# Extensible and Scalable Adaptive Sampling on Supercomputers

## TL;DR

This paper presents the ExTASY framework that enables efficient, scalable adaptive sampling of protein dynamics on supercomputers, significantly improving sampling efficiency over traditional methods.

## Contribution

It introduces a framework that simplifies the deployment of adaptive sampling strategies on HPC systems, facilitating large-scale protein dynamics simulations.

## Key findings

- Adaptive sampling achieves over tenfold speedup compared to brute force MD.
- ExTASY reliably executes adaptive workflows at scale on HPC platforms.
- Folding dynamics of four proteins predicted without prior information.

## Abstract

The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of High-Performance Computers (HPC) systems. Utilizing only "brute force" MD simulations requires an unacceptably long time to solution. Adaptive sampling methods allow a more effective sampling of protein dynamics than standard MD simulations. Depending on the restarting strategy the speed up can be more than one order of magnitude. One challenge limiting the utilization of adaptive sampling by domain experts is the relatively high complexity of efficiently running adaptive sampling on HPC systems. We discuss how the ExTASY framework can set up new adaptive sampling strategies, and reliably execute resulting workflows at scale on HPC platforms. Here the folding dynamics of four proteins are predicted with no a priori information.