Performance Evaluation and Modeling Techniques for Parallel Processors
Dimpsey, Robert Tod
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Permalink
https://hdl.handle.net/2142/71979
Description
Title
Performance Evaluation and Modeling Techniques for Parallel Processors
Author(s)
Dimpsey, Robert Tod
Issue Date
1992
Doctoral Committee Chair(s)
Iyer, R.,
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Abstract
This thesis addresses the issue of application performance under real operational conditions. A technique is introduced which accurately models the behavior of an application in real workloads. The methodology can evaluate the performance of the application as well as predict the effects on performance of certain system design changes. The constructed model is based on measurements obtained during normal machine operation and captures various performance issues including multiprogramming and system overheads, and contentions for resources.
Methodologies to measure multiprogramming overhead (MPO) are introduced and illustrated on an Alliant FX/8, an Alliant FX/80, and the Cedar parallel supercomputer. The measurements collected suggest that multiprogramming and system overheads can significantly impact application performance. The mean MPO incurred by PERFECT benchmarks executing in real workloads on an Alliant FX/80 is found to consume 16% of the processing power. For applications executing on Cedar, between 10 and 60% of the application completion time is attributable to overhead caused by multiprogramming. Measurements also identify a Cedar FORTRAN construct (SDOALL) which is susceptible to performance degradation due to multiprogramming.
Using the MPO measurements, the application performance model discussed above is constructed for computationally bound, parallel jobs executing on an Alliant FX/80. It is shown that the model can predict application completion time under real workloads. This is illustrated with several examples from the Perfect Benchmark suite. It is also shown that the model can predict the performance impact of system design changes. For example, the completion times of applications under a new scheduling policy are predicted. The model-building methodology is then validated with a number of empirical experiments.
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