A GPU-accelerated Molecular Docking Workflow with Kubernetes and Apache Airflow

TL;DR

This paper presents a GPU-accelerated molecular docking workflow adapted for cloud-native deployment using Kubernetes and Apache Airflow, enabling efficient resource utilization and faster drug discovery processes.

Contribution

It introduces a novel DAG-based implementation of a GPU-accelerated molecular docking workflow for cloud environments, bridging HPC and cloud resource integration.

Findings

Workflow effectively overlaps different computational stages.

Successful deployment on heterogeneous cloud resources.

Demonstrated efficiency with SWEETLEAD dataset.

Abstract

Complex workflows play a critical role in accelerating scientific discovery. In many scientific domains, efficient workflow management can lead to faster scientific output and broader user groups. Workflows that can leverage resources across the boundary between cloud and HPC are a strong driver for the convergence of HPC and cloud. This study investigates the transition and deployment of a GPU-accelerated molecular docking workflow that was designed for HPC systems onto a cloud-native environment with Kubernetes and Apache Airflow. The case study focuses on state-of-of-the-art molecular docking software for drug discovery. We provide a DAG-based implementation in Apache Airflow and technical details for GPU-accelerated deployment. We evaluated the workflow using the SWEETLEAD bioinformatics dataset and executed it in a Cloud environment with heterogeneous computing resources. Our workflow can effectively overlap different stages when mapped onto different computing resources.