New GPU Applications Accelerate Search for More Effective Medicines and Higher Quality Materials

Thursday, November 10, 2011

LAMMPS, GROMACS, GAMESS, QMCPACK Join Ranks of Top Multiple-GPU Accelerated Scientific Applications

SANTA CLARA, CA -- NVIDIA today announced that four leading applications for material-science and biomolecular modeling -- LAMMPS, GROMACS, GAMESS, and QMCPACK -- have added support for multiple GPU acceleration, enabling them to cut simulation times from days to hours.

As a result, scientists can study larger molecular models for longer time periods with greater accuracy, leading to increased knowledge of the potential impact of drugs and the effectiveness of new materials. Drug developers could also benefit from shorter discovery times and lower development costs.

The four scientific modeling applications join a growing list of applications -- including AMBER, NAMD and TeraChem, among others -- that enable university, government and industry researchers to advance research by leveraging the power of GPUs.

"Wide access to inexpensive, energy efficient supercomputing enabled by GPUs has the potential to accelerate the pace of scientific research," Sumit Gupta, manager of the Tesla business unit at NVIDIA. "The benefit of this computing power to science is significant, such as enabling researchers to more quickly and accurately simulate biological behavior of protein and drug candidate interactions prior to expensive and time-consuming animal studies and patient trials."

The four applications are widely used by scientists engaged in using supercomputing to advance modeling in a variety of key areas:

  • GAMESS is a quantum chemistry application important in the design of new drugs and materials. It uses computational methods to solve the electronic structure and properties of molecules.
  • GROMACS enables the simulation of biomolecular interactions between proteins and drug candidates. It can be used to study protein folding and mis-folding, which is relevant in understanding such diseases as Alzheimer's, Huntington's disease and some forms of cancer.
  • LAMMPS is utilized to model, at the atomic scale, soft (biomolecules, polymers) or solid-state (metals, semiconductors) materials.
  • QMCPACK simulates the properties of materials, achieving high accuracy and excellent scalability using a continuum quantum Monte Carlo method.

"We like to push the envelope as far as we can toward highly scalable efficient code. GPU technology is the most promising way to achieve this goal. Given our association with a DOE laboratory, energy efficiency is equally important, which is another benefit of accelerating quantum chemistry using GPUs."
-- Mark Gordon, distinguished professor at Iowa State University's Chemistry Department, director of the Applied Mathematical Sciences Program at AMES Laboratory, project lead for GAMESS

"GROMACS 4.6 supported by GPUs is expected to accelerate simulation performance 2-3 times faster than previously possible. The greater simulation speed enables research scientists to have deeper insights into the biological behavior of drug candidate and protein receptors involved in diseases."
-- Erik Lindahl, professor of Theoretical & Computational Biophysics, KTH Royal Institute, and technology professor of Computational Structural Biology, at Stockholm University's AlbaNova University Center

"For major workloads using QMCPACK, we're seeing a 3x node-to-node speedup for single-GPU nodes over dual-socket CPU nodes. We're also seeing excellent scaling of this performance for hundreds of GPUs. This allows us to investigate material properties at an unprecedented scale and level of accuracy."
-- Jeongnim Kim, R&D scientist at Oak Ridge National Laboratory.

"Molecular dynamics practitioners are handicapped by well-known timescale limitations: they can't simulate long enough to model many phenomena of interest," said one of the original LAMMPS developers. "Simulation timescales can be extended dramatically by use of large-scale clusters of GPUs."
-- Steve Plimpton, distinguished member of technical staff at Sandia National Laboratories

"The Availability of many computationally efficient GPU nodes locally has allowed us to approach drug design in a new way, giving fresh insights into disease mechanisms. With GPUs, we've been able to run many more simulations with fewer assumptions, creating more realistic models."
-- Dr. Michael Kuiper, computational scientist at Victorian Partnership for Advanced Computing

Biomolecular and material-science researchers interested in a free trial of these new these GPU-accelerated applications can register today for the NVIDIA® Tesla™ MD SimCluster program. This off-the-shelf integrated cluster solution is fully optimized to simulate large-size models and achieve higher accuracy, while significantly reducing simulation time.

For more information about NVIDIA Tesla GPUs visit here. For more information about the NVIDIA CUDA® architecture visit here.

Tags / Keywords:
NVIDIA, CUDA, GPU, GPU computing, supercomputing, parallel computing, GPGPU, high performance computing, HPC, developers, research, scientific computing, LAMMPS, GROMACS, GAMESS, QMCPACK, AMBER, NAMD, TeraChem

NVIDIA (NASDAQ: NVDA) awakened the world to computer graphics when it invented the GPU in 1999. Today, its processors power a broad range of products from smart phones to supercomputers. NVIDIA's mobile processors are used in cell phones, tablets and auto infotainment systems. PC gamers rely on GPUs to enjoy spectacularly immersive worlds. Professionals use them to create visual effects in movies and design everything from golf clubs to jumbo jets. And researchers utilize GPUs to advance the frontiers of science with high-performance computing. The company holds more than 2,100 patents worldwide, including ones covering ideas essential to modern computing. For more information, see

Certain statements in this press release including, but not limited to statements as to: the effects, benefits and impact of GPU technology on certain scientific applications and the effects of the company's patents on modern computing are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended July 31, 2011. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

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NVIDIA (NASDAQ: NVDA) is the AI computing company. Its invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world. More information at 
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