Performance Analysis of Parallel Processing Systems with Horizontal Decomposition
Cluster Computing(2012)
摘要
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.
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关键词
parallel processing systems,parallel processing,horizontal decomposition,fork-join queueing network,fjqn,cluster systems,service center,traditional hierarchical decomposition approach,queueing theory,multi-class fjqns,evaluation result,hierarchical decomposition approach,non-product-form fjqn,closed product form networks,fork join queueing network,performance evaluation,parallel processing system,efficient method,performance analysis,cluster system,open product form networks,statistics,sociology,niobium,computational modeling,throughput,approximation algorithms
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