Parallel cuda implementation of conflict detection for application to airspace deconfliction

Methods for maintaining separation between aircraft in the current airspace system rely heavily on human operators. A conflict is an event in which two or more aircraft experience a loss of minimum allowable separation. Interest has grown in developing more advanced automation tools to predict when...

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Veröffentlicht in:The Journal of supercomputing Jg. 71; H. 10; S. 3787 - 3810
Hauptverfasser: Thompson, Elizabeth, Clem, Nathan, Peter, David A., Bryan, John, Peterson, Barry I., Holbrook, Dave
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2015
Schlagworte:
Compilers
Computer Science
Interpreters
Processor Architectures
Programming Languages
CUDA
Airspace
Conflict detection
Quadro FX 5800
Alerting systems
Warning systems
High-performance computing
Parallel processing
Air traffic control
Tesla C1060
GPU
Collision avoidance
ISSN:0920-8542, 1573-0484
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Beschreibung
Zusammenfassung:Methods for maintaining separation between aircraft in the current airspace system rely heavily on human operators. A conflict is an event in which two or more aircraft experience a loss of minimum allowable separation. Interest has grown in developing more advanced automation tools to predict when a traffic conflict is going to occur and to assist in its resolution. The term air space deconfliction is used to describe the resolution of conflicts after they have been predicted or detected. Due to the computationally intensive character of conflict detection and airspace deconfliction, as well as their data parallel nature, they are naturally amenable to parallel processing. This work discusses a parallel implementation of a conflict detection algorithm for application to airspace deconfliction. It uses the NVIDIA Quadro FX 5800 and the Tesla C1060 graphical processing units (GPUs) in conjunction with the Compute Unified Device Architecture (CUDA) hardware/software architecture. Details of the implementation are discussed, including the use of streams for asynchronous programming and the use of multiple GPUs. The performance of the parallel implementation is compared to that of an equivalent sequential version and shown to exhibit improvement in execution time. Recommendations are provided to further improve performance of the algorithm.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-015-1467-z