Efficient Multiple Multicast on Heterogeneous Network of Workstations

  • Authors:

  • Jan-jan Wu;Shih-hsien Yeh;Pangfeng Liu

  • Affiliations:

  • Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C. wuj@iis.sinica.edu.tw;Institute of Information Science, Academia Sinica, Taipei, Taiwan, R.O.C. shyeh@iis.sinica.edu.tw;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, R.O.C. pangfeng@csie.ntu.edu.tw

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2004

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Abstract

In recent years, network of workstations/PCs (so called NOW) are becoming appealing vehicles for cost-effective parallel computing. Due to the commodity nature of workstations and networking equipment, LAN environments are gradually becoming heterogeneous. The diverse sources of heterogeneity in NOW systems pose a challenge on the design of efficient communication algorithms for this class of systems. In this paper, we propose efficient algorithms for multiple multicast on heterogeneous NOW systems, focusing on heterogeneity in processing speeds of workstations/PCs. Multiple multicast is an important operation in many scientific and industrial applications. Multicast on heterogeneous systems has not been investigated until recently. Our work distinguishes itself from others in two aspects: (1) In contrast to the blocking communication model used in prior works, we model communication in a heterogeneous cluster more accurately by a non-blocking communication model, and design multicast algorithms that can fully take advantage of non-blocking communication. (2) While prior works focus on single multicast problem, we propose efficient algorithms for general, multiple multicast (in which single multicast is a special case) on heterogeneous NOW systems. To our knowledge, our work is the earliest effort that addresses multiple multicast for heterogeneous NOW systems. These algorithms are evaluated using a network simulator for heterogeneous NOW systems. Our experimental results on a system of up to 64 nodes show that some of the algorithms outperform others in many cases. The best algorithm achieves completion time that is within 2.5 times of the lower bound.