Skip to main content

Advertisement

SpringerLink
Log in

Parallel cuda implementation of conflict detection for application to airspace deconfliction

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Kuchar JR, Yang LC (2000) A review of conflict detection and resolution modeling methods. IEEE Trans Intell Transp Syst 1(4):179–189

    Article  Google Scholar 

  2. Sislak D, Volf P, Pechoucek M, Suri N (2011) Automated conflict resolution utilizing probability collectives optimizer. IEEE Trans Syst Man Cybern Part C Appl Rev 41(3):365–375

    Article  Google Scholar 

  3. Gerasch TE (2000) “Four-Dimensional Airspace Deconfliction MOIE Final Report,” Mitre Technical Report,MTR 00W0000093, October

  4. Von Viebahn H, Schiefele J (1997) A method for detecting and avoiding flight hazards. In: Proceedings of SPIE Enhanced and Synthetic Vision Conference, Orlando, FL

  5. Brudnicki DJ, Lindsay KS, McFarland AL (1997) Assessment of field trials, algorithmic performance, and benefits of the User Request Evaluation Tool (URET) conflict probe. In: Proceedings of the 16th AIAA/IEEE Digital Avionics Systems, DASC, Part 1, Irvine, CA

  6. Isaacson DR, Erzberger H (1997) Design of a conflict detection algorithm for the Center/TRACON Automation System. In: Proceedings of the 16th AIAA/IEEE Digital Avionics Systems Conference, DASC Part 1 (of 3), Irvine, CA

  7. Sridhar B, Chatterji GB (1997) Computationally efficient conflict detection methods for air traffic management. In: Proceedings of the IEEE American Control Conference Part 3 (of 6), Albuquerque, NM

  8. Carnes D, Wieland F (2002) Techniques to enhance performance of an existing aviation simulation. In: Proceedings of the ASA/ACM/IEEE Winter Simulation Conference, San Diego, CA

  9. Du Z, Yin Z, Bader D (2010) A tile-based parallel Viterbi algorithm for biological sequence alignment on GPU with CUDA. In: Proceedings of the IEEE International Symposium on Parallel and Distributed Processing, Workshops and PhD Forum (IPDPSW), Atlanta, GA

  10. van der Lann W, Jalba A, Roerdink J (2011) Accelerating wavelet lifting on graphics hardware using CUDA. IEEE Trans Parallel Distrib Syst 22(1):132–146

    Article  Google Scholar 

  11. Han B, Taha T (2010) Acceleration of spiking network based pattern recognition on NVIDIA graphics processors. Appl Opt 49(1):B83–B91

    Article  Google Scholar 

  12. Thompson EA, Anderson TR (2014) A CUDA implementation of the continuous space language model. J Supercomput 68(1):65–86

    Article  Google Scholar 

  13. Laosooksathit S, Nassar R, Leangsuksun C, Paun M (2014) Reliability-aware performance model for optimal GPU-enabled cluster environment. J Supercomput 68(3):1630–1651

    Article  Google Scholar 

  14. CUDA C Programming Guide NVIDIA (2012) [Online]. Available: http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html. (Accessed 11 July 2013)

  15. NVIDIA Performance Primitives (2013) (Online). Available: https://developer.nvidia.com/npp. (Accessed 16 July 2013)

  16. NVIDIA Corporation, “Thrust,” NVIDIA (2014) (Online). Available: http://docs.nvidia.com/cuda/thrust/index.html#axzz317ospKh5. (Accessed 9 Dec. 2014)

  17. Hill JFS (2001) Computer Graphics Using OpenGL, 2nd edn. Prentice-Hall, Upper Saddle River

    Google Scholar 

  18. Harris M (2014) How to Overlap Data Transfers in CUDA C/C++. NVIDIA Corporation (Online). Available: http://devblogs.nvidia.com/parallelforall/how-overlap-data-transfers-cuda-cc/. (Accessed 16 Dec 2014)

Download references

Acknowledgments

This work was supported by the Security and Software Engineering Research Center (\(\hbox {S}^{2}\hbox {ERC}\)) through Purdue SPS Grant 207185 and by the National Science Foundation (NSF) through the grant DGE-SMP 1010908 titled “Science Master’s Program: Concentration in Wireless Technology and Systems Engineering”.

Author information

Authors and Affiliations

  1. Department of Engineering, Indiana University-Purdue University Fort Wayne, 2101 Coliseum Blvd., East, Fort Wayne, IN, 46805, USA

    Elizabeth Thompson & Nathan Clem

  2. Raytheon Company, 1010 Production Road, Fort Wayne, IN, 46808, USA

    David A. Peter, John Bryan, Barry I. Peterson & Dave Holbrook

Authors
  1. Elizabeth Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathan Clem
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David A. Peter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John Bryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Barry I. Peterson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dave Holbrook
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elizabeth Thompson.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thompson, E., Clem, N., Peter, D.A. et al. Parallel cuda implementation of conflict detection for application to airspace deconfliction. J Supercomput 71, 3787–3810 (2015). https://doi.org/10.1007/s11227-015-1467-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-015-1467-z

Keywords

Search

Navigation