Parallel cuda implementation of conflict detection for application to airspace deconfliction

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• 作者：Elizabeth Thompson ; Nathan Clem ; David A. Peter…
• 关键词：CUDA ; GPU ; High ; performance computing ; Parallel processing ; Airspace ; Air traffic control ; Alerting systems ; Conflict detection ; Warning systems ; Collision avoidance ; Quadro FX 5800 ; Tesla C1060
• 刊名：The Journal of Supercomputing
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：71
• 期：10
• 页码：3787-3810
• 全文大小：1,977 KB
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• 作者单位：Elizabeth Thompson (1)
  Nathan Clem (1)
  David A. Peter (2)
  John Bryan (2)
  Barry I. Peterson (2)
  Dave Holbrook (2)
  
  1. Department of Engineering, Indiana University-Purdue University Fort Wayne, 2101 Coliseum Blvd., East, Fort Wayne, IN, 46805, USA
  2. Raytheon Company, 1010 Production Road, Fort Wayne, IN, 46808, USA
  
• 刊物类别：Computer Science
• 刊物主题：Programming Languages, Compilers and Interpreters
  Processor Architectures
  Computer Science, general
  
• 出版者：Springer Netherlands
• ISSN：1573-0484

文摘

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. Keywords CUDA GPU High-performance computing Parallel processing Airspace Air traffic control Alerting systems Conflict detection Warning systems Collision avoidance Quadro FX 5800 Tesla C1060