  The goal of this work is to parallelize the multistep scheme for the
numerical approximation of the backward stochastic differential equations
(BSDEs) in order to achieve both, a high accuracy and a reduction of the
computation time as well. In the multistep scheme the computations at each grid
point are independent and this fact motivates us to select massively parallel
GPU computing using CUDA. In our investigations we identify performance
bottlenecks and apply appropriate optimization techniques for reducing the
computation time, using a uniform domain. Finally, some examples with financial
applications are provided to demonstrate the achieved acceleration on GPUs.
