Abstract
The paper presents a data and task parallel low-level image processing environment for distributed memory systems. Image processing operators are parallelized by data decomposition using algorithmic skeletons. Image processing applications are parallelized by task decomposition, based on the image application task graph. In this way, an image processing application can be parallelized both by data and task decomposition, and thus better speed-ups can be obtained. We validate our method on the multi-baseline stereo vision application.
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