Computational models for image processing for shared-memory multiprocessors

Different tasks in image processing exhibit different computational require ments that should be considered with respect to the architecture. This is p articularly critical in parallel machines where many parallelization techni ques, as data partitioning and mapping on processors, use of shared memory space, exploitation of pipelining with pre-fetching affect dramatically the performance with a strong relation with algorithm and architectural parame ters. The paper defines computational models for tightly-coupled multiprocessors with crossbar architecture, both for data-parallel local algorithms and for global algorithms such as spatial transformations. To solve the intrinsic memory limitations of low-cost, highly integrated systems, the paper propos es to extend the classical block processing model by analytically modeling also the case of multiple processing stages. The models have been compared in detail and have been efficiently adopted f or optimizing performance in block processing on crossbar multiprocessors f or low-level computer vision applications.