Abstract. This paper presents a simulation of the casting solidification process performed on graphics processors compatible with nVidia CUDA architecture. Indispensable for the parallel implementation of a computer simulation of the solidification process, it was necessary to modify the numerical model. The new approach shown in this paper allows the process of matrix building to be divided into two independent phases. The first is independent from the nodal temperature values computed in successive time-steps. The second is performed on the basis of nodal temperature values, but does not require a description of the finite element mesh. This phase is performed in each time step of the simulation of the casting solidification process. The separation of these two phases permits an effective implementation of the simulation software of the casting solidification process on the nVidia CUDA architecture or any other multi-/manycore architecture. The use of GPUs nVidia for the implementation of a computer simulation of the solidification process significantly reduced the waiting time for results. In the course of computer simulations important speedup of the computations was observed.Keywords: numerical modeling, solidification, distributed and parallel processing, CUDA General informationProblems faced by engineers and scientists often require carry out the complicated and time-consuming computer simulations, on the basis of which changes can be incorporated into the analyzed object (its geometric model). Such tasks require the most modern solutions in the field of computer science, the most popular solution is parallel processing. Over the last few years the dynamic development of multicore processors has had a direct impact on the availability of advanced high performance solutions for scientists and engineers. Graphic processors (GPUsGraphics Processing Unit) are increasingly being used in high performance computations. A single GPU has a theoretical computational power several times higher than the fastest available CPU. Figure 1 shows the increase in the theoretical computational power of graphic processors and general purpose processors since 2003.