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  More bang for your buck: Improved use of GPU nodes for GROMACS 2018.

Kutzner, C., Páll, S., Fechner, M., Esztermann, A., de Groot, B. L., & Grubmüller, H. (2019). More bang for your buck: Improved use of GPU nodes for GROMACS 2018. Journal of Computational Chemistry, (in press). doi:10.1002/jcc.26011.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-ED8C-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-ED91-E
Genre: Journal Article

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 Creators:
Kutzner, C.1, Author              
Páll, S., Author
Fechner, M.1, Author              
Esztermann, A.1, Author              
de Groot, B. L.2, Author              
Grubmüller, H.1, Author              
Affiliations:
1Department of Theoretical and Computational Biophysics, MPI for biophysical chemistry, Max Planck Society, ou_578631              
2Research Group of Computational Biomolecular Dynamics, MPI for biophysical chemistry, Max Planck Society, ou_578573              

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Free keywords: CUDA; GPU; GROMACS; benchmark; computer simulations; energy efficiency; high throughput MD; molecular dynamics; parallel computing; performance to price
 Abstract: We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more toward the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift toward GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware.

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Language(s): eng - English
 Dates: 2019-07-01
 Publication Status: Published online
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 Rev. Method: Peer
 Identifiers: DOI: 10.1002/jcc.26011
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Title: Journal of Computational Chemistry
Source Genre: Journal
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