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SC Conference - Activity Details

A Scalable Parallel Framework for Analyzing Terascale Molecular Dynamics Trajectories

Tiankai Tu  (D.E. Shaw Research)
Charles A. Rendleman  (D.E. Shaw Research)
David W. Borhani  (D.E. Shaw Research)
Ron O. Dror  (D.E. Shaw Research)
Justin Gullingsrud  (D.E. Shaw Research)
Morten Ø. Jensen  (D.E. Shaw Research)
John L. Klepeis  (D.E. Shaw Research)
Paul Maragakis  (D.E. Shaw Research)
Patrick Miller  (D.E. Shaw Research)
Kate A. Stafford  (D.E. Shaw Research)
David E. Shaw  (D.E. Shaw Research)
Papers Session
Applications: Models and Analysis
Thursday,  04:00PM - 04:30PM
Room Ballroom E
As parallel algorithms and architectures drive the longest molecular dynamics (MD) simulations towards the millisecond scale, traditional sequential post-simulation data analysis methods are becoming increasingly untenable. Inspired by the programming interface of Google's MapReduce, we have built a new parallel analysis framework called HiMach, which allows users to write trajectory analysis programs sequentially, and carries out the parallel execution of the programs automatically. We introduce (1) a new MD trajectory data analysis model that is amenable to parallel processing, (2) a new interface for defining trajectories to be analyzed, (3) a novel method to make use of an existing sequential analysis tool called VMD, and (4) an extension to the original MapReduce model to support multiple rounds of analysis. Performance evaluations on up to 512 processor cores demonstrate the efficiency and scalability of the HiMach framework on a Linux cluster.
The full paper can be found in the IEEE Xplore Digital Library and ACM Digital Library
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