A concurrent adaptive conjugate gradient learning algorithm on MIMD shared-memory machines

H. Adeli, Shih-Lin Hung

研究成果: Article同行評審

54 引文 斯高帕斯(Scopus)

摘要

A concurrent adaptive conjugate gradient learning al gorithm has been developed for training of multilayer feed-forward neural networks and implemented in C on a MIMD shared-memory machine (CRAY Y-MP/8- 864 supercomputer). The learning algorithm has been applied to the domain of image recognition. The per formance of the algorithm has been evaluated by ap plying it to two large-scale training examples with 2,304 training instances. The concurrent adaptive neural networks algorithm has superior convergence property compared with the concurrent momentum back-propagation algorithm. A maximum speedup of about 7.9 is achieved using eight processors for a large network with 4,160 links as a result of microtask ing only. When vectorization is combined with micro tasking, a maximum speedup of about 44 is realized using eight processors.

原文English
頁(從 - 到)155-166
頁數12
期刊International Journal of High Performance Computing Applications
7
發行號2
DOIs
出版狀態Published - 1 1月 1993

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