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AMBER

Both explicit solvent Particle Mesh Ewald (PME) Molecular Dynamics and implicit solvent Generalized Born (GB) 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.

GPU-Accelerate Your AMBER and NAMD Simulations Cut simulation time from days to hours with the Tesla Molecular Dynamics SimCluster. Preconfigured to accelerate AMBER or NAMD, all you need to do is load your models and start simulating. Test drive one today!

Download and Installation

• Download AMBER 11 (supports CUDA GPUs)
• Implicit solvent Generalized Born simulations
• Explicit solvent PME simulations in NVE, NVT, and NPT canonical ensembles
• Installation instructions are here.

Benchmark Data

The following charts show the acceleration and the scalability when running the explicit solvent PME and implicit solvent GB Benchmarks on a six-core CPU versus Tesla C2070 and Tesla M2090. Details can be found on the AMBER 11 NVIDIA benchmark page courtesy of the San Diego Supercomputing Center.

Technical Papers, News And Presentations

• New publications based on AMBER 11 GPU-acceleration work coming soon
• New Flagship Tesla M2090 CPU Sets Record for AMBER Molecular Dynamics Simulation
• "Redesigning Molecular Dynamics for GPUs and GPU Clusters" by – Scott Le Grand, NVIDIA, GTC 2010
• "Bringing GPUs to Mainstream Molecular Dynamics Packages" by – Ross Walker, The San Diego Supercomputer Center , GTC 2010
• Overview of NVIDIA CUDA Support in AMBER- Lessons Learned, Capabilities Gained – Ross Walker, The San Diego Supercomputer Center, from 2009 GPU Technology Conference, SC09.
• Computational Biophysics and Long Range Electrostatics on GPUs – Scott Le Grand, NVIDIA, GTC 2009.

CUDA-Acceleration in Related Verticals

• Accelerating Molecular Dynamics applications using GPUs
• Accelerating Quantum Chemistry applications using GPUs

Discussion Forums

• GPU computing communities on GPUcomputing.net

Interviews / Videos

• GPU Technology Conference 2010, video from keynote speech by NVIDIA CEO, Jen-Hsun Huang
• SC09 BLOG on NVIDIA nTersect: AMBER Research Code Gets Impressive Gains with NVIDIA CUDA

The Tesla Bio Workbench applications can be deployed on GPU-based desktop personal supercomputers or in data center solutions. Built on the revolutionary massively parallel CUDA architecture, these solutions are designed to accelerate the pace of computational science.

RECOMMENDED HARDWARE CONFIGURATION

Desktop Workstation Configuration	Data Center Configuration
GPUs 2-4 Tesla C2075 GPUs CPU and Main Memory 2.33 GHz Quad-Core x86 CPU 12 GB or more	GPUs per node 2 or more Tesla M2090 GPUs CPU and Main Memory 2.33 GHz Quad-Core x86 CPU per server 12 GB or more per server

WORKSTATION SOLUTIONS TESLA PERSONAL SUPERCOMPUTER For personal supercomputing at your desk Learn more >		DATA CENTER SOLUTIONS TESLA GPU COMPUTING CLUSTERS For computing with large-scale installations Learn more >