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Communication and computation patterns of large scale image convolutions on parallel architectures

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4 Author(s)
Dykes, S.G. ; High Performance Comput. & Software Lab., Texas Univ., San Antonio, TX, USA ; Xiaodong Zhang ; Yan Zhou ; Haixu Yang

Segmentation and other image processing operations rely on convolution calculations with heavy computational and memory access demands. The article presents an analysis of a texture segmentation application containing a 96×96 convolution. Sequential execution required several hours an single processor systems with over 99% of the time spent performing the large convolution. 70% to 75% of execution time is attributable to cache misses within the convolution. We implemented the same application on CM-5, iPSC/860 and PVM distributed memory multicomputers, tailoring the parallel algorithms to each machine's architecture. Parallelization significantly reduced execution time, taking 49 seconds on a 512 node CM-5 and 6.5 minutes on a 32 node iPSC/860. The results indicate for large kernel convolutions the size and bandwidth of the fast memory store is more important than processor power or communication overhead

Published in:

Parallel Processing Symposium, 1994. Proceedings., Eighth International

Date of Conference:

26-29 Apr 1994