The importance of synchronization structure in parallel program optimization
ICS '97 Proceedings of the 11th international conference on Supercomputing
Network performance modeling for PVM clusters
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Symbolic Performance Modeling of Parallel Systems
IEEE Transactions on Parallel and Distributed Systems
Performance Prediction of PVM Programs
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions
IEEE Transactions on Parallel and Distributed Systems
Parallel execution time prediction of the multitask parallel programs
Performance Evaluation
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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.