Abstract

We describe a set of algorithms that allow to simulate dihydrofolate reductase (DHFR, a common benchmark) with the AMBER all‐atom force field at 160 nanoseconds/day on a single Intel Core i7 5960X CPU (no graphics processing unit (GPU), 23,786 atoms, particle mesh Ewald (PME), 8.0 Å cutoff, correct atom masses, reproducible trajectory, CPU with 3.6 GHz, no turbo boost, 8 AVX registers). The new features include a mixed multiple time‐step algorithm (reaching 5 fs), a tuned version of LINCS to constrain bond angles, the fusion of pair list creation and force calculation, pressure coupling with a “densostat,” and exploitation of new CPU instruction sets like AVX2. The impact of Intel's new transactional memory, atomic instructions, and sloppy pair lists is also analyzed. The algorithms map well to GPUs and can automatically handle most Protein Data Bank (PDB) files including ligands. An implementation is available as part of the YASARA molecular modeling and simulation program from www.YASARA.org. © 2015 The Authors Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Highlights

  • Molecular simulations with empirical force fields like AMBER,[1] CHARMM,[2] or OPLS[3] are enjoying a phase of enthusiastic interest, thanks to the arrival of personal supercomputers, that is, graphics processing units (GPUs) that can accelerate science well as video games

  • Believe that molecular dynamics (MD) simulations are best approached with a capable “home base” on the CPU, which can handle the countless complications in real-life applications (like knowledge-based force fields,[6] X-ray,[7] and nuclear magnetic resonance (NMR) refinement[8]) and offloads tasks to the GPU when beneficial

  • We focus on this home base and describe a number of algorithms to generally improve simulation performance, and we benchmark them on a single Intel Core i7 CPU with AVX2

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Summary

Introduction

Molecular simulations with empirical force fields like AMBER,[1] CHARMM,[2] or OPLS[3] are enjoying a phase of enthusiastic interest, thanks to the arrival of personal supercomputers, that is, graphics processing units (GPUs) that can accelerate science well as video games. We focus on this home base and describe a number of algorithms to generally improve simulation performance, and we benchmark them on a single Intel Core i7 CPU with AVX2. Most of the algorithms are well suited to accelerate simulations using multiple CPUs and GPUs. simulation performance is usually considered less important than accuracy (which we focused on previously[6,9]), only fast simulations allow an important accuracy check: whether the force field can reproduce folding and structural changes of proteins or not.[10]

Results and Discussion
Evaluation of simulation accuracy
AX toms
Methods
Materials and Methods
Full Text
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