Parallelization of Virtual Screening in Drug Discovery on Massively Parallel Architectures

Abstract

The current trend in medical research for the discovery of new drugs is the use of Virtual Screening (VS) methods. In these methods, the calculation of the non-bonded interactions, such as electrostatics or van der Waals forces, plays an important role, representing up to 80% of the total execution time. These kernels are computational intensive and massively parallel in nature, and thus they are well suited to be accelerated on parallel architectures. In this work, we discuss the effective parallelization of the non-bonded electrostatic interactions kernel for VS on three different parallel architectures: a shared memory system, a distributed memory system, and a Graphics Processing Units (GPUs). For an efficient handling of the computational intensive and massively parallelism of this kernel, we enable different data policies on those architectures to take advantage of all computational resources offered by them. Four implementations are provided based on MPI, OpenMP, Hybrid MPI Open MP and CUDA programming models. The sequential implementation is defeated by a wide margin by all parallel implementations, obtaining up to 72x speed-up factor on the shared memory system through OpenMP, up to 60x and229x speed-ups factors on the distributed memory system for the MPI implementation and the Hybrid MPI-Open MP implementation respectively, and finally, up to 213x speedup factor for the CUDA implementation on the GPU architecture to offer the best alternative in terms of performance/cost ratio.