Data parallel processing on processor array architectures has gained popularity in data intensive applications, such as image processing and scientific computing, as massively parallel processor array machines became feasible commercially. 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. We presented data replication technique to speed up the processing of small data structures on large processor arrays. In this paper, we present replicated data algorithms for digital image convolutions and median filtering, and compare their performance with conventional data parallel algorithms for the same on three popular array interconnection networks, namely, the 2-D mesh, the 3-D mesh, and the hypercube.
Dictionary-Based, Clustered Sparse Representation for Hyperspectral Image Classification