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Thanks to our Tesla Accelerated Computing Platform, researchers can now run computations and visualize the results on the same GPUs in a single system — either simultaneously (“in-situ”
or separately. And Titan, the U.S.’s most powerful supercomputer, now holds the title of largest GPU-accelerated visualization system in the world. That’s a major step forward. Researchers worldwide run high-powered supercomputer simulations to understand how the galaxy formed, how the human brain works and more. Then they create visualizations of their work to gain new insights.
Until recently, researchers did this work on two different systems. They used one for computation (running the science application). Then they switched to another to render the results visually. It could take days or weeks to complete the simulation before they could visualize it. And depending upon the configuration of the data center, this could require moving massive amounts of data from the simulation system to the visualization system. During this process, if researchers found an error or a parameter changed, they would have to re-run the entire simulation and render again. It’s an arduous process that could delay scientific results.
or separately. And Titan, the U.S.’s most powerful supercomputer, now holds the title of largest GPU-accelerated visualization system in the world. That’s a major step forward. Researchers worldwide run high-powered supercomputer simulations to understand how the galaxy formed, how the human brain works and more. Then they create visualizations of their work to gain new insights.Until recently, researchers did this work on two different systems. They used one for computation (running the science application). Then they switched to another to render the results visually. It could take days or weeks to complete the simulation before they could visualize it. And depending upon the configuration of the data center, this could require moving massive amounts of data from the simulation system to the visualization system. During this process, if researchers found an error or a parameter changed, they would have to re-run the entire simulation and render again. It’s an arduous process that could delay scientific results.