Vinasc: Scalable Autodock Vina With Fine-Grained Scheduling On Heterogeneous Platform

In this paper we present VinaSC, an improved version of Autodock Vina, that performs molecular docking simulation efficiently on large-scale heterogeneous cluster for massive docking scenario. Both application and platform optimizations are implemented to fully exploit performance potentials of heterogeneous platforms. Specifically, computation is offloaded to Intel Many Integrated Core (MIC) using Intel Coprocessor Offload Infrastructure (COI) to make host CPU and coprocessor collaborate during docking simulation. Moreover, a dynamic scheduling framework is implemented in VinaSC using MPI and Pthread to leverage heterogeneous resources. Our work makes the following improvements: 1) Compared to original Vina that only supports single-node CPU platform, VinaSC fully utilizes computing resources including CPU and MIC coprocessor. 2) Load unbalance due to the random algorithm and heterogeneous platform is alleviated. 3) Utilization of vector units on MIC is significantly improved. 4) VinaSC scales well on heterogeneous cluster, which enables mass docking using clusters. Experiments on a cluster with 6 CPU+MIC nodes using PDBBIND dataset demonstrate that VinaSC outperforms original Vina by more than 2.3x. In addition, VinaSC maintains scalable performance speedup as the docking scale increases.