Analytic Queueing Network Models for Parallel Processing of Task Systems
IEEE Transactions on Computers
Performance and Reliability Analysis Using Directed Acyclic Graphs
IEEE Transactions on Software Engineering
On Performance Prediction of Parallel Computations with Precedent Constraints
IEEE Transactions on Parallel and Distributed Systems
Symbolic Performance Modeling of Parallel Systems
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
Low-Cost Performance Prediction of Data-Dependent Data Parallel Programs
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Predicting the performance of parallel programs
Parallel Computing
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions
IEEE Transactions on Parallel and Distributed Systems
A performance prediction framework for scientific applications
Future Generation Computer Systems
Performance prediction and its use in parallel and distributed computing systems
Future Generation Computer Systems - Systems performance analysis and evaluation
Three ITS path algorithms OpenMP parallel optimization on multi-core systems
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
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A critical problem of predicting the execution time of parallel programs is computing the maximum execution time of tasks involved in the parallel computation. For a parallel composition of n tasks, distribution information of execution time of the constituent task is crucial to accurately predicting mean, variance and even distribution of execution time of the composition of tasks when the execution time of them is stochastic. This paper presents a method of predicting the maximum parallel execution time of n constituent tasks, each of which has independent, identically distributed random execution time. First, execution time of any constituent task as a random variable is transformed into standard normal variable, or approximately so, by using Johnson distributions. Then the distribution and moments of the maximum parallel execution time are obtained after appropriate Johnson transformation is chosen and its corresponding parameters are determined. The experiment on synthetic distributions such as exponential distribution shows that most relative errors of the first four moments are near the 0.1%. And the experiment on some practical applications shows that the relative errors of the first four moments are below 0.1%. The computing complexity of our algorithm, as a function of the number n of the tasks or processors, is O(n), and even O(1) while using approximate distribution, such as Gumbel distribution.