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Open-Source Multi-GPU-Accelerated QM/MM Simulations with AMBER and QUICK.
Cruzeiro, Vinícius Wilian D; Manathunga, Madushanka; Merz, Kenneth M; Götz, Andreas W.
Affiliation
  • Cruzeiro VWD; San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, United States.
  • Manathunga M; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.
  • Merz KM; Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute of Cyber-Enabled Research, Michigan State University, East Lansing, Michigan 48824, United States.
  • Götz AW; Department of Chemistry, Department of Biochemistry and Molecular Biology, Institute of Cyber-Enabled Research, Michigan State University, East Lansing, Michigan 48824, United States.
J Chem Inf Model ; 61(5): 2109-2115, 2021 05 24.
Article in En | MEDLINE | ID: mdl-33913331
ABSTRACT
The quantum mechanics/molecular mechanics (QM/MM) approach is an essential and well-established tool in computational chemistry that has been widely applied in a myriad of biomolecular problems in the literature. In this publication, we report the integration of the QUantum Interaction Computational Kernel (QUICK) program as an engine to perform electronic structure calculations in QM/MM simulations with AMBER. This integration is available through either a file-based interface (FBI) or an application programming interface (API). Since QUICK is an open-source GPU-accelerated code with multi-GPU parallelization, users can take advantage of "free of charge" GPU-acceleration in their QM/MM simulations. In this work, we discuss implementation details and give usage examples. We also investigate energy conservation in typical QM/MM simulations performed at the microcanonical ensemble. Finally, benchmark results for two representative systems in bulk water, the N-methylacetamide (NMA) molecule and the photoactive yellow protein (PYP), show the performance of QM/MM simulations with QUICK and AMBER using a varying number of CPU cores and GPUs. Our results highlight the acceleration obtained from a single or multiple GPUs; we observed speedups of up to 53× between a single GPU vs a single CPU core and of up to 2.6× when comparing four GPUs to a single GPU. Results also reveal speedups of up to 3.5× when the API is used instead of FBI.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Software / Molecular Dynamics Simulation Language: En Year: 2021 Type: Article

Full text: 1 Database: MEDLINE Main subject: Software / Molecular Dynamics Simulation Language: En Year: 2021 Type: Article