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On Performance Prediction of Parallel Computations with Precedent Constraints
May 2000 (vol. 11 no. 5)
pp. 491-508

Abstract—Performance analysis of concurrent executions in parallel systems has been recognized as a challenging problem. The aim of this research is to study approximate but efficient solution techniques for this problem. We model the structure of a parallel machine and the structure of the jobs executing on such a system. We investigate rich classes of jobs, which can be expressed by series, parallel-and, parallel-or, and probabilistic-fork. We propose an efficient performance prediction method for these classes of jobs running on a parallel environment which is modeled by a standard queueing network model. The proposed prediction method is computationally efficient, it has polynomial complexity in both time and space. The time complexity is $O(C^{2}N^{2}K)$ and the space complexity is $O(C^{2}N^{2}K)$, where $C$ is the number of job classes in the system, the number of tasks in each job class is $O(N)$, and $K$ is the number of service centers in the queueing model. The accuracy of the approximate solution is validated via simulation.

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Index Terms:
Concurrent programs, distributed systems, parallel processing, performance evaluation, queueing networks, task graphs.
De-Ron Liang, Satish K. Tripathi, "On Performance Prediction of Parallel Computations with Precedent Constraints," IEEE Transactions on Parallel and Distributed Systems, vol. 11, no. 5, pp. 491-508, May 2000, doi:10.1109/71.852402
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