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Heterogeneous CPU+GPU-Enabled Simulations for DFTB Molecular Dynamics of Large Chemical and Biological Systems

preprint
submitted on 15.03.2019 and posted on 18.03.2019 by Sarah I. Allec, Yijing Sun, Jianan Sun, Chia-En A. Chang, Bryan Wong
We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Compared to homogenous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3,974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly-solvated protein has been treated at a quantum-based MD level of detail.

Funding

U.S. Department of Energy, National Energy Technology Laboratory (NETL), Award No. DE-FE0030582

History

Email Address of Submitting Author

bryan.wong@ucr.edu

Institution

University of California, Riverside

Country

USA

ORCID For Submitting Author

0000-0002-3477-8043

Declaration of Conflict of Interest

No conflict of interest

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