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The configuration of most current academic high-performance computing (HPC) resources tends to enforce ways of working with, and thinking about, molecular dynamics (MD) simulations that are not always optimal. For example, when the aim of the simulation(s) is to produce a representative sample of a Boltzmann weighted ensemble, the ideal scenario would be to be able to do just that-i.e. to tap into a running simulation of indefinite length, collect data from it in real time, and only terminate the simulation once the quality of a sample was assured. Current approaches, based on batch jobs of proscribed maximum length, and a postprocessing style of data analysis, inhibit this. In the spirit of the Internet of Things, we have developed Tios, a Python application that turns MD simulations into remotely discoverable and accessible streaming web applications to which researchers can connect and download data as they please. Tios is freely available, works with standard MD codes, and requires no modifications to them. In this paper we outline how Tios works and present a number of test cases that demonstrate its capabilities.

Tios: The Internet of Simulations. Turning Molecular Dynamics into a Data Streaming Web Application