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A safe basis for automatic loop parallelization is the polyhedron model which represents the iteration domain of a loop nest as a polyhedron in $\mathbb{Z}^n$. However, turning the parallel loop program in the model to efficient code meets with several obstacles, due to which performance may deteriorate seriously -- especially on distributed memory architectures. We introduce a fine-grained model of the computation performed and show how this model can be applied to create efficient code.