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NETRA: A parallel architecture for integrated vision systems 2: Algorithms and performance evaluationIn part 1 architecture of NETRA is presented. A performance evaluation of NETRA using several common vision algorithms is also presented. Performance of algorithms when they are mapped on one cluster is described. It is shown that SIMD, MIMD, and systolic algorithms can be easily mapped onto processor clusters, and almost linear speedups are possible. For some algorithms, analytical performance results are compared with implementation performance results. It is observed that the analysis is very accurate. Performance analysis of parallel algorithms when mapped across clusters is presented. Mappings across clusters illustrate the importance and use of shared as well as distributed memory in achieving high performance. The parameters for evaluation are derived from the characteristics of the parallel algorithms, and these parameters are used to evaluate the alternative communication strategies in NETRA. Furthermore, the effect of communication interference from other processors in the system on the execution of an algorithm is studied. Using the analysis, performance of many algorithms with different characteristics is presented. It is observed that if communication speeds are matched with the computation speeds, good speedups are possible when algorithms are mapped across clusters.
Document ID
19900012241
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Choudhary, Alok N. (Illinois Univ. Urbana-Champaign, IL, United States)
Patel, Janak H. (Illinois Univ. Urbana-Champaign, IL, United States)
Ahuja, Narendra (Illinois Univ. Urbana-Champaign, IL, United States)
Date Acquired
September 6, 2013
Publication Date
December 1, 1989
Subject Category
Computer Systems
Report/Patent Number
UILU-ENG-89-2242
CSG-118
NASA-CR-186510
NAS 1.26:186510
Accession Number
90N21557
Funding Number(s)
CONTRACT_GRANT: NAG1-613
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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