SEATTLE, Dec. 06, 2017 (GLOBE NEWSWIRE) -- Global supercomputer leader Cray Inc. (Nasdaq:CRAY) today announced the Company has joined the Big Data Center (BDC) at the Department of Energy’s National Energy Research Scientific Computing Center (NERSC). The collaboration between the two organizations is representative of Cray’s commitment to leverage its supercomputing expertise, technologies, and best practices to advance the adoption of Artificial Intelligence (AI), deep learning, and data-intensive computing.
The BDC at NERSC was established with a goal of addressing the Department of Energy’s leading data-intensive science problems, harnessing the performance and scale of the Cray® XC40™ “Cori” supercomputer at NERSC. The collaboration is focused on three fundamental areas that are key to unlocking the capabilities required for the most challenging data-intensive workflows:
- Advancing the state-of-the-art in scalable, deep learning training algorithms, which is critical to the ability to train models as quickly as possible in an environment of ever-increasing data sizes and complexity;
- Developing a framework for automated hyper-parameter tuning, which provides optimized training of deep learning models and maximizes a model’s predictive accuracy;
- Exploring the use of deep learning techniques and applications against a diverse set of important scientific use cases, such as genomics and climate change, which broadens the range of scientific disciplines where advanced AI can have an impact.
“We are really excited to have Cray join the Big Data Center,” said Prabhat, Director of the Big Data Center, and Group Lead for Data and Analytics Services at NERSC. “Cray’s deep expertise in systems, software, and scaling is critical in working towards the BDC mission of enabling capability applications for data-intensive science on Cori. Cray and NERSC, working together with Intel and our IPCC academic partners, are well positioned to tackle performance and scaling challenges of Deep Learning.”
“Deep learning is increasingly dependent on high performance computing, and as the leader in supercomputing, Cray is focused on collaborating with the innovators in AI to address present and future challenges for our customers,” said Per Nyberg, Cray’s Senior Director of Artificial Intelligence and Analytics. “Joining the Big Data Center at NERSC is an important step forward in fostering the advancement of deep learning for science and enterprise, and is another example of our continued R&D investments in AI.”
About the Big Data Center at NERSC
The Big Data Center is a collaboration between the Department of Energy’s National Energy Research Scientific Computing Center (NERSC), Intel, and five Intel Parallel Computing Centers (IPCCs). The five IPCCs that are part of the Big Data Center program include the University of California-Berkeley, the University of California-Davis, New York University (NYU), Oxford University, and the University of Liverpool. The Big Data Center program was established in August 2017.
About Cray Inc.
Global supercomputing leader Cray Inc. (Nasdaq:CRAY) provides innovative systems and solutions enabling scientists and engineers in industry, academia and government to meet existing and future simulation and analytics challenges. Leveraging more than 40 years of experience in developing and servicing the world’s most advanced supercomputers, Cray offers a comprehensive portfolio of supercomputers and big data storage and analytics solutions delivering unrivaled performance, efficiency and scalability. Cray’s Adaptive Supercomputing vision is focused on delivering innovative next-generation products that integrate diverse processing technologies into a unified architecture, allowing customers to meet the market’s continued demand for realized performance. Go to www.cray.com for more information.
Cray, and the stylized CRAY mark are registered trademarks of Cray Inc. in the United States and other countries, and XC40 is a trademark of Cray Inc. Other product and service names mentioned herein are the trademarks of their respective owners.