Enhancing Parallelism of Pairwise Statistical Significance Estimation for Local Sequence Alignment
Pairwise Statistical Significance (PSS) has been found to be able to accurately identify related sequences (homology detection), which is a fundamental step in numerous applications relating to sequence analysis. Although more accurate than database statistical significance, it is both computationally intensive and data intensive to construct the empirical score distribution during the estimation of PSS, which poses a big challenge in terms of performance and scalability. Multicore computers and clusters have become increasingly ubiquitous and more powerful than before. In this paper, the authors evaluate the use of OpenMP, MPI and hybrid paradigms to accelerate the estimation of PSS of local sequence alignment.