On Design and Implementation of Adaptive Data Classification Scheme for DSM Systems

  • Chun-Chieh Yang
  • Ssu-Hsuan Lu
  • Hsiao-Hsi Wang
  • Kuan-Ching Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)


Distributed Shared Memory (DSM) environment is built by using specific softwares, to combine a number of computer hardware resources into one computing environment. Such environment not only provides an easy way to execute parallel applications, but also combines resources to speedup execution of these applications. DSM systems need to maintain data consistency in memory, what usually leads to communication overhead. Therefore, there exist a number of strategies that can be used to overcome this overhead and improve overall performance. Prefetching strategies have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. However, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the analysis of data access pattern during execution of parallel applications. We propose an Adaptive Data Classification scheme to improve prefetching strategy, with the goal to improve overall performance. Adaptive Data Classification scheme classifies data according to the access behavior of pages, so that home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, our method can improve the performance of prefetching strategies in DSM systems.


Synchronization Time Parallel Application Remote Node Home Node Page Fault 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abe, T., Okamoto, S.: A Moving Home-based Software DSM System. In: The Proceedings of Communication, Computers and Signal Processing, vol. 1, pp. 17–20 (2003)Google Scholar
  2. 2.
    Eskicioglu, M.R., Marsland, T.A., Hu, W., Shi, W.: Evaluation of the JIAJIA Software DSM System on High Performance Computer Architectures. In: The Proceedings of HICSS-32 The 32nd Annual Hawaii International Conference on System Sciences, vol. Track8 (1999)Google Scholar
  3. 3.
    Hu, W., Shi, W., Tang, Z.: JIAJIA: An SVM System Based on a New Cache Coherence Protocol. In: The Proceedings of HPCN 1999 The High Performance Computing and Networking, pp. 463–472 (1999)Google Scholar
  4. 4.
    Hu, W., Shi, W., Tang, Z.: Write Detection in Home-based Software DSMs. In: The Proceedings of the EuroPar 1999, August 31-September 2 (1999)Google Scholar
  5. 5.
    Hu, W., Shi, W., Tang, Z.: Reducing System Overheads in Home-based Software DSMs. In: The Proceedings of 13th International and 10th Symposium on Parallel and Distributed Processing, pp. 167–173 (1999)Google Scholar
  6. 6.
    Hu, W., Shi, W., Tang, Z.: Home Migration in Home Based Software DSMs. In: The Proceedings of ACM 1st Workshop on Software DSM System (June 1999)Google Scholar
  7. 7.
    Hu, W., Shi, W., Tang, Z.: Optimizing Home-Based Software DSM Protocols. Journal of Networks, Software Tools and applications 4(3), 235–242 (2001)Google Scholar
  8. 8.
    Hu, W., Zhang, F., Liu, H.: Dynamic Data Prefetching in Home-based Software DSM. Journal of Computer Science and Technology (May 2001)Google Scholar
  9. 9.
    Liu, H., Hu, W.: A Comparison of Two Strategies of Dynamic Data Prefetching in Software DSM. In: Parallel and Distributed Processing Symposium, IEEE Proceedings 15th International (2001)Google Scholar
  10. 10.
    Lu, S.H., Yang, C.C., Wang, H.H., Li, K.C.: On Design of Agent Home Scheme for Prefetching Strategy in DSM Systems. In: The Proceedings of AINA 2005 The 19th IEEE International Conference on Advanced Information Networking and Applications, vol. 1, pp. 693–698 (2005)Google Scholar
  11. 11.
    Park, D., Saavedra, R.H.: Adaptive Granularity: Transparent Integration of Fine- and Coarse-Grain Communication. In: The Proceedings of Parallel Architectures and Compilation Techniques, pp. 260–268 (1996)Google Scholar
  12. 12.
    Roh, Y., Seong, B.H., Park, D.: Hiding Latency through Bulk Transfer and Prefetching in Distributed Shared Memory Multiprocessors. In: The Proceedings of High Performance Computing in the Asia-Pacific Region, May 14-17, vol. 1, pp. 164–166 (2000)Google Scholar
  13. 13.
    Shi, W.: Improving the Performance of Software DSM Systems, Chinese Academy of Sciences, Institute of Computing Technology, Dept. of Computer Sciences, Doctor Thesis, Beijing, China (1999)Google Scholar
  14. 14.
    Tu, J.F., Wang, Y.H., Wang, L.H.: A Dynamic Data Prefetching Method of Improving the Memory Latency. In: International Conference on High Performance Computing in the Asia-Pacific Region, vol. 1, pp. 13–18 (2000)Google Scholar
  15. 15.
    Wang, K.J.: On the Design and Implementation of an Effective Prefetch Strategy on DSM Systems, Providence University, Dept. of Computer Science and Information Management, Master Thesis, Taiwan (2004)Google Scholar
  16. 16.
    Wang, K.J., Wang, H.H., Li, K.C.: On Design of a Prefetching Strategy for DSM System. In: PDPTA 2004 International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, USA (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chun-Chieh Yang
    • 1
  • Ssu-Hsuan Lu
    • 1
  • Hsiao-Hsi Wang
    • 1
  • Kuan-Ching Li
    • 2
  1. 1.Parallel and Distributed Processing Center, Dept. of Computer Science and Information ManagementProvidence UniversityShaluTaiwan
  2. 2.Dept. of Computer Science and Information EngineeringProvidence UniversityShaluTaiwan

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