Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms

TL;DR

This paper explores the integration of molecular dynamics simulations with cloud computing and machine learning platforms, highlighting new opportunities, challenges, and potential solutions for scalable scientific computing.

Contribution

It introduces the concept of ML-assisted molecular dynamics simulations on cloud platforms and discusses the associated computational patterns and resource management challenges.

Findings

Cloud resources like preemptible VMs can support molecular dynamics workloads.

ML platforms such as TensorFlow can be integrated with molecular dynamics simulations.

Identifies key challenges and opportunities in cloud-based scientific computing.

Abstract

Scientific computing applications have benefited greatly from high performance computing infrastructure such as supercomputers. However, we are seeing a paradigm shift in the computational structure, design, and requirements of these applications. Increasingly, data-driven and machine learning approaches are being used to support, speed-up, and enhance scientific computing applications, especially molecular dynamics simulations. Concurrently, cloud computing platforms are increasingly appealing for scientific computing, providing "infinite" computing powers, easier programming and deployment models, and access to computing accelerators such as TPUs (Tensor Processing Units). This confluence of machine learning (ML) and cloud computing represents exciting opportunities for cloud and systems researchers. ML-assisted molecular dynamics simulations are a new class of workload, and exhibit unique computational patterns. These simulations present new challenges for low-cost and high-performance execution. We argue that transient cloud resources, such as low-cost preemptible cloud VMs, can be a viable platform for this new workload. Finally, we present some low-hanging fruits and long-term challenges in cloud resource management, and the integration of molecular dynamics simulations into ML platforms (such as TensorFlow).