Most of the exact algorithms for biological sequence comparison obtain the optimal result by calculating dynamic programming (DP) matrices with quadratic time and space complexity. Fickett prunes the DP matrices by only computing values inside a band of size k, thus reducing time and space complexity to [Formula: see text]. Myers and Miller (MM) proposed a linear space algorithm that splits a sequence comparison into multiple comparisons of subsequences, using a divide-and-conquer approach. In this article, we propose a parallel strategy that combines the Fickett and MM algorithms, thus adding pruning capability to the MM algorithm. By using an appropriate Fickett band in each subsequence comparison, we can significantly reduce the number of cells computed in the DP matrices. Our strategy was integrated to stages 3 and 4 of CUDAlign, a state-of-the-art parallel tool for optimal biological sequence comparison, generating two implementations: Fickett-MM-4 and Fickett-MM-3-4. These implementations were used to compare real DNA sequences, reaching a speedup of 101.19 × in the 10 × 10 millions of base pairs comparison when compared with CUDAlign stages 3 and 4. In this case, the execution time was reduced from 71.42 to 0.7 seconds.
Keywords: Fickett band; dynamic programming; sequence alignment.