Design and implementation of an agent home scheme strategy for prefetch-based DSM systems

  • Authors:

  • Hsiao-Hsi Wang;Kuan-Ching Li;Ssu-Hsuan Lu;Chun-Chieh Yang;Jean-Luc Gaudiot

  • Affiliations:

  • Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan, R.O.C.;Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, R.O.C.;Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, R.O.C.;Department of Electrical Engineering and Computer Science, University of California-Irvine, Irvine, CA

  • Venue:
  • International Journal of Parallel Programming
  • Year:
  • 2008

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Abstract

In recent years, cluster computing has been widely investigated and there is no doubt that it can provide a cost-effective computing infrastructure by aggregating computational power, communication, and storage resources. Moreover, it is also considered to be a very attractive platform for low-cost supercomputing. Distributed shared memory (DSM) systems utilize the physical memory of each computing node interconnected in a private network to form a global virtual shared memory. Since this global shared memory is distributed among the computing nodes, accessing the data located in remote computing nodes is an absolute necessity. However, this action will result in significant remote memory access latencies which are major sources of overhead in DSM systems. For these reasons, in order to increase overall system performance and decrease this overhead, a number of strategies have been devised. Prefetching is one such approach which can reduce latencies, although it always increases the workload in the home nodes. In this paper, we propose a scheme named Agent Home Scheme. Its most noticeable feature, when compared to other schemes, is that the agent home distributes the workloads of each computing nodes when sending data. By doing this, we can reduce not only the workload of the home nodes by balancing the workload for each node, but also the waiting time. Experimental results show that the proposed method can obtain about 20% higher performance than the original JIAJIA, about 18% more than History Prefetching Strategy (HPS), and about 10% higher than Effective Prefetch Strategy (EPS).