Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well‐exploited with a combination of single instruction multiple data, multithreading, and message passing interf...

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Veröffentlicht in:	Journal of computational chemistry Jg. 36; H. 26; S. 1990 - 2008
Hauptverfasser:	Kutzner, Carsten, Páll, Szilárd, Fechner, Martin, Esztermann, Ansgar, de Groot, Bert L., Grubmüller, Helmut
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Blackwell Publishing Ltd 05.10.2015 Wiley Subscription Services, Inc John Wiley and Sons Inc
Schlagworte:	Analytical chemistry benchmark Benchmarking Benchmarks Central processing units Computer Simulation CPUs energy efficiency GPU hybrid parallelization Molecular chemistry molecular dynamics Molecular Dynamics Simulation parallel computing Simulation Software Software and Updates MD energy efficiency hybrid parallelization molecular dynamics GPU parallel computing benchmark
ISSN:	0192-8651, 1096-987X, 1096-987X
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Abstract	The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well‐exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)‐based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off‐loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance‐to‐price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer‐class GPUs this improvement equally reflects in the performance‐to‐price ratio. Although memory issues in consumer‐class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost‐efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well‐balanced ratio of CPU and consumer‐class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. Molecular dynamics (MD) simulation is a crucial tool for the study of (bio)molecules. MD simulations typically run for weeks or months even on modern computer clusters. Choosing the optimal hardware for carrying out these simulations can increase the trajectory output twofold or threefold. With GROMACS, the maximum amount of MD trajectory for a fixed budget is produced using nodes with a well‐balanced ratio of CPU and consumer‐class GPU resources.
AbstractList	The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well‐exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)‐based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off‐loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance‐to‐price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer‐class GPUs this improvement equally reflects in the performance‐to‐price ratio. Although memory issues in consumer‐class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost‐efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well‐balanced ratio of CPU and consumer‐class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well‐exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)‐based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off‐loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance‐to‐price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer‐class GPUs this improvement equally reflects in the performance‐to‐price ratio. Although memory issues in consumer‐class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost‐efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well‐balanced ratio of CPU and consumer‐class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. Molecular dynamics (MD) simulation is a crucial tool for the study of (bio)molecules. MD simulations typically run for weeks or months even on modern computer clusters. Choosing the optimal hardware for carrying out these simulations can increase the trajectory output twofold or threefold. With GROMACS, the maximum amount of MD trajectory for a fixed budget is produced using nodes with a well‐balanced ratio of CPU and consumer‐class GPU resources. The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well-exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)-based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off-loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime.The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well-exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)-based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off-loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime. The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well-exploited with a combination of single instruction multiple data, multithreading, and message passing interface (MPI)-based single program multiple data/multiple program multiple data parallelism while graphics processing units (GPUs) can be used as accelerators to compute interactions off-loaded from the CPU. Here, we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Although hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed as these cards do not support error checking and correction memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime.
Author	Páll, Szilárd Grubmüller, Helmut Esztermann, Ansgar Kutzner, Carsten Fechner, Martin de Groot, Bert L.
AuthorAffiliation	1 Theoretical and Computational Biophysics Department Max Planck Institute for Biophysical Chemistry Am Fassberg 11 37077 Göttingen Germany 2 Theoretical and Computational Biophysics KTH Royal Institute of Technology 17121 Stockholm Sweden
AuthorAffiliation_xml	– name: 2 Theoretical and Computational Biophysics KTH Royal Institute of Technology 17121 Stockholm Sweden – name: 1 Theoretical and Computational Biophysics Department Max Planck Institute for Biophysical Chemistry Am Fassberg 11 37077 Göttingen Germany
Author_xml	– sequence: 1 givenname: Carsten surname: Kutzner fullname: Kutzner, Carsten email: ckutzne@gwdg.de organization: Theoretical and Computational Biophysics Department, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany – sequence: 2 givenname: Szilárd surname: Páll fullname: Páll, Szilárd organization: Theoretical and Computational Biophysics, KTH Royal Institute of Technology, Stockholm, 17121, Sweden – sequence: 3 givenname: Martin surname: Fechner fullname: Fechner, Martin organization: Theoretical and Computational Biophysics Department, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany – sequence: 4 givenname: Ansgar surname: Esztermann fullname: Esztermann, Ansgar organization: Theoretical and Computational Biophysics Department, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany – sequence: 5 givenname: Bert L. surname: de Groot fullname: de Groot, Bert L. organization: Theoretical and Computational Biophysics Department, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany – sequence: 6 givenname: Helmut surname: Grubmüller fullname: Grubmüller, Helmut organization: Theoretical and Computational Biophysics Department, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077, Göttingen, Germany
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26238484$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173956$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan)
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Keywords	MD energy efficiency hybrid parallelization molecular dynamics GPU parallel computing benchmark
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Snippet	The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing...
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SubjectTerms	Analytical chemistry benchmark Benchmarking Benchmarks Central processing units Computer Simulation CPUs energy efficiency GPU hybrid parallelization Molecular chemistry molecular dynamics Molecular Dynamics Simulation parallel computing Simulation Software Software and Updates
Title	Best bang for your buck: GPU nodes for GROMACS biomolecular simulations
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