New approach for task clustering

Weiping Zhu, Tyng Yeu Liang, Ce Kuen Shieh

研究成果: Paper

摘要

Static clustering has been used to group tasks for parallel processing. Most clustering methods used for current multithreaded DSM systems only consider the workload balance. In contrast, in this paper we present a static method to cluster closely related tasks of an application onto a multithreaded DSM system. This method relies on the Hopfield neural network to find optimal or near-optimal clusters. An optimal solution identified by this method tends to minimize load imbalance and communication overhead. We have implemented this method on Cohesion which is a multithreaded DSM system. Three programs, SOR, Nbody, and Gaussian Elimination, are used to test the effectiveness of this method. The result shows that our method indeed can find optimal or near-optimal clustering for these programs.

原文English
頁面538-542
頁數5
出版狀態Published - 1998 一月 1
事件Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2) - Beijing, China
持續時間: 1997 十月 281997 十月 31

Other

OtherProceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2)
城市Beijing, China
期間97-10-2897-10-31

    指紋

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

引用此

Zhu, W., Liang, T. Y., & Shieh, C. K. (1998). New approach for task clustering. 538-542. 論文發表於 Proceedings of the 1997 IEEE International Conference on Intelligent Processing Systems, ICIPS'97. Part 1 (of 2), Beijing, China, .