Efficient backprojection-based synthetic aperture radar computation with many-core processors

International Conference for High Performance Computing, Networking, Storage and Analysis, SC

Volumn , Issue , 2012, Pages

  Park, Jongsoo ^a   Tang, Ping Tak Peter ^a   Smelyanskiy, Mikhail ^a   Kim, Daehyun ^a   Benson, Thomas ^b  

^a INTEL CORPORATION   (United States)

^b GEORGIA TECH RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED ARCHITECTURE; CODE TRANSFORMATION; COMPUTATION COSTS; ENERGY-EFFICIENT COMPUTING; IMAGE RECONSTRUCTION METHODS; LOCALITY OPTIMIZATION; MANY-CORE PROCESSORS; STRENGTH REDUCTION;

ALGORITHMS; COSINE TRANSFORMS; DIGITAL STORAGE; IMAGE RECONSTRUCTION; MEDICAL IMAGING; MICROPROCESSOR CHIPS; PARALLEL PROCESSING SYSTEMS;

SYNTHETIC APERTURE RADAR;

EID: 84877693508     PISSN: 21674329     EISSN: 21674337     Source Type: Conference Proceeding    
DOI: 10.1109/SC.2012.53     Document Type: Conference Paper

References (30)

1. P. Kogge, K. Bergman, S. Borkar, D. Campbell, W. Carlson, W. Dally, M. Denneau, P. Franzon, W. Harrod, K. Hill, J. Hiller, S. Karp, S. Keckler, D. Klein, R. Lucas, M. Richards, A. Scarpelli, S. Scott, A. Snavely, T. Sterling, R. S. Williams, and K. Yelick, "ExaScale Computing Study: Technology Challenges in Achieving Exascale Systems," 2008, www.cse.nd.edu/Reports/ 2008/TR-2008-13.pdf.
2. F. Niu, B. Recht, C. Re, and S. J. Wright, "HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent," in Advances in Neural Information Processing Systems (NIPS), 2011.
3. M. Wolfe, High Performance Compilers for Parallel Computing. Addison-Wesley, 1996.
4. A. Nguyen, N. Satish, J. Chhugani, C. Kim, and P. Dubey, "3.5-D Blocking Optimization for Stencil Computations on Modern CPUs and GPUs," in International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2010, pp. 1-13.
5. M. A. Richards, Fundamentals of Radar Signal Processing. McGraw-Hill, 2005.
6. D. P. Campbell, D. A. Cook, and B. P. Mulvaney, "A Streaming Sensor Challenge Problem for Ubiquitous High Performance Computing," http://www.ll.mit.edu/HPEC/agendas/proc11/Day1/Session-1/1025-Campbell.pdf, 2011.
7. D. C. Munson, Jr., J. D. O'Brien, and W. K. Jenkins, "A Tomographic Formulation of Spotlight-Mode Synthetic Aperture Radar," Proceedings of the IEEE, vol. 71, no. 8, pp. 917-925, 1983.
8. C. V. Jakowatz, Jr., D. E. Wahl, and D. A. Yocky, "Beamforming as a Foundtation for Spotlight-mode SAR Image Formation by Backprojection," in SPIE 6970, 2008.
9. A. Isola, A. Ziegler, T. Koehler, W. J. Niessen, and M. Grass, "Motion-compensated iterative cone-beam CT image reconstruction with adapted blobs as basis functions," Physics in Medicine and Biology, vol. 53, p. 6777, 2008.
10. E. Hansis, J. Bredno, D. Sowards-Emmerd, and L. Shao, "Iterative Reconstruction for Circular Cone-Beam CT with an Offset Flat-Panel Detector," in IEEE Nuclear Science Symposium Conference Record (NSS/MIC), 2010, pp. 2228-2231.
11. J. Cocke and K. Kennedy, "An Algorithm for Reduction of Operator Strength," Communications of the ACM, vol. 20, no. 11, pp. 850-856, 1977.
12. F. E. Allen, J. Cocke, and K. Kennedy, "Reduction of Operator Strength," Program Flow Analysis, pp. 79-101, 1981.
13. M. D. Desai and W. K. Jenkins, "Convolution Backprojection Image Reconstruction for Spotlight Mode Synthetic Aperture Radar," Image Processing, IEEE Transactions on Image Processing, vol. 1, no. 4, pp. 505-517, 1992.
14. C. V. Jakowatz, Jr., D. E. Wahl, D. C. G. Paul H. Eichel, and P. A. Thompson, Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach. Kluwer Academic Publishers, 1996.
15. G. M. Amdahl, "Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities," in AFIPS spring joint computer conference, 1967, pp. 483-485.
16. A. V. Aho, M. S. Lam, R. Sethi, and J. D. Ullman, Compilers: Principles, Techniques, and Tools. Addison-Wesley, 2007.
17. J.-M. Muller, Elementary Functions: Algorithms and Implementation, Birkhauser.
18. B. Cordes and M. Leeser, "Parallel Backprojection: A Case Study in High-Performance Reconfigurable Computing," EURASIP Journal on Embedded Systems, vol. 2009, p. 1, 2009.
19. T. D. R. Hartley, A. R. Fasih, C. A. Berdanier, F. Özgüner, and U. V. Catalyurek, "Investigating the Use of GPU-Accelerated Nodes for SAR Image Formation," in IEEE International Conference on Cluster Computing (CLUSTER), 2009, pp. 1-8.
20. A. R. Fasih and T. D. R. Hartley, "GPU-accelerated synthetic aperture radar backprojection in CUDA," in IEEE Radar Conference, 2010, pp. 1408-1413.
21. R. Portillo, S. Arunagiri, P. J. Teller, S. J. Park, L. H. Nguyen, J. C. Deroba, and D. Shires, "Power versus Performance Trade-offs of GPU-accelerated Backprojection-based Synthetic Aperture Radar Image Formation," Proceedings of SPIE, Modeling and Simulation for Defense Systems and Applications VI, vol. 8060, 2011.
22. T. M. Benson, D. P. Campbell, and D. A. Cook, "Gigapixel Spotlight Synthetic Aperture Radar Backprojection Using Clusters of GPUs and CUDA," in IEEE Radar Conference, 2012, pp. 853-858.
23. M. Soumekh, Synthetic Aperture Radar Signal Processing with MATLAB Algortihms. Wiley Interscience, 1999.
24. 2logN Back-Projection Algorithm for SAR Image Formation," in Asilomar Conference on Signals, Systems and Computers, 2000, pp. 3-7.
25. D. E. Wahl, D. A. Yocky, and C. V. Jakowatz, Jr., "An implementation of a fast backprojection image formation algorithm for spotlight-mode SAR," Algorithms for Synthetic Aperture Radar Imagery XV, vol. 6970, p. 69700H, 2008.
26. NVIDIA, "OpenCL Programming Guide for the CUDA Architecture," www.nvidia.com/content/cudazone/download/OpenCL/NVIDIA-OpenCL-ProgrammingGuide. pdf, 2009.
27. J. E. Volder, "The CORDIC Trigonometric Computing Technique," IRE Transactions on Electronic Computers, pp. 330-334, 1959.
28. J. R. Humphrey, D. K.Price, K. E.Spagnoli, A. L. Paolini, and E. J. Kelmelis, "CULA: hybrid GPU accelerated linear algebra routines," in SPIE Conference Series, vol. 7705, Apr. 2010.
29. S. McIntosh-Smith and J. Irwin, "The best of both worlds: Delivering Aggregated performance for high-performance math libraries in accelerated system," in ISC, 2007, pp. 331-340.
30. M. Deisher, M. Smelyanskiy, B. Nickerson, V. W. Lee, M. Chuvelev, and P. Dubey, "Designing and dynamically load balancing hybrid lu for multi/many-core," Computer Science-Research and Development, vol. 26, no. 3-4, pp. 211-220, 2011.