Swiss National Supercomputing Center Plans to Scale Application Performance Heights With NVIDIA GPUsMarch 19, 2013
Ask any weatherman: Predicting the weather is not easy. Throw in Switzerland’s many diverse microclimates and topographical features and the challenge is even greater.
The Swiss National Supercomputing Center (CSCS), one of Europe’s top institutions for computational research, plans to change that by applying a petaflop of GPU-accelerated processing power to the problem.
CSCS is building a new Cray XC30 supercomputer called “Piz Daint,” named after a mountain in the Swiss Alps. Piz Daint will be extended with GPU accelerators to dramatically expand the breadth and depth of the center’s research and discovery in climate and weather modeling, as well as a host of other fields, such as astrophysics, materials science and life science.
In particular, CSCS is working with MeteoSwiss, Switzerland’s national weather service, to reach the holy grail of meteorology: predicting local and national weather patterns days or even weeks ahead of time with the highest degree of accuracy.
With NVIDIA Tesla K20X GPU accelerators, Piz Daint will have more than 1 petaflops of performance – that’s 1,000 trillion floating point operations per second – which is expected to make it the fastest GPU accelerator-based scientific supercomputer in Europe when it becomes operational in early 2014.
Based on the NVIDIA Kepler architecture – the world’s fastest and most energy-efficient high performance computing architecture – the Tesla GPUs will dramatically accelerate performance at an affordable cost. This is key as running complex, compute-intensive simulations of large-scale environmental phenomenon accurately takes massive computing resources.
This is an ideal task for a GPU-powered supercomputer, but beyond the abilities of typical CPU-based systems, and certainly impossible for CPUs to do quickly.
“Piz Daint will help advance our research into alpine climate and weather patterns by leaps and bounds,” said Thomas Schulthess, director of CSCS. “With GPU acceleration, researchers can run many more sophisticated, ultra-high-resolution models, giving us an unprecedented level of visibility and understanding into how these systems work.”