Cray Powers Geospatial AI Revolution With Breakthrough Deep Learning Performance
06/17/2019 | 03:02am EDT
SEATTLE and FRANKFURT, Germany, June 17, 2019 (GLOBE NEWSWIRE) -- 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 Statement This 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.