In order to reduce the large execution time of an existing fingerprint enhancement system, a parallel implementation method based on GPU via CUDA is proposed. Firstly, the necessity and feasibility of employing parallel programming for the whole system are analyzed. Then pre-processing, global analysis, local analysis and matched filtering of the whole fingerprint enhancement system is designed, optimized and implemented respectively using parallel computing technology via CUDA. Finally, numerous fingerprints from FVC2000 databases are tested and the  obtained execution time is compared with that of the CPU based system. The results show that the execution time is significantly reduced by using the parallel implementation method based on GPU.