Abstract
Making use of many workstations connected by a network can give better performance than the same number of discrete workstations. We investigate various partitioning strategies for parallel digital image convolution in such a network. CORBA (Common Object Request Broker Architecture) is employed in implementing parallel processing with distributed workstations, allowing heterogeneous workstations to be used for parallel processing. We present a parallel and distributed image convolution processing model. We also describe several heterogeneous partitioning strategies and discuss the performance of each based on experimental results obtained by real implementation.
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