Cray Powers Geospatial AI Revolution With Breakthrough Deep Learning Performance
June 17 2019 - 3:01AM
Today at the 2019 International Supercomputing Conference in
Frankfurt, Germany, global supercomputer leader Cray Inc.
(Nasdaq:CRAY) announced enhanced capabilities to empower data
scientists and engineers who are innovating in the field of
Geospatial AI. Cray introduced a new Geospatial Reference
Configuration as well as new features in its Cray® Urika®-CS and
Urika®-XC AI and Analytics software suites. The new features
include an augmented Deep Learning Plugin that provides
best-in-class deep neural network performance training and
broadened support for deep learning frameworks. In performance
studies, the plugin showed training time reductions up to 23% over
open source alternatives for a single node, dense GPU
configuration. Both the reference configuration and plugin are
designed for IT and AI teams implementing complex infrastructure to
support Geospatial AI workloads. Cray also announced that it has
delivered and installed a Cray CS™ Series system at the U.S.
Geological Survey agency to support AI initiatives in geospatial
analysis and the agency’s mission to provide reliable information
for understanding the Earth.
Geospatial AI is the marriage of geospatial data and artificial
intelligence. It promises to be one of the most important
uses of AI across a range of industries such as oil and gas
companies, state and local governments, property and casualty
insurance businesses, weather forecasting centers, and beyond. Data
scientists are exploring the use of AI, deep learning and machine
learning to deliver new applications and insights based on
geospatial data. For example:
- Oil and gas companies will perform market supply analysis by
applying AI to satellite images of tank farms and refineries.
- Municipal governments will use AI to detect changes in
satellite imagery for infrastructure planning and disaster and
resiliency response planning.
- Property and casualty insurance businesses will apply AI to
satellite imagery for disaster impact analysis and claim fraud
detection.
- Weather forecasters will make more accurate predictions because
Geospatial AI uncovers new information, such as soil moisture, with
high resolution.
Shorter Training Times Advance Geospatial AI
Innovation.
As Geospatial AI becomes core to organizational missions, the
time to develop and refine neural network models at optimal
accuracy becomes a challenging factor to innovation. To shorten the
time data scientists spend developing Geospatial AI applications,
Cray is releasing updates to the Urika-CS and Urika-XC AI and
Analytics software suites. The augmented Cray Programming
Environment (PE) Deep Learning Plugin will significantly reduce
training times for complex neural network models. Internal
performance studies, using the widely-available ResNet-152 and
Inception-V4 neural network models, have shown significant training
time improvements. Coupled with Cray's hyperparameter optimization
capabilities, the Cray Urika AI and Analytics suites dramatically
improve data scientist productivity and accelerate the development
of advanced Geospatial AI applications.
New Reference Configuration for Geospatial
AI
The availability of new sources of geospatial data is driving
the adoption of AI. Implementing a Geospatial AI workflow requires
a balanced system that is able to handle the demands of data
preparation and model development. Cray is introducing a new
Geospatial AI Reference Configuration comprised of CS-StormÔ 500NX
GPU accelerated nodes and CS500 CPU nodes that will be able to
handle the entire Geospatial AI workflow.
“Geospatial AI presents both data and compute challenges for
data science and IT teams tasked with developing new applications.
Our forte has long been understanding performance issues and
improving performance with supercomputing technologies,” said Per
Nyberg, vice president market development, AI at Cray. “Complete
systems optimized for the geospatial workflow and enhanced with
high-performance deep learning eliminate boundaries faced by
geospatial teams exploring and implementing advanced AI
applications.”
USGS Chooses Cray for Geospatial Innovation
The US Geological Survey (USGS), the science arm of the U.S.
Department of the Interior, has selected a Cray CS Series system to
further the use of AI in natural sciences. USGS is active in
promoting the use of machine and deep learning in areas ranging
from earth observation, numerical weather prediction,
hydrology, solid earth geoscience and land imaging.
The updated versions of the Urika-CS AI and Analytics software
suites and the Geospatial Reference Configuration are expected to
be available within 30 days.
To learn more and to see a live demo of Cray geospatial
capabilities, stop by the Cray booth E-921 at ISC19.
About Cray Inc.Cray Inc. (Nasdaq:CRAY) combines
computation and creativity so visionaries can keep asking questions
that challenge the limits of possibility. Drawing on more than 45
years of experience, Cray develops the world’s most advanced
supercomputers, pushing the boundaries of performance, efficiency
and scalability. Cray continues to innovate today at the
convergence of data and discovery, offering a comprehensive
portfolio of supercomputers, high-performance storage, data
analytics and artificial intelligence solutions. Go to
www.cray.com for more information.
Safe Harbor StatementThis press release contains forward-looking
statements within the meaning of Section 21E of the Securities
Exchange Act of 1934 and Section 27A of the Securities Act of 1933,
including, but not limited to, statements related to the
availability and performance of the enhancements to its Urika AI
and Analytics software suites and Geospatial Reference
Configuration and the features and functionality of the Urika AI
and Analytics software suites and Geospatial Reference
Configuration. These statements involve current expectations,
forecasts of future events and other statements that are not
historical facts. Inaccurate assumptions and known and unknown
risks and uncertainties can affect the accuracy of forward-looking
statements and cause actual results to differ materially from those
anticipated by these forward-looking statements. Factors that could
affect actual future events or results include, but are not limited
to, the risk that Cray is not able to successfully complete its
planned product development efforts in a timely fashion or at all,
the risk that the enhancements to its Urika AI and Analytics
software suites and Geospatial Reference Configuration are not
generally available when expected or at all, the risk that its
Urika AI and Analytics software suites and Geospatial Reference
Configuration do not have the features and functionality expected
or do not perform as expected and such other risks as identified in
the Company’s quarterly report on Form 10-Q for the quarter ended
March 31, 2019, and from time to time in other reports filed by
Cray with the U.S. Securities and Exchange Commission. You should
not rely unduly on these forward-looking statements, which apply
only as of the date of this release. Cray undertakes no duty to
publicly announce or report revisions to these statements as new
information becomes available that may change the Company’s
expectations.
CRAY and Urika are registered trademarks of Cray Inc. in the
United States and other countries. CS is a trademark of Cray Inc.
Other product and service names mentioned herein are the trademarks
of their respective owners.
Cray Media:Diana Brodskiy415/306-6199pr@cray.com
Cray Investors:Paul Hiemstra206/701-2044ir@cray.com
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