This paper presents the application of Graphics Processing Unit (GPU) to improve the control performance of sampling based predictive control algorithms. As an example problem, obstacle avoidance situation with parked cars in a street is modeled as a non-linear model predictive control problem. Car dynamics and non-linear constraints are considered to achieve collision avoidance. The control input must be optimized in every control step in real-time considering the non-linear constraints. Sampling based approach is used to solve this problem and one of the major limitations to this approach is the computational cost involved. In this paper, the sampling-based optimization algorithm was adapted to utilize the parallel computing capabilities of GPU using CUDA. The generated input sequence and the computational speeds were compared with a CPU based program for the same case. The proposed method is implemented in a simulation experiment with car dynamics simulator to verify its performance in terms of path tracking. Finally, a general relationship between sample size and GPU acceleration of its calculation speed is also discussed.