Computational models for image processing for shared-memory multiprocessors

Different tasks in image processing exhibit different computational requirements that should be considered with respect to the architecture. This is particularly critical in parallel machines where many parallelization techniques, 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 parameters.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 proposes 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 for optimizing performance in block processing on crossbar multiprocessors for low-level computer vision applications.