Performance enhancement of smith-waterman algorithm using hybrid model: Comparing the MPI and hybrid programming paradigm on SMP clusters

Nowadays, database pattern searching is the most heavily used operation in computational biology. Indeed, sequence alignment algorithm plays an important role to find the homologous groups of sequences which may help to determine the function of new sequences. Meanwhile Smith-Waterman algorithm is one of the most prominent pattern matching algorithms. However, it cost the large quantity of time and resource power. By the aid of parallel hardware and software architecture it becomes more feasible to get the fast and accurate result in efficient time. In this paper, Smith-Waterman algorithm is parallelized base on various types of parallel programming, pure MPI, pure OpenMP and hybrid MPI-OpenMP model. In addition, based on the experiments it will be proved that hybrid programming which employ the coarse grain and fine grain parallelization, is more efficient compare with pure MPI and pure OpenMP in cluster of SMP machines.