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Take drug discovery, says Gupta. It takes around five years in a wet lab to sort through millions of compounds to come up with thousands of possible drug leads that might lead to something that a pharma peddler can put through clinical trials and government approval processes, which themselves take maybe five to seven years. While the wet labs have been automated with robotics, there is a lot of trial and error and the mechanical processes still take a lot of time.

What Nvidia and its software partners want research labs to do instead is to use computational chemistry to simulate new compounds rather than synthesizing them physically, use virtual screening in a simulation to see if these fake compounds will bind to targeted proteins (which is how many medicines work), and then whittle down those millions of potential compounds down to thousands inside the computer.

"The idea is not to replace the wet labs, but to home in on the interesting compounds more quickly," says Gupta. "So instead of taking the shotgun approach with the wet lab, you do it in the simulation."

The methods and programs that pharmaceutical companies can deploy to find new drugs can be used to design all manner of new chemicals and materials. About half of the cycles on the TeraGrid in the United States (a cluster of clusters funded by the National Science Foundation) are burnt up running molecular simulations (29 per cent), chemical simulations (13 per cent) and materials simulations (6 per cent). So these applications could certainly use a boost from GPUs, and application providers are jockeying to make sure their applications can take advantage of GPUs.

Temple University did a research project with soap maker Proctor & Gamble to show how GPUs could be used to do a lot more flops for a lot less money. Temple's techies found that a personal supercomputer with two Tesla C1060 GPU co-processors could run P&G's molecular dynamics simulation twice as fast as 128 Opteron cores on its Cray XT3 super or 1,024 Power cores on its BlueGene/L super - once it was tuned for the GPUs, that is. That simulation is based on HOOMD, short for Highly Optimized Object Oriented Molecular Dynamics, a software package that was written by hackers at Ames Laboratory, funded by the US Department of Energy.

The Tesla Bio Workbench is not a product, but a community of hardware and software vendors. Thus far, AMBER, GROMACS, LAMMPS, NAMD, TeraChem, and VMD, which are molecular dynamics and quantum chemistry applications, have been tweaked to exploit Tesla GPUs, as have bioinformatics applications such as CUDASW++, GPU-HMMER, and MUMmerGPU.

Others will no doubt follow, and Nvidia will no doubt create workbench communities for engineering, financial, oil and gas, media and rendering, and other key industries where GPU co-processors will be snapped up enthusiastically. Mathematica and Matlab, two popular mathematics simulation programs from Wolfram Research and Mathworks, respectively, can already exploit Tesla GPUs. ®

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