Defense and Intelligence
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The defense and intelligence community heavily relies on accurate and timely information in its strategic and day-to-day operations. Intelligence gathering and assessment are essential parts of these activities that comprises of data coming from a number of diverse sources such as satellites, UAVs, surveillance cameras, and radar. Converting the raw data collected from such diverse sources into actionable information requires a significant infrastructure. GPUs represent a “game changing” technology that dramatically increases productivity while reducing cost, power, and facilities use.
In the following charts, we highlight work completed on NPP and CuFFT.
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Key areas where GPUs are already showing significantly improved performance are:
The role of image processing is expanding for defense and intelligence. The amount of imagery available today is unprecedented. For example, geo-spatial imagery available through satellites already covers the earth’s surface five-fold. There are also over 100 million images of fingerprints stored in the FBI database. GPUs accelerate the image processing workflow including georectification, filtering algorithms, change detection, and 3D reconstruction.
Learn more about the impact of GPUs by reading the Digital Globe Case Study (PDF 90 KB) on accelerating disaster relief efforts.
Persistent Video Surveillance:
Specialists predict that the global video surveillance market will exceed $25 billion by 2016. Additionally each month, the Department of Defense collects more than 10,000 hours of aerial surveillance video in Afghanistan and Iraq. These videos must be processed and analyzed in real time. GPUs represent a great tool to archive real time performance for video processing and analytics algorithms.
Learn more by reading the Intuvision Case Study (PDF 346 KB), on Real-time Video Surveillance.
The capabilities of modern sensors continue to grow. Making use of information collected from these sensors demands increasing computing capabilities. GPUs are providing a step function in processing speed necessary to keep up with sensor capabilities providing an opportunity for real time integration of sensor data with other data sources to better understand the complex environments our defense teams now operate in.
Learn more by reading the OpCoast Case Study (PDF 88.6 KB) on Modeling Radio Jammer Effectiveness.
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|| The video displays the difference in performance between a system with CPUs to a system with GPUs. The application calculates line of sight to determine visibility for a particular geographic location. It allows analysts to quickly analyze the ground, aerial and radar visibility to determine such things like optimal radar placements. The areas with limited visibility are shadowed in green and the areas of high visibility are shadowed in red.|
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||This video shows the analytical process involved in changing low quality videos taken by UAVs into accurate mission critical data that can be used for analysis. Multiple algorithms have to be applied to improve the quality.
All algorithms require compute power, especially when computing in real time.
On the right, you can see the required compute flops. Once the image is clean,it can be analyzed to identify any other moving targets. All is done in real time, which would have been impossible without GPUs.
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||This video shows real time face recognition made possible by GPUs. The camera captures people walking through the hallway. Images of their faces are automatically matched against an existing database for identification.
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