Cray Adds NVIDIA Tesla K40 GPU Accelerators to Its Complete Line of Supercomputing Systems – Nov 19, 2013– Seattle, USA (Techreleased) – Global supercomputer leader Cray Inc. today announced the Cray® CS300™ line of cluster supercomputers and the Cray XC30 supercomputers are now available with the NVIDIA® Tesla® K40 GPU accelerators. Designed to solve the most demanding supercomputing challenges, the NVIDIA Tesla K40 provides 40 percent higher peak performance than its predecessor, the Tesla K20X GPU.
“The addition of the NVIDIA K40 GPUs furthers our vision for Adaptive Supercomputing, which provides outstanding performance with a computing architecture that accommodates powerful CPUs and highly-advanced accelerators from leading technology companies like NVIDIA,” said Barry Bolding, vice president of marketing at Cray. “We have proven that acceleration can be productive at high scalability with Cray systems such as ‘Titan’, ‘Blue Waters’, and most recently with the delivery of a Cray XC30 system at the Swiss National Supercomputing Centre (CSCS). Together with Cray’s latest OpenACC 2.0 compiler, the new NVIDIA K40 GPUs can process larger datasets, reach higher levels of acceleration and provide more efficient compute performance, and we are pleased these features are now available to customers across our complete portfolio of supercomputing solutions.”
The NVIDIA Tesla K40 GPU accelerator features 12 gigabytes of ultra-fast GDDR5 memory, 2,880 CUDA® parallel processing cores and is built using the NVIDIA Kepler™ compute architecture. It is available in both the air and liquid-cooled models of the Cray CS300 cluster systems and Cray XC30 supercomputers.
“The NVIDIA Tesla GPU accelerators are the highest performance, most energy-efficient accelerators we have ever built,” said Sumit Gupta, general manager of Tesla Accelerated Computing products at NVIDIA. “With Tesla accelerators added to the Cray CS300 cluster systems and Cray XC30 supercomputers, Cray customers now have additional options to accelerate a wide range of compute- and data-intensive applications and workloads.”