Towards a Portable Drug Discovery Pipeline with SYCL 2020

Abstract

The outcome of the drug discovery process is a molecule that has strong interaction with the target protein. Domain experts expect a beneficial effect from this interaction. The virtual screening is one of the early stages of the process and it aims at finding promising molecules to forward to later stages. We perform this task in-silico to evaluate a very large chemical library in a short time frame. This activity typically comprises two compute-intensive tasks: a docking function that predicts the displacement of atoms, and a scoring function, which estimates the interaction strength [6] Dompé Farmaceutici led the development of LiGen [1, 2, 3], a molecular docking platform targeting High-Performance Computing systems. LiGen has been used for the discovery of novel treatments in the fight against viral infections and multidrug-resistant bacteria [4]. The LiGen processing pipeline includes two main components, ligen-dock and ligen-score, originally developed in OpenACC, refactored to CUDA using non-portable target-specific optimizations [7]. In this talk, we discuss the challenges of making the LiGen docking pipeline portable among different accelerators and GPUs by porting the original codebase from CUDA to SYCL. The code has been refactored by removing critical CUDA semantics with portable ones, and by exploiting several features from the SYCL 2020 standard [5], including sub-groups, group algorithms, and Unified Shared Memory. For comparison, we have developed two versions based on, respectively, accessor and USM-based memory accesses. Particular efforts have been spent on kernel tuning, in particular to optimize those kernels with high register pressure. The final SYCL code base, comprising more than 20 SYCL kernels, has been evaluated on several architectures including NVIDIA V100, NVIDIA A100, AMD MI100 as well as Intel Xeon, and by using both HipSYCL and Intel DPC++ compiler. In terms of performance portability, the SYCL implementation achieves similar performance compared to the CUDA native version on NVIDIA V100 and AMD M100, with minimal modification needed.