Universities, research institutions, and companies in the geosciences, molecular biology, and medical diagnostics fields face a big challenge: as their simulation models become increasingly complex, so does their need for better computational resources.
NVIDIA’s Tesla brand of computing products is based on a graphics’ processing unit (GPU) that transforms today’s workstations into personal supercomputers. Tesla can reduce computation time from weeks to hours. What’s more, it makes parallel computing power more widespread and affordable.
“Many of the molecular structures we analyze are so large that they take weeks of processing time to run the calculations required for their physical simulation,” said John Stone, senior research programmer at the University of Illinois Urbana-Champaign. “NVIDIA’s GPU computing technology has given us a 100-fold increase in some of our programs, and this is on desktop machines where previously we had to run these calculations to a cluster.”
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