We’ve had more than ample opportunity to comment on NVIDIA’s GPUs here over the past year or so, and the point I keep coming back to is that they have been genius at building an ecosystem around their product that has created a network effect — the more people that use NVIDIA’s gear, the easier it is for still more people to come into the fold.
Today NVIDIA continues that strategy in the life sciences vertical with the announcement of the Bio Workbench. With the Workbench NVIDIA went out and rounded up 11 popular life sciences applications, made sure they could take advantage of NVIDIA’s GPUs with CUDA, and launched a community site around them. According to that site the codes that are already done include names you’ll probably recognize — AMBER, GROMACS, HOOMD, LAMMPS, NAMD, TeraChem, VMD — with others such as GROMOS, GPU-HMMER, and CUDA-SmithWaterman “coming soon.”
As with GPU-enabled applications in other domains, when they work, they work
“We are working on a new GPU-based technique in the VMD molecular dynamics visualization software that investigates how small molecules like oxygen and CO2 migrate inside proteins. This research is critical in the study of enzymatic reaction mechanisms,” said John Stone, senior research programmer, University of Illinois at Urbana-Champaign. “A simulation that takes 1 day to run on a GPU-based workstation would have taken 30 days to run on a CPU-based machine, rendering it impractical for real research.”
In the life sciences scientists are often trying to use numerical models to create beneficial agents without the mess and expense of experiments in wet labs, generally the point of a lot of numerical modeling no matter what the field. But life sciences results are often a lot more visceral than those of your friendly-neighborhood numerical magnetohydronamicist. For example, researchers at Temple University are using HOOMD from the Bio Workbench codes to improve soaps and shampoos, and talking up the advantages they are seeing over BlueGene/L
Improving the cleaning power of shampoos and liquid detergents and making them more environmentally friendly is as much a computer problem as it is a balance of chemicals. By harnessing the parallel processing power of NVIDIA Tesla GPUs, researchers at Temple University are developing a computer simulation model which provides companies like Procter and Gamble with a fast, cost effective and accurate tool for research and development of surfactant molecules.
…”The computer models needed to accurately simulate surfactant properties are extremely demanding in terms of computational power,” said Axel Kohlmeyer of the Institute for Computational Molecular Science at Temple University. “We discovered that by adding just two NVIDIA Tesla C1060 GPUs, each node in our newest cluster can do 16 times more work, and thus multiplies our local compute capacity far beyond what we could previously get through the national supercomputing centers.”
“To put this into context, we can run a single GPU-optimized molecular dynamics simulation on two Tesla GPUs as fast as we can on 128 CPU cores of a Cray XT3 supercomputer or on 1024 CPUs of an IBM BlueGene/L machine with conventional software,” continues Dr. Kohlmeyer. “With the NVIDIA Tesla GPU-based solution, we now have a more powerful, cost-effective solution that will enable us to advance critical research at a much faster pace. We’re moving rapidly ahead to deploy a larger Tesla GPU cluster at Temple, which will give another huge boost to our work.”