14 DAY TRIAL // GPUs may have big parallel processing performance, but that ultimately doesn't matter if applications don't support it, so it is understandable that NVIDIA would eagerly announce if an important software gained such support, not to mention four.
Indeed, according to its new press release, LAMMPS, GROMACS, GAMESS and QMCPACK all have multiple GPU acceleration support.
These are four of the most often used applications in material-science and biomolecular modeling.
Along with AMBER, NAMD and TeraChem, to name a few, these applications are utilized by government, industry and university researchers.
“Wide access to inexpensive, energy efficient supercomputing enabled by GPUs has the potential to accelerate the pace of scientific research,” said 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.”
GAMESS is used for designing drugs and materials, while GROMACS simulates biomolecular interactions between proteins and drug candidates.
Meanwhile, LAMMPS makes models of biomolecules and polymers (on the atomic scale) or solid-state material (metals, semiconductors, etc.).
Finally, QMPACK simulates material properties using a continuum quantum Monte Carlo method, for high accuracy and scalability.
"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," said Steve Plimpton, distinguished member of technical staff at Sandia National Laboratories
Now that these programs have multi-GPU support, they should be able to leverage the parallel processing capabilities for studying molecular models for longer time periods, discovering drug uses and developing medicine faster, determining their effectiveness and possible impact, and so on.