The long term behavior of dynamical system is usually analized by means of basins of attraction (BOA) and most often, in particular, with cell mapping methods that ensure a straightforward technique of approximation.Unfortunately the construction of BOA requires large resources, especially for higher-dimensional systems, both in term of computational time and memory space.In this paper, the implementation of cell mapping methods towards a distributed computing is undertaken; a new efficient parallel algorithm for the computation of large-scale basins of attraction is presented herein, also by addressing issues arising from the inner seriality related to the BOA construction. A cell-mapping core is thus wrapped in a management shell, in charge of the core administration, it permits to split over a multi-core environment the computing domain, by carrying out an efficient use of the distributed memory.The proposed approach makes use of a double-step algorithm in order to generate, first, the multidimensional BOA of the system and, then to evaluate arbitrary 2D Poincaré sections of the hypercube that stores the information.An analysis on a test system is performed by considering different dimensional grids; the effort of a parallel implementation towards medium and large clusters is balanced by a great results in terms of computational speed. The performances are strictly affected not only by the number of cores used to run the code, but in particular in the way they are instructed. To get the best from an implementation on a massive parallel architecture, the processes must be properly balanced between memory operations and numerical integrations.