CUDA Spotlight: GPU-Accelerated Dynamic Airspace Configuration R&D

This week's spotlight is on Bart Gallet, a principal analyst at Mosaic ATM, a business founded in 2004 to conduct research and development for improving the efficiency and safety of air transportation. This interview is part of the CUDA Spotlight Series.

NVIDIA: Bart, tell us about Mosaic ATM.
Bart: Mosaic ATM is an engineering and consulting firm focusing on technology solutions for Air Traffic Management (ATM) and having a particular expertise in technology and engineering analysis for applied NextGen research.

Our staff consists of industry-recognized experts who combine substantial operational knowledge of the National Airspace System (NAS) with extensive capabilities and experience in computer science, operations research, systems engineering and human factors.

NVIDIA: How does GPU computing play a role in your R&D?
Bart: The design of airspace and traffic flows through airspace is currently conducted through manual processes. In order to achieve the vision of NextGen, airspace resources will be allocated to accommodate traffic demand dynamically.

The computational loads required for wide-area dynamic airspace configuration (DAC) processing is a significant impediment for transitioning this concept into a pertinent and usable technology. Customized computing hardware could be applied to address some of these issues, but they are costly and more than often the software does not scale graciously as new technologies become available. For this reason, we are looking at CUDA and GPU-based systems.

NVIDIA: Why is dynamic airspace configuration important?
Bart: Today’s airspace operations are based on the division of airspace into “sectors,” and the route and altitude restrictions that are used to organize flight paths such that they conform to sector boundaries.

Currently, the route and altitude restrictions placed on aircraft are largely a result of the static sectorization of airspace. This static division of airspace allows an Air Traffic Specialist (ATS) to effectively organize traffic flows and maintain situational awareness, which thereby enables air transportation safety.

As we move toward NextGen, it is essential that these important aspects of current procedures are understood and that future procedures are designed to continue to satisfy the root level safety requirements. The ideal air transportation system would allow flights to follow their user-preferred trajectories to the maximum extent possible.

However, it is still necessary to ensure that the workload and complexity, which is referred to as Dynamic Density (DD), in the sector remains below the maximum threshold.

Today, the coordinated linkage between airspace design and required flight routes results in a managed complexity in the sector and allows the ATS to maintain situational awareness and safety. Under NextGen, the Dynamic Airspace Configuration and Trajectory-Based Operations concepts seek to allow the use of user-preferred trajectories as much as possible, which will reduce flight time and fuel usage.

NVIDIA: What kind of advantages have you achieved with CUDA?
Bart: We have implemented our parallel CUDA DD library (CUDD) entailing four DD metrics on an NVIDIA Tesla C2050 PCI express graphics card with 448 streaming processors and 3Gof memory over various sizes of data sets.

The CUDD library was interfaced with a test harness and that also called the original sequential code of our particular DD metrics and which served as our baseline. The tests were performed on our PC that hosts the Tesla and which contains an Intel i7 running at 2.80GHz with 8GB of memory. For smaller data sets the advantages are not as pronounced as with larger data sets. However, in a larger data set the CUDA version of the same Pre-Sort phase of the computation is more than 14 times faster than the sequential version.

NVIDIA: When you think about the future of air traffic management, what will be the biggest change from today?
Bart: The most significant change in air traffic management in the future will be the integration of the computer with the human decision making process. Today, computer-based tools are provided to controllers and air traffic managers, but those tools have very little ‘intelligence’ and what intelligence does exist in these tools is provided to the human in a minimally interactive manner.

Because of the need for system monitoring, anomaly detection and handling, and public perception and acceptance, the human being as a decision maker will remain a core component of air traffic management in the future.

However, a tight integration of human and computer will allow the computer to provide monitoring and safety/robustness enhancements for the human being, while the human also provides the same for the computer.

NVIDIA: Are you pursuing any other projects using GPU computing?
Bart:  Many opportunities exist to apply GPU computing to Mosaic ATM NextGen projects. The first of two likely candidates is the use of GPU computing to accelerate the computationally intensive post-processing of recorded air traffic data to generate metrics and to mine the data for events and anomalies. We are also considering the use of GPU computing to support the execution of a particle filter in real-time for detection of specific events and flight patterns.

Mosaic's Autonomous Systems Group is also currently examining the potential for GPU-derived performance gains in applications that involve tracking of visual features over water and determination of camera pose from noisy sensor metadata and results from image and video processing algorithms.


Mr. Bart Gallet is a Principal Analyst for Mosaic ATM, where he has been researching and developing technologies for advanced autonomy of Unmanned Aircraft Systems (UAS) in the NAS. He holds a B.S. in Electronic Industrial Engineering from the Katholieke Industriële Hogeschool der Kempen in Belgium and a Masters in Avionics from Cranfield University in the UK.

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