AMBER -- Assisted Model Building with Energy Refinement: Difference between revisions
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[http:// | '''AMBER''' <ref>[http://dx.doi.org/10.1002/jcc.20290 David A. Case, Thomas E. Cheatham III, Tom Darden, Holger Gohlke, Ray Luo, Kenneth M. Merz Jr., Alexey Onufriev, Carlos Simmerling, Bing Wang, Robert J. Woods "The Amber biomolecular simulation programs", Journal of Computational Chemistry '''26''' pp. 1668-1688 (2005)]</ref> is a package of [[Materials modelling and computer simulation codes |molecular simulation programs]], which includes source code and demonstrations. The same name (see [[AMBER forcefield | AMBER]] entry) is given to a set of molecular mechanical [[force fields]] for the simulation of [[Biological systems |biomolecules]] (which are in the public domain, and are used in a variety of simulation programs). | ||
==AMBER on Tesla GPUs== | |||
The explicit solvent and implicit solvent simulations in AMBER have been accelerated using CUDA-enabled GPUs. Paired with a Tesla GPU computing solution built on the CUDA architecture, it enables more than 10x speedup compared to a single quad-core CPU <ref>source: [http://www.nvidia.com/object/amber_on_tesla.html NVIDIA]</ref> (''see also'': [[Tesla Bio Workbench]]). | |||
==References== | ==References== | ||
<references/> | |||
[[Category: Materials | ==External links== | ||
*[http://ambermd.org/ AMBER homepage] | |||
[[Category: Materials modelling and computer simulation codes]] | |||
[[Category: Force fields]] | [[Category: Force fields]] |
Latest revision as of 19:05, 15 September 2012
AMBER [1] is a package of molecular simulation programs, which includes source code and demonstrations. The same name (see AMBER entry) is given to a set of molecular mechanical force fields for the simulation of biomolecules (which are in the public domain, and are used in a variety of simulation programs).
AMBER on Tesla GPUs[edit]
The explicit solvent and implicit solvent simulations in AMBER have been accelerated using CUDA-enabled GPUs. Paired with a Tesla GPU computing solution built on the CUDA architecture, it enables more than 10x speedup compared to a single quad-core CPU [2] (see also: Tesla Bio Workbench).