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CUDA (Compute Unified Device Architecture) is an efficient architecture developed by NVIDIA to compute parallel algorithms on Graphic Processing Unit (GPU). Using the API associated with this architecture, we develop fast parallel algorithms to compute standard methods for computerized tomography. Computation times are compared to their similar implementations on CPU to illustrate the efficiency of GPU implementation. Some limitations are highlighted and we develop different GPU-computation strategies induced by the size of used/computed data.