A Coarse-Grained Reconfigurable Processor for Sequencing and Phylogenetic Algorithms in Bioinformatics

A coarse-grained reconfigurable processor tailored for accelerating multiple bioinformatics algorithms is proposed. In this paper, a programmable and scalable architectural platform instantiates an array of coarse grained light weight processing elements, which allows arbitrary partitioning, scheduling schemes and capable of solving complete four popular bioinformatics algorithms: the Needleman-Wunsch, Smith-Waterman, and HMMER on sequencing, and Maximum Likelihood on phylogenetic. The key difference of the proposed CGRA based solution compared to FPGA and GPU based solutions is a much better match on architecture and algorithms for the core computational needs, as well as the system level architectural needs. For the same degree of parallelism, we provide a 5X to 14X speed-up improvements compared to FPGA solutions and 15X to 78X compared to GPU acceleration on 3 sequencing algorithms. We also provide 2.8X speed-up compared to FPGA with the same amount of core logic and 70X compared to GPU with the same silicon area for Maximum Likelihood.