Parallel Longest Common Sequence Algorithm on Multicore Systems Using OpenACC, OpenMP and OpenMPI

The longest common subsequence (LCS) problem is one of the most useful algorithms being applied in various research areas. This problem is known to be NP-hard for arbitrary data. In this paper, we present a parallel LCS algorithm using the GPU-based OpenACC model, which is based on the existing dynamic approach and parallel anti-diagonal scheme that is applied in order to eliminate the data dependencies. The proposed algorithm in this paper has been benchmarked using four different computing models: OpenMPI, OpenMP, hybrid OpenMPI & OpenMP, and OpenACC model. The parallel LCS algorithm has been implemented using Swiss-Prot databases over these computing models, so that their execution times, speed-ups and speed-ratios have been measured and analogized among them extensively. Our experimental results reveal that the computation of our algorithm on OpenACC (on GPU) is around 16 times faster than the execution on a single CPU, and around 2 times faster than on the octa-core processor systems. The performance of the OpenACC model stands out among the four tested models in solving the LCS problem.