Quinn,
PY - 1995
KW - parallel processing; data analysis; statistical analysis; performance evaluation; software performance evaluation; multivariate statistical techniques; parallel performance prediction; multicomputer efficiency; program execution time; architectural characterization; algorithmic characterization; critical model parameters; message latency; cache miss penalty; multivariate data analysis; random variables; variance; performance model; confidence interval; predicted execution time; standard error values; model parameters; large variance values
VL - 0
JA - Hawaii International Conference on System Sciences
ER -
 
M.J. Clement, Dept. of Comput. Sci., Brigham Young Univ., Provo, UT, USA
M.J. Quinn, Dept. of Comput. Sci., Brigham Young Univ., Provo, UT, USA
Performance prediction can play an important role in improving the efficiency of multicomputers in executing scalable parallel applications. An accurate model of program execution time must include detailed algorithmic and architectural characterizations. The exact values for critical model parameters such as message latency and cache miss penalty can often be difficult to determine. This research uses multivariate data analysis to estimate the values of these coefficients in an analytical model. Representing the coefficients as random variables with a specified mean and variance improves the utility of a performance model. Confidence intervals for predicted execution time can be generated using the standard error values for model parameters. Improvements in the model can also be made by investigating the cause of large variance values for a particular architecture.
Index Terms:
parallel processing; data analysis; statistical analysis; performance evaluation; software performance evaluation; multivariate statistical techniques; parallel performance prediction; multicomputer efficiency; program execution time; architectural characterization; algorithmic characterization; critical model parameters; message latency; cache miss penalty; multivariate data analysis; random variables; variance; performance model; confidence interval; predicted execution time; standard error values; model parameters; large variance values
Citation:
M.J. Clement, M.J. Quinn, "Multivariate statistical techniques for parallel performance prediction," hicss, pp.446, 28th Hawaii International Conference on System Sciences (HICSS'95), 1995
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