Design of a Functionally Distributed, Multiprocessor Database Machine Using Data Flow Analysis
IEEE Transactions on Computers
Application Load Imbalance on Parallel Processors
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Symbolic Performance Prediction of Data-Dependent Parallel Programs
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Performance evaluation of non-Markovian stochastic event graphs
PNPM '95 Proceedings of the Sixth International Workshop on Petri Nets and Performance Models
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions
IEEE Transactions on Parallel and Distributed Systems
Experience with Multiprocessor Algorithms
IEEE Transactions on Computers
Performance modeling and analysis of correlated parallel computations
Parallel Computing
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Efficient algorithms for asynchronous multiprocessor systems must achieve a balance between low process communication and high adaptability to variations in process speed. Algorithms that employ problem decomposition may be classified as static (in which decomposition takes place before execution) and dynamic (in which decomposition takes place during execution). Static and dynamic algorithms are particularly suited for low process communication and high adaptability, respectively. For static algorithms the following analysis techniques are presented: finding the probability distribution of execution time, deriving bounds on mean execution time using order statistics, finding asymptotic mean speedup, and using approximations. For dynamic algorithms the technique of modeling using a queueing system is presented. For each technique, an example application to parallel sorting is given.