This paper describes a mapping toolbox, whose aim is to optimize the execution time of parallel programs described as task graphs on distributed memory parallel systems. The toolbox includes several classical mapping algorithms. It was assessed by computing the mapping of randomly generated task graphs and by mapping and executing on a parallel system synthetic programs representing some classical numerical algorithms. A large number of experiments were used to validate the cost functions used in the toolbox and to compare the algorithms.