Replicated data algorithms in image processing

Abstract

Data parallel processing on processor array architectures has gained considerable popularity in data intensive applications such as image processing and scientific computing. The data parallel paradigm of assigning one processing element to each data element results in an inefficient utilization of a large processor array when a relatively small data structure is processed on it. The large degree of parallelism of a massively parallel processor array machine does not result in a faster solution to a problem involving relatively small data structures than the modest degree of parallelism of a machine that is just as large as the data structure. In this paper, we present an algorithmic technique, called data replication technique, that speeds up the processing of small data structures both analytically and in practice. The technique combines data parallelism and operation parallelism using multiple copies of the data structure. We demonstrate the technique for two image processing operations, namely, image histogram computation and image convolution, and present the results of implementing them on a Connection Machine CM-2. In each case, we also compare the replicated data algorithm with the data parallel algorithm on three common interconnection network architectures to determine the conditions under which a speedup is obtained. Finally, we demonstrate the generality of the data replication technique in image processing by showing how replicated data algorithms can be developed automatically for any operation that can be described as an image to template operation using image algebra.