Cray Systems Power Deep Learning in Supercomputing at Scale
November 15 2016 - 2:01AM
At the 2016 Supercomputing Conference in Salt Lake City, Utah,
global supercomputer leader Cray Inc. (Nasdaq:CRAY) today announced
the Company has 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.
PGS, a leading marine geophysical company, is running machine
learning algorithms on its Cray XC40™ supercomputer, nicknamed
“Abel.” Machine learning technologies such as regularization and
steering can be applied to a significant computational problem in
seismic exploration – Full Waveform Inversion (FWI), which is a
methodology that seeks to find a high-resolution, high-fidelity
representation of the subsurface in the ultra-deep Gulf of
Mexico.
“This class of problems is notoriously hard,” said Dr. Sverre
Brandsberg-Dahl, global chief geophysicist for Imaging and
Engineering, at PGS. “It is a multidimensional ill-posed
optimization problem that is far from automated and requires lots
of skilled resources’ intervention – sometimes more art than
science in many cases. Our Cray XC40 system was able to learn how
to best steer refracted and diving waves for deep model updates and
how best to reproduce the sharp salt boundaries in the Gulf of
Mexico. Machine learning at scale on our Cray supercomputer showed
dramatic improvement in the quality of the inversion process as
compared to current state-of-the-art FWI.”
For more information on Cray’s machine learning and deep
learning solutions, the Cray XC series of supercomputers, and the
Cray CS-Storm system, please visit the Cray website at
www.cray.com.
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 XC,
CS-Storm, XC50 and XC40 are trademarks of Cray Inc. Other product
and service names mentioned herein are the trademarks of their
respective owners.
Cray Media:Nick Davis206/701-2123pr@cray.com
Cray Investors:Paul Hiemstra206/701-2044ir@cray.com
Cray (NASDAQ:CRAY)
Historical Stock Chart
From Sep 2024 to Oct 2024
Cray (NASDAQ:CRAY)
Historical Stock Chart
From Oct 2023 to Oct 2024