SWhybrid: A Hybrid-Parallel Framework for Large-Scale Protein Sequence Database Search

Haidong Lan, Weiguo Liu, Yongchao Liu, Bertil Schmidt

Abstract: Computer architectures continue to develop rapidly towards massively parallel and heterogeneous systems. Thus, easily extensible yet highly efficient parallelization approaches for a variety of platforms are urgently needed. In this paper, we present SWhybrid, a hybrid computing framework for large-scale biological sequence database search on heterogeneous computing environments with multi-core or many-core processing units (PUs) based on the Smith- Waterman (SW) algorithm. To incorporate a diverse set of PUs such as combinations of CPUs, GPUs and Xeon Phis, we abstract them as SIMD vector execution units with different number of lanes. We propose a machine model, associated with a unified programming interface implemented in C++, to abstract underlying architectural differences. Performance evaluation reveals that SWhybrid (i) outperforms all other tested state-of-the-art tools on both homogeneous and heterogeneous computing platforms, (ii) achieves an efficiency of over 80% on all tested CPUs and GPUs and over 70% on Xeon Phis, and (iii) achieves utlization rates of over 80% on all tested heterogeneous platforms. Our results demonstrate that there is enough commonality between vector-like instructions across CPUs and GPUs that one can develop higher-level abstractions and still specialize with close-to-peak performance. SWhybrid is open-source software and freely available at https://github.com/turbo0628/swhybrid.