Sequence alignment has been widely utilized in biological computing science. To obtain the optimal alignment results many algorithms adopts dynamic programming method to achieve this goal. Smith-Waterman algorithm is the famous in the sequence alignment approach. However, such dynamic programming algorithms are computation-consuming. It is impossible to use these algorithms to compare query sequence with a sequence database such as GenBank and PDB. Recently, GPU computing has been applied in many sequence alignment algorithms to enhance the performance. In this paper, we proposed a GPU-based Smith-Waterman algorithm by combining the CPU and GPU computing capabilities to accelerate alignments on a sequence database. In the proposed algorithm, a filtration mechanism using frequency distance is used to decrease the number of compared sequences. We implemented the Smith-Waterman alignments by CUDA on the NVIDIA Tesla C2050. The experimental results show that the highest speedup ratio is about 80 to 90 times over CPU-based Smith-Waterman algorithm.