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|Cray Works With Industry Leaders to Reach New Performance Milestone for Deep Learning at Scale|
Running larger deep learning models is a path to new scientific possibilities, but conventional systems and architectures limit the problems that can be addressed, as models take too long to train. Cray worked with
By accelerating the training process, instead of waiting weeks or months for results, data scientists can obtain results within hours or even minutes. With the introduction of supercomputing architectures and technologies to deep learning frameworks, customers now have the ability to solve a whole new class of problems, such as moving from image recognition to video recognition, and from simple speech recognition to natural language processing with context.
Deep learning problems share algorithmic similarities with applications traditionally run on a massively parallel supercomputer. By optimizing inter-node communication using the Cray® XC™ Aries network and a high performance MPI library, each training job can leverage significantly more compute resources – reducing the time required to train an individual model.
“Cray’s proficiency in performance analysis and profiling, combined with the unique architecture of the XC systems, allowed us to bring deep learning problems to our Piz Daint system and scale them in a way that nobody else has,” said Prof. Dr.
“Applying a supercomputing approach to optimize deep learning workloads represents a powerful breakthrough for training and evaluating deep learning algorithms at scale,” said Dr.
A team of experts from Cray,
To simplify the building and deploying of deep learning environments in supercomputing, Cray is supporting its Cray XC customers with deep learning toolkits, such as the Microsoft Cognitive Toolkit, that allow customers to run deep learning applications at their fullest potential – at scale on a Cray supercomputer. Fusing high performance computing capability with deep learning is another step forward in Cray’s vision of the convergence of supercomputing and big data.
“Only Cray can bring the combination of supercomputing technologies, supercomputing best practices, and expertise in performance optimization to scale deep learning problems,” said Dr.
For more information on Cray’s machine learning and deep learning solutions and the Cray XC series of supercomputers, and please visit the Cray website at www.cray.com.
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