Allocating Independent Subtasks on Parallel Processors
IEEE Transactions on Software Engineering
Determining average program execution times and their variance
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
A performance evaluation of a general parallel processing model
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Stochastic Bounds on Execution Times of Parallel Programs
IEEE Transactions on Software Engineering
Task Response Time for Real-Time Distributed Systems with Resource Contentions
IEEE Transactions on Software Engineering
A methodology for performance evaluation of parallel applications on multiprocessors
Journal of Parallel and Distributed Computing
The influence of random delays on parallel execution times
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Stochastic performance models of parallel task systems (extended abstract)
SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Probabilistic performance analysis for parallel search techniques
International Journal of Parallel Programming
Realistic parallel performance estimation
Parallel Computing - Special double issue on environment and tools for parallel scientific computing
On Performance Prediction of Parallel Computations with Precedent Constraints
IEEE Transactions on Parallel and Distributed Systems
Predicting Performance of Parallel Computations
IEEE Transactions on Parallel and Distributed Systems
Performance of Synchronous Parallel Algorithms with Regular Structures
IEEE Transactions on Parallel and Distributed Systems
Performance Analysis of Synchronized Iterative Algorithms on Multiprocessor Systems
IEEE Transactions on Parallel and Distributed Systems
Parallel Median Splitting and k-Splitting with Application to Merging and Sorting
IEEE Transactions on Parallel and Distributed Systems
Prediction of Performance and Processor Requirements in Real-Time Data Flow Architectures
IEEE Transactions on Parallel and Distributed Systems
Symbolic Performance Modeling of Parallel Systems
IEEE Transactions on Parallel and Distributed Systems
Multithreaded Parallel Computer Model with Performance Evaluation
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
SCALEA: A Performance Analysis Tool for Distributed and Parallel Programs
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Performance Evaluation of Parallel File Systems for PC Clusters and ASCI Red
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions
IEEE Transactions on Parallel and Distributed Systems
Some Analysis Techniques for Asynchronous Multiprocessor Algorithms
IEEE Transactions on Software Engineering
Performance of Synchronized Iterative Processes in Multiprocessor Systems
IEEE Transactions on Software Engineering
Computer Languages, Systems and Structures
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A performance analysis methodology for correlated parallel computations based on statistical theory is proposed. Divide-and-conquer strategy is widely used in solving problems in parallel by partitioning and allocating a number of given tasks to available computing resources. When the tasks exhibit run-time-dependent behaviors during execution and share a universal distribution function in their execution times, analysis of parallel execution time can be performed with the assistance of probabilistic and statistical models. Correlation (dependence) in execution times among tasks has posed a significant factor in influencing the analysis accuracy which is unmanageable by any known analysis methodologies. We establish a relation between a task's or a processor's execution time and the parallel execution time, in terms of expected value as well as variance when each task's execution time can be closely modeled by a normal distribution, for either uncorrelated or correlated tasks. This relation is then applied to the modeling and analysis of various parallel computation paradigms in which different communication and synchronization patterns along the processing are present. The method proposed has a wider application scope and gives more accurate prediction results than previously known approaches. We also show that, as an extended application of the analysis method to a large scope of problems, load balance among processors can be vastly improved with some novel static task allocation technique in manipulating the correlation among tasks. Experimental results in analyzing a parallel tree search algorithm and two parallel sorting algorithms show very accurate analysis and prediction with the proposed method.