Prototyping methodology of image processing applications on heterogeneous parallel systems

Jinglin Zhang
IETR – Institut d’Electronique et de Telecommunications de Rennes
tel-00959330, (14 March 2014)


   title={Prototyping methodology of image processing applications on heterogeneous parallel systems},

   author={Zhang, Jinglin},



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The work presented in this thesis takes place in a context of growing demand for image and video applications on parallel embedded systems. The limitations and lack of flexibility of current design with parallel embedded systems make increasingly complicated to implement applications, particularly on heterogeneous systems. But Open Computing Language (OpenCL) is a new framework for fully employ the capability of computation of general purpose processors or embedded processors. In the meantime, some rapid prototyping tools to design systems are proposed to generate a reliably prototype or automatically implement the image and video applications on embedded systems. The goal of this thesis was to evaluate and to improve design processes for embedded systems, especially based on the dataflow approach (high level of abstraction) and OpenCL approach (intermediate level of abstraction). This challenge is tackled by several projects including the collaborative project COMPA which studies a framework based on the Orcc, Preesm and HMPP tools. In this context, this thesis aims to validate and to evaluate the framework with motion estimation and stereo matching algorithms. For this aim, algorithms have been described using the high-level RVC-CAL language. With the help of Orcc, Preesm, and HMPP tools, we generated and verified C code or OpenCL code or CUDA code for heterogeneous platforms based on multi-core CPU and GPU. We also studied the implementations of these algorithms onto the last generation of many-core for embedded system called MPPA and developed by KALRAY. We proposed three algorithms. One is a parallelized motion estimation method for heterogeneous system based on one CPU and one GPU: we developed one basic method to balance the workload distribution on such heterogeneous system. The second algorithm is a real-time stereo matching method that adopts combined costs and costs aggregation with square size step to implement on laptop’s GPU platform: our experimental results outperform other baseline methods about tradeoff between matching accuracy and time-efficiency. The third algorithm is a joint motion-based video stereo matching method that uses the motion vectors calculated by the first algorithm to build the support region for the second algorithm: our experimental results outperform the stereo video matching methods in the test sequences with abundant movement even in large amounts of noise.
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