Memory architecture in multi-channel optically interconnected distributed shared memory multiprocessor systems

Date
1997
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Abstract

Multi-channel optical networks, although common in telecommunication applications, have only recently found application in computer systems. Multi-channel optical networks offer the potential for high performance interconnects for both local computer networks and multiprocessor systems. In addition to providing high bandwidth, multi-channel optical networks exhibit the capability for efficient broad-cast. The absence of an efficient broadcast mechanism in point-to-point networks has governed the choice of memory subsystem architecture for many parallel computer systems, and has in particular favored cache-coherent non-uniform memory access (CC-NUMA) over cache-only memory access (COMA) architecture. This thesis examined the choice of memory system architecture in the presence of high band-width and efficient broadcast. Using computer simulation, we compared the performance of CC-NUMA and COMA memory architectures in a multi-channel optical network multiprocessing system. Seven well-known parallel benchmarks were used in the study. Our results indicate that COMA consistently and significantly outperforms CC-NUMA in the multi-channel network. We also examined the performance of multi-channel optical networks for a varying-number of optical channels. We found that for the simulated architecture, the sampled benchmarks exhibit significant performance gain using only a small number of channels, relative to the number of nodes in the system. We further found that multiple channels offer better performance than that of a single channel with the same aggregated bandwidth.

Description
Degree
Master of Science
Type
Thesis
Keywords
Electronics, Electrical engineering, Computer science
Citation

Xiao, Yanyang. "Memory architecture in multi-channel optically interconnected distributed shared memory multiprocessor systems." (1997) Master’s Thesis, Rice University. https://hdl.handle.net/1911/17145.

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