CPU-GPU Collaborated Computation Models for Biological Sequence Alignment with Mirror-Based Work Load Balancing

Biological sequence alignment has been used in many application areas of computational biology and bioinformatics and both non-heuristic and heuristic algorithms have been developed and used. Although the high accuracy is guaranteed, non-heuristic approaches, such as Smith-Waterman algorithm, are not popularly used in the real world due to the quadratic time complexity. However, recent technological development on HPC systems made researchers propose a diversified acceleration approaches of the Smith-Waterman algorithm, including GPU based approaches. In this paper, we propose efficient CPU-GPU collaborated computation models for protein sequence alignment based on the Smith-Waterman algorithm to exploit the maximum efficiency on the heterogeneous system with multicore processor(s) and GPU(s). The proposed approach implements the full functionalities of the local sequence alignment, which are BLAST compatible, and uses efficient strategies for work load balancing and exploiting the maximum performance on the heterogeneous system. Our experimental results showed that the best CPU-GPU collaborated computation model outperforms the corresponding serial and the basic GPU computation models with 30.5x and 2.78x speedups, respectively, on the system with two Xeon E5-2670 processors and a Tesla M2075 GPU.