Abstract: As SLP (super-word level parallelism) auto-vectorization algorithm will lose some vectorization opportunities by using heuristic strategy, an auto-vectorization method named DPSLP that based on dynamic programming was proposed in this paper. In this method, the candidate statement groups were searched for SIMD (single instruction multiple data) instruction by using more aggressive strategy than SLP, and the optimal statement groups were selected to vectorize according to the cost of which calculated by dynamic programming formula. Experimental result show that DPSLP achieves on average a total decrease of 8% in execution time, 10% in static instruction cost and increase of 66.4% in vector width, compared with SLP.