Originally designed to relieve the CPU from graphics rendering tasks, the GPU has becomea massively parallel architecture suitable for processing large amounts of data. While it haswon a significant market share in the High Performance Computing domain, an Algorithm-Architecture Matching approach is still necessary to efficiently implement an algorithm onGPU.The contribution of this thesis is twofold. Firstly, we present the significant gain providedby the implementation of a granulometry optimized algorithm (computation time decreasesfrom several hours to less than minute for a volume of 10243 voxels). An analytical modelestablishing the performance variations of the granulometry application is also presented. Webelieve it can be expanded to other regular algorithms.Secondly, the deployment of Signal and Image processing applications on multi-GPUcluster can be a tedious task for the programmer. In order to help him, we developped alibrary that reduces the scope of the programmer’s contribution in the development. Hisremaining tasks are decomposing the application into a Data Flow Graph and giving mappingannotations in order for the tool to automatically dispatch tasks on the processing elements(GPP or GPU). The throughput of a visual sailency streaming application is then improvedthanks to the efficient implementation brought by our tool on a multi-GPU cluster. In orderto permit dynamic load balancing, a task migration method has also been incorporated into it.
