New GPU Applications Accelerate Search for More Effective Medicines and Higher Quality Materials
LAMMPS, GROMACS, GAMESS, QMCPACK Join Ranks of Top Multiple-GPU accelerated Scientific Applications
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SANTA CLARA, Calif. – Nov. 10, 2011 – 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:
--Mark Gordon, distinguished professor at Iowa State University’s Chemistry Department,
“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.”
“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.”
“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
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