Proceedings ArticleDOI
Copernicus: a new paradigm for parallel adaptive molecular dynamics
Sander Pronk,Gregory R. Bowman,Berk Hess,Per Larsson,Imran S. Haque,Vijay S. Pande,Iman Pouya,Kyle A. Beauchamp,Peter M. Kasson,Erik Lindahl +9 more
- pp 60
TLDR
A new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus, which combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring.Abstract:
Biomolecular simulation is a core application on supercomputers, but it is exceptionally difficult to achieve the strong scaling necessary to reach biologically relevant timescales. Here, we present a new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus. This framework combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring. Copernicus enables execution as single parallel jobs with automatic resource allocation. Even for a small protein such as villin (9,864 atoms), Copernicus exhibits near-linear strong scaling from 1 to 5,376 AMD cores. Starting from extended chains we observe structures 0.6 A from the native state within 30h, and achieve sufficient sampling to predict the native state without a priori knowledge after 80--90h. To match Copernicus' efficiency, a classical simulation would have to exceed 50 microseconds per day, currently infeasible even with custom hardware designed for simulations.read more
Citations
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Book ChapterDOI
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
TL;DR: GROMACS as mentioned in this paper is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world.
Journal ArticleDOI
HTMD: High-Throughput Molecular Dynamics for Molecular Discovery
TL;DR: HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery, is presented.
Journal ArticleDOI
On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.
Stefan Doerr,G. De Fabritiis +1 more
TL;DR: An automatic, iterative, on-the-fly method for learning and sampling molecular simulations in the context of ligand binding for the case of trypsin-benzamidine binding is demonstrated, achieving a converged binding affinity in approximately one microsecond, 1 order of magnitude faster than classical sampling.
Journal ArticleDOI
Automatic GROMACS topology generation and comparisons of force fields for solvation free energy calculations
TL;DR: A new framework is presented that enables fully automated generation of GROMACS topologies for any of these force fields and an automated setup for parallel adaptive optimization of high-throughput free energy calculation by adjusting lambda point placement on the fly.
Journal ArticleDOI
Evaluating the Effects of Cutoffs and Treatment of Long-range Electrostatics in Protein Folding Simulations
Stefano Piana,Kresten Lindorff-Larsen,Robert M. Dirks,John K. Salmon,Ron O. Dror,David E. Shaw,David E. Shaw +6 more
TL;DR: The results show that the free energy of folding is relatively insensitive to the choice of cutoff beyond 9 Å, and it is found that the structural properties of the unfolded state depend more strongly on the two approximations examined here.
References
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Journal ArticleDOI
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