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This paper presents the design of an efficient multi-target (CPU+GPU) implementation for the Parallel_for skeleton. Emerging massively parallel architectures promise very high performances for a low cost. However, these architectures change faster than ever. Thus, optimization of codes becomes a very complex and time consumming task. We have identified the data storage as the main difference between the CPU and the GPU implementation of a code. We introduce an abstract data layout in order to adapt the data storage. Based on this layout, the utilization of Parallel_for skeleton allows to compile and execute the same program both on CPU and on GPU. Once compiled, the program runs close to the hardware limits.