In this thesis, we are interested in adapting algorithms to parallel architectures. Current high performance platforms have several levels of parallelism and require a significant amount of work to make the most of them. Supercomputers possess more and more computational units and are more and more heterogeneous and hierarchical, which make their use very difficult.We take an interest in several aspects which enable to benefit from modern parallel architectures. Throughout this thesis, several problems with different natures are tackled, more theoretically or more practically according to the context and the scale of the considered parallel platforms.We have worked on modeling problems in order to adapt their formulation to existing solvers or resolution methods, in particular in the context of integer factorization problem modeled and solved with integer programming tools.The main contribution of this thesis corresponds to the design of algorithms thought from the beginning to be efficient when running on modern architectures (multi-core processors, Cell, GPU). Two algorithms which solve the compressive sensing problem have been designed in this context: the first one uses linear programming and enables to find an exact solution, whereas the second one uses convex programming and enables to find an approximate solution.We have also used a high-level parallelization library which uses the BSP model in the context of model checking to implement in parallel an existing algorithm. From a unique implementation, this tool enables the use of the algorithm on platforms with different levels of parallelism, while obtaining cutting edge performance for each of them. In our case, the largest-scale platform that we considered is the cluster of multi-core multiprocessors. More, in the context of the very particular Cell processor, an implementation has been written from scratch to take benefit from it.
