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Proceedings Paper

Highly scalable interconnection network for parallel image processing
Author(s): Hongyu Wang; Weikang Gu
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Paper Abstract

In this paper, we introduce a new hierarchical interconnection network for massively parallel systems, named Fully Connected Cubic Network (FCCN). FCCN is able to emulate the popular Hypercube. FCCN has a constant nodal degree of 4 and it therefore eliminates the problem of large fanout in Hypercube. Moreover, the constant degree is an important requirement for efficiently fabricating an architecture in parallel image processing. FCCN is also a highly scalable architecture in that the existing links remain intact when new nodes are introduced. FCCN is maximally fault tolerant, and it enjoys reasonably low diameter, growth of the number of links and average internodal distance. At last, FCCN is used for parallel image processing system for interconnection. The computation results show that FCCN is a high efficient interconnection network for parallel image processing.

Paper Details

Date Published: 20 September 2001
PDF: 5 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441679
Show Author Affiliations
Hongyu Wang, Zhejiang Univ. (China)
Weikang Gu, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing

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