Adaptive exploration for large-scale protein analysis in the molecular dynamics database

Sarana Nutanong, Nick Carey, Yanif Ahmad, Alex S. Szalay, Thomas B. Woolf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Molecular dynamics (MD) simulations generate detailed time-series data of all-atom motions. These simulations are leading users of the world's most powerful supercomputers, and are standard-bearers for a wide range of high- performance computing (HPC) methods. However, MD data exploration and analysis is in its infancy in terms of scalability, ease-of-use, and ultimately its ability to answer 'grand challenge' science questions. This demonstration introduces the Molecular Dynamics Database (MDDB) project at Johns Hopkins, to study the co-design of database methods for deep on-the-fly exploratory MD analyses with HPC simulations. Data exploration in MD suffers from a "human bottleneck", where the laborious administration of simulations leaves little room for domain experts to focus on tackling science questions. MDDB exploits the data-rich nature of MD simulations to provide adaptive control of the exploration process with machine learning techniques, specifically reinforcement learning (RL). We present MDDB's data and queries, architecture, and its use of RL methods. Our audience will co-operate with our steering algorithm and science partners, and witness MDDB's abilities to significantly re- duce exploration times and direct computation resources to where they best address science questions.

Original languageEnglish (US)
Title of host publicationSSDBM 2013 - Proceedings of the 25th International Conference on Scientific and Statistical Database Management
DOIs
StatePublished - Aug 30 2013
Event25th International Conference on Scientific and Statistical Database Management, SSDBM 2013 - Baltimore, MD, United States
Duration: Jul 29 2013Jul 31 2013

Publication series

NameACM International Conference Proceeding Series

Other

Other25th International Conference on Scientific and Statistical Database Management, SSDBM 2013
CountryUnited States
CityBaltimore, MD
Period7/29/137/31/13

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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