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Performance Analysis of Parallel Processing Systems with Horizontal Decomposition

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3 Author(s)
Hanwei Chen ; Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China ; Jianwei Yin ; Pu, C.

Parallel processing is an important pattern in cluster systems. To analyze the performance of parallel processing systems, we leveraged the fork-join queueing network (FJQN) models. However, there are no easy solutions to these models, especially for the multi-class closed ones. In this paper, a novel and efficient method named horizontal decomposition has been proposed. The main idea of our method is to approximate a non-product-form FJQN with some closed and open product-form networks. So the computational complexity can be dramatically reduced compared with the traditional hierarchical decomposition approach. And the algorithms for solving single-class and multi-class closed FJQNs have been developed respectively based on the horizontal decomposition. With these algorithms, the response time and throughput of each service center in a FJQN can be approximately calculated. The evaluation results show that 90 percentile of relative errors of most service centers are less than 15% except for the shared ones. The evaluation results also showed that the number of iterations in the algorithm for the multi-class FJQNs almost grows linearly with the population of networks.

Published in:

Cluster Computing (CLUSTER), 2012 IEEE International Conference on

Date of Conference:

24-28 Sept. 2012

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