Cray expands deep learning to supercomputers and cluster systems

November 15, 2016 // By Julien Happich
At the 2016 Supercomputing Conference in Salt Lake City, Utah, Cray Inc. unveiled new deep learning capabilities across its line of supercomputing and cluster systems. With validated deep learning toolkits and the most scalable supercomputing systems in the industry, Cray customers can now run deep learning workloads at their fullest potential – at scale on a Cray supercomputer.

“The convergence of supercomputing and big data analytics is happening now, and the rise of deep learning algorithms is evidence of how customers are increasingly using high performance computing techniques to accelerate analytics applications,” said Steve Scott, senior vice president and chief technology officer at Cray.

“Training problems look very much like classical supercomputing problems. We believe that with our Cray Programming Environment, validated toolkits, and the latest processing technologies, we have the right combination of hardware and software expertise to help our customers efficiently execute deep learning workloads now and in the future.”

Cray has validated and made available several deep learning toolkits on Cray XC and Cray CS-Storm systems to simplify the transition to running deep learning workloads at scale. These toolkits include the Microsoft Cognitive Toolkit (previously CNTK), TensorFlow, NVIDIA DIGITS (Deep Learning GPU Training System), Caffe, Torch, and MXNet.

Additionally, the Cray CS-Storm system – a dense, accelerated GPU cluster supercomputer that offers 850 GPU teraflops in a single rack – now supports the NVIDIA Tesla P100 for PCIe data center accelerator and the NVIDIA Tesla M40 deep learning training accelerator. And with the addition of the NVIDIA Tesla P100 to the Cray XC50 supercomputer, Cray now has a variety of scalable systems well suited for running a wide array of emerging deep and machine learning applications.

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