A performance model of block structured parallel programs
Proceedings of the international workshop on Parallel algorithms & architectures
Performance and Reliability Analysis Using Directed Acyclic Graphs
IEEE Transactions on Software Engineering
A framework for determining useful parallelism
ICS '88 Proceedings of the 2nd international conference on Supercomputing
Building analytical models into an interactive performance prediction tool
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Solving problems on concurrent processors: vol. 2
Solving problems on concurrent processors: vol. 2
A static performance estimator to guide data partitioning decisions
PPOPP '91 Proceedings of the third ACM SIGPLAN symposium on Principles and practice of parallel programming
Parallel sorting by regular sampling
Journal of Parallel and Distributed Computing
A static parameter based performance prediction tool for parallel programs
ICS '93 Proceedings of the 7th international conference on Supercomputing
On Performance Prediction of Parallel Computations with Precedent Constraints
IEEE Transactions on Parallel and Distributed Systems
Partitioning and Scheduling Parallel Programs for Multiprocessors
Partitioning and Scheduling Parallel Programs for Multiprocessors
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Performance of Synchronous Parallel Algorithms with Regular Structures
IEEE Transactions on Parallel and Distributed Systems
Multivariate statistical techniques for parallel performance prediction
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Multivariate statistical techniques for parallel performance prediction
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Integrated Compilation and Scalability Analysis for Parallel Systems
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions
IEEE Transactions on Parallel and Distributed Systems
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Predicting the execution time of parallel programs involves computing the maximum or minimum of the execution times of the tasks involved in the parallel computation. We present a method to accurately compute the distribution of the largest (Max) and the smallest (Min) execution time of the composite of a number of parallel programming tasks, each having an independent, stochastic, arbitrary workload. The Max function applies to the general case that the composite task completes at the time its longest constituent task terminates. The Min function applies when the completion of its shortest task terminates the whole parallel process, such as in a parallel searching program. Both the Min and Max density function of a constituent task are characterized in terms of a Pearson distribution. Due to its accuracy, the presented method is especially of interest when the performance of time critical parallel applications must be derived. Both prediction methods are tested against three well-known distributions. Furthermore, the Max prediction method is also tested against a number of measured real-life data parallel programs with different degree of parallelism. The results show excellent accuracy of better than 1% with a very few exceptions in extreme situations.