An Hybrid Approach to accelerate a Molecular Docking Application for Virtual Screening in Heterogeneous Nodes

Molecular Docking is a crucial task in the process of Drug Discovery. This task consists in the estimation of the position of a molecule inside the docking site. It is used in the early stages of the drug discovery process to perform a virtual screening of a large library of molecule candidates. This task is usually performed using High Performance Computing platforms, due to sheer number of candidates and due to complexity of the docking problem. In this work we ported and optimized a Molecular Docking Module to an heterogeneous system with one or more GPGPU accelerators, leveraging the directive languages OpenMP and OpenACC. We show that with the proposed approach, we are able to reach a better utilization of the available resources compared to the usual CPU/GPU data splitting, reaching a 25% throughput improvement within the single node.