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Scene optimization of GPU-based back-projection algorithm

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Abstract

The back-projection algorithm can adapt to synthetic aperture radar imaging in any mode because of its time-domain imaging characteristics, so it has received increasing attention in scientific research and engineering practice. However, the large enormous of computation of the back-projection algorithm limits its application. Since the back-projection algorithm has a high degree of data parallelism, it is very suitable for GPU parallel processing. The data scale and hardware environment in different application scenarios will result in different bottlenecks which may prevent the back-projection algorithm from performing optimally. Thus, this paper proposes a series of strategies to aid the back-projection algorithm in achieving the best peak performance with any given problem scale on any given platform. A heuristic block strategy is proposed to optimize the peak performance of the back-projection algorithm on servers and miniaturized GPU devices, which can handle the differences in hardware platforms as well as the differences in data scales. To optimize the peak performance of the back-projection algorithm on miniaturized GPU devices, a memory management strategy is proposed by using unified memory and pinned host memory. The method proposed in this paper has achieved an acceleration ratio close to the device’s peak performance on servers and miniaturized GPU devices. By using the proposed methods, any algorithm with data-independent chunking and any GPU device with a uniform memory architecture can achieve higher peak performance.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Kusk A, Dall J (2010) SAR focusing of P-band ice sounding data using back-projection. In: 2010 IEEE international geoscience and remote sensing symposium, pp 4071–4074. https://doi.org/10.1109/IGARSS.2010.5651038

  2. Zhong H, Tang J, Ma M, Tian Z, Wu H (2019) A fast accurate back-projection algorithm for multi-receiver synthetic aperture sonar in heterogeneous environment. Geomatics Inf Sci Wuhan Univ 38(5):651–657

    Google Scholar 

  3. Liu, B (2013) Research on motion compensation and gpu parallel implementation of bp algorithm imaging for airborne sar. Master’s thesis, University of Electronic Science and Technology of China

  4. Wei S, Pu L, Zhang X, Shi J (2016) Image streams gpu-based back projection for complex trajectory synthetic aperture radar imaging. Telecommun Eng 56(8):879–886. https://doi.org/10.3969/j.issn.1001-893x.2016.08.009

    Article  Google Scholar 

  5. Shi J, Ma L, Zhang X (2013) Streaming BP for non-linear motion compensation SAR imaging based on GPU. IEEE J Sel Topics Appl Earth Observ Remote Sens 6(4):2035–2050. https://doi.org/10.1109/JSTARS.2013.2238891

    Article  Google Scholar 

  6. Wijayasiri A, Banerjee T, Ranka S, Sahni S, Schmalz M (2018) Dynamic data-driven sar image reconstruction using multiple GPUs. IEEE J Sel Topics Appl Earth Observ Remote Sens 11(11):4326–4338. https://doi.org/10.1109/JSTARS.2018.2873198

    Article  Google Scholar 

  7. Wijayasiri A, Banerjee T, Ranka S, Sahni S, Schmalz M (2016) Dynamic data driven image reconstruction using multiple GPUs. In: 2016 IEEE international symposium on signal processing and information technology (ISSPIT), pp 241–246 . https://doi.org/10.1109/ISSPIT.2016.7886042

  8. Bishop EE, Miller J (2021) Fast backprojection for video-SAR. In: algorithms for synthetic aperture radar imagery XXVIII, 11728, 117280 . https://doi.org/10.1117/12.2589592

  9. Zhang X, Yang P (2022) Back projection algorithm for multi-receiver synthetic aperture sonar based on two interpolators. J Marine Sci Eng 10(6):718. https://doi.org/10.3390/jmse10060718

    Article  Google Scholar 

  10. Liu X, Yue S, Ren H (2022) A \({\lambda }\)-level partition-based linear back projection algorithm to electrical resistance tomography. In: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp 1–6 . https://doi.org/10.1109/I2MTC48687.2022.9806584

  11. Li M, Yue X, Ding F, Ning B, Wang J, Zhang N, Luo J, Huang L, Wang Y, Wang Z (2022) Focused Lunar imaging experiment using the back projection algorithm based on sanya incoherent scatter Radar. Remote Sens 14(9):2048. https://doi.org/10.3390/rs14092048

    Article  Google Scholar 

  12. Ma J (2017) Study of electromagnetic scattering property analysis and bp imaging based on GPU. Master’s thesis, Xidian University

  13. Wu Z, Liu Y, Zhang L, Li N, Du K, Balz T (2015) Highly efficient synthetic aperture radar processing system for airborne sensors using CPU+GPU architecture. J Appl Remote Sens 9(1):097293. https://doi.org/10.1117/1.JRS.9.097293

    Article  Google Scholar 

  14. Zhang W, Deng Y, Wang Y (2013) A fast backprojection algorithm for spotlight mode bi-sar imaging. J Radars 2(3):357–366. https://doi.org/10.3724/SP.J.1300.2013.13031

    Article  Google Scholar 

  15. Li C, Deng Y, Su W, Gu H, Ma C, Chen J (2014) Pixel-oriented paralleled fast back projection algorithm. J Nanjing Univ Sci Technol 38(05):651–657. https://doi.org/10.14177/j.cnki.32-1397n.2014.05.034

    Article  Google Scholar 

  16. Shao YF, Wang R, Deng YK, Liu Y, Chen R, Liu G, Loffeld O (2013) Fast backprojection algorithm for bistatic SAR imaging. IEEE Geosci Remote Sens Lett 10(5):1080–1084. https://doi.org/10.1109/LGRS.2012.2230243

    Article  Google Scholar 

  17. Ge B, Chen L, An D, Zhou Z (2017) GPU-based FFBP algorithm for high-resolution spotlight SAR imaging. In: 2017 IEEE International Conference on Signal Processing Communications and Computing (ICSPCC) . https://doi.org/10.1109/ICSPCC.2017.8242555

  18. Wielage M, Cholewa F, Fahnemann C, Pirsch P, Blume H (2017) High Performance and Low Power Architectures: GPU vs. FPGA for Fast Factorized Backprojection. In: 2017 fifth international symposium on computing and networking (CANDAR), pp 351–357 . https://doi.org/10.1109/CANDAR.2017.101

  19. Pu L, Zhang X, Yu P, Wei S (2018) A fast three-dimensional frequency-domain back projection imaging algorithm based on GPU. In: 2018 IEEE Radar Conference (RadarConf18), pp 1173–1177 . https://doi.org/10.1109/RADAR.2018.8378728

  20. Portillo R, Arunagiri S, Teller PJ, Park SJ, Nguyen LH, Deroba JC, Shires D (2011) Power versus performance tradeoffs of GPU-accelerated backprojection-based synthetic aperture radar image formation. In: modeling and simulation for defense systems and applications VI, vol 8060, p 806008 . https://doi.org/10.1117/12.885120

  21. Fulai L, Xiaojiang Q, Yanghuan L, Qian S, Hanhua Z (2011) GPU-accelerated SAR backprojection in JACKET for MATLAB. In: 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)

  22. Rogan A, Carande R (2010) Improving the fast back projection algorithm through massive parallelizations. In: radar sensor technology XIV, vol. 7669, p 76690 . https://doi.org/10.1117/12.850332

  23. Zhou B, Peng Y, Yeh C, Tang J (2011) GPGPU accelerated fast convolution back-projection for radar image reconstruction. Tsinghua Sci Technol 16(3):256–263. https://doi.org/10.1016/S1007-0214(11)70037-2

    Article  Google Scholar 

  24. Frey O, Werner CL, Wegmuller U (2014) GPU-based parallelized time-domain back-projection processing for Agile SAR platforms. In: 2014 IEEE geoscience and remote sensing symposium, pp 1132–1135 . https://doi.org/10.1109/IGARSS.2014.6946629

  25. Zhang X, Zhang X, Shi J, Liu Z (2011) GPU-based parallel back projection algorithm for the translational variant BiSAR imaging. In: 2011 IEEE International geoscience and remote sensing symposium, pp 2841–2844 . https://doi.org/10.1109/IGARSS.2011.6049806

  26. Zhang Y, Xie H, Du G, Xu M, Xue Y, Xiao T (2021) Fast reconstruction of x-ray dynamic micro-Ct based on Gpu parallel computing. Nuclear Tech 44(06):3–10. https://doi.org/10.11889/j.0253-3219.2021.hjs.44.060101

    Article  Google Scholar 

  27. Wu S, Xu Z, Wang F, Yang D, Guo G (2021) an improved back-projection algorithm for GNSS-R BSAR imaging based on CPU and GPU platform. Remote Sens 13(11):2107. https://doi.org/10.3390/rs13112107

    Article  Google Scholar 

  28. Lee D, Lee D, Park E-Y, Park E-Y, Choi S, Kim H, Min J-J, Lee C, Lee C, Kim C, Kim C (2020) GPU-accelerated 3D volumetric X-ray-induced acoustic computed tomography. Biomed Opt Express 11(2):752–761. https://doi.org/10.1364/BOE.381963

    Article  Google Scholar 

  29. Liang M, Ren Z, Li G, Zhang C, Fathy AE (2022) Thz isar imaging using Gpu-accelerated phase compensated back projection algorithm. J Infrared Millimeter Waves 41(02):448–456. https://doi.org/10.11972/j.issn.1001-9014.2022.02.011

    Article  Google Scholar 

  30. Ponce O, Prats P, Rodriguez-Cassola M, Scheiber R, Reigber A (2011) Processing of circular SAR trajectories with fast factorized back-projection. In: 2011 IEEE international geoscience and remote sensing symposium, pp 3692–3695 . https://doi.org/10.1109/IGARSS.2011.6050026

  31. Zhai X, Wei L, Wang B, Xiang M (2016) Research on real-time imaging algorithm for multi-mode Sar with Gpu. Electron Measurement Technol 39(10):81–86. https://doi.org/10.19651/j.cnki.emt.2016.10.018

    Article  Google Scholar 

  32. Capozzoli A, Curcio C, Liseno A, Testa PV (2012) NUFFT-based SAR backprojection on multiple GPUs. In: 2012 Tyrrhenian workshop on advances in radar and remote sensing (TyWRRS), pp 62–68 . https://doi.org/10.1109/TyWRRS.2012.6381104

  33. Capozzoli A, Curcio C, Liseno A (2013) Fast GPU-based interpolation for SAR backprojection. Progress Electromag Res 133:259–283. https://doi.org/10.2528/PIER12071909

    Article  Google Scholar 

  34. Stringham C, Long DG (2014) GPU processing for UAS-Based LFM-CW Stripmap SAR. Photogrammet Eng & Remote Sens 80(12):1107–1115. https://doi.org/10.14358/PERS.80.12.1107

    Article  Google Scholar 

  35. Benson TM, Campbell DP, Cook DA (2012) Gigapixel spotlight synthetic aperture radar backprojection using clusters of GPUs and CUDA. In: 2012 IEEE Radar Conference, pp 0853–0858 . https://doi.org/10.1109/RADAR.2012.6212256

  36. Sun M (2013) Design of multi-core parallel computing system for backprojection imaging. Master’s thesis, Nanjing University

  37. Hu K, Zhang X, Wu W, Shi J, Wei S (2014) Three GPU-Based Parallel Schemes for SAR Back Projection Imaging Algorithm. In: 2014 IEEE 17th International Conference on Computational Science and Engineering, pp 324–328 . https://doi.org/10.1109/CSE.2014.87

  38. Ban Y, Zhang J, Chen J, Qiu X (2014) Gpu optimization method for backprojection imaging algorithm. Radar Sci Technol 12(06):659–665. https://doi.org/10.3969/j.issn.1672-2337.2014.06.018

    Article  Google Scholar 

  39. Ban Y (2014) Sar imaging algorithm and gpu acceleration research based on back projection. Master’s thesis, Nanjing University of Aeronautics and Astronautics

  40. Jiang X, Wang J, Song Q, Zhou Z (2014) Back-projection algorithm for Sar imaging with Gpu. Radar Sci Technol 12(04):350–357. https://doi.org/10.3969/j.issn.1672-2337.2014.04.002

    Article  Google Scholar 

  41. Hartley TDR, Fasih AR, Berdanier CA, Ozguner F, Catalyurek UV (2009) Investigating the use of GPU-accelerated nodes for SAR image formation. In: 2009 IEEE International Conference on Cluster Computing And Workshops . https://doi.org/10.1109/CLUSTR.2009.5289125

  42. Gocho M, Oishi N, Ozaki A (2017) Distributed Parallel Backprojection for Real-Time Stripmap SAR Imaging on GPU Clusters. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp 619–620 . https://doi.org/10.1109/CLUSTER.2017.64

  43. Liu X (2019) Signal Processing System of Back-Projection Algorithm with Multi GPU s. In: 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) . https://doi.org/10.1109/ICSIDP47821.2019.9173054

  44. Lee S, Ban I, Lee M, Jung Y, Lee W (2021) Architecture exploration of a backprojection algorithm for real-time video SAR. Sensors 21(24):8258. https://doi.org/10.3390/s21248258

    Article  Google Scholar 

  45. Hidayetoğlu M, Bicer T, de Gonzalo SG, Ren B, De Andrade V, Gursoy D, Kettimuthu R, Foster IT, Hwu WmW (2020) Petascale XCT: 3D Image reconstruction with hierarchical communications on multi-GPU nodes. In: SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pp 1–13 . https://doi.org/10.1109/SC41405.2020.00041

  46. Tang J, Deng Y, Wang R, Zhao S, Li N (2017) High-resolution slide spotlight Sar imaging by Bp algorithm and heterogeneous parallel implementation. J Radars 6(4):368–375. https://doi.org/10.12000/JR16053

    Article  Google Scholar 

  47. Fasih A, Hartley T (2010) GPU-accelerated synthetic aperture radar backprojection in CUDA. In: 2010 IEEE Radar Conference, pp 1408–1413 . https://doi.org/10.1109/RADAR.2010.5494395

  48. Bishop E, Linnehan R, Doerry A (2016) Video-SAR using higher order Taylor terms for differential range. In: 2016 IEEE Radar Conference (RadarConf) . https://doi.org/10.1109/RADAR.2016.7485169

  49. Park J, Tang PTP, Smelyanskiy M, Kim D, Benson T (2012) Efficient backprojection-based synthetic aperture radar computation with many-core processors. In: SC ’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis . https://doi.org/10.1109/SC.2012.53

  50. Rother N, Fahnemann C, Wittler J, Blume H (2020) Optimized Minimum-Search for SAR Backprojection Autofocus on GPUs Using CUDA. In: 2020 IEEE Radar Conference (RadarConf20) . https://doi.org/10.1109/RadarConf2043947.2020.9266636

  51. Chilingaryan S, Ametova E, Kopmann A, Mirone A (2020) Reviewing GPU architectures to build efficient back projection for parallel geometries. J Real-Time Image Proc 17(5):1331–1373. https://doi.org/10.1007/s11554-019-00883-w

    Article  Google Scholar 

  52. Cao Y, Guo S, Jiang S, Zhou X, Wang X, Luo Y, Yu Z, Zhang Z, Deng Y (2022) Parallel Optimisation and Implementation of a Real-Time Back Projection (BP) Algorithm for SAR Based on FPGA. Sensors 22(6):2292. https://doi.org/10.3390/s22062292

    Article  Google Scholar 

  53. Hettiarachchi DLN, Balster EJ (2021) Fixed-point processing of the SAR back-projection algorithm on FPGA. IEEE J Sel Topics Appl Earth Observ Remote Sens 14:10889–10902. https://doi.org/10.1109/JSTARS.2021.3119007

    Article  Google Scholar 

  54. Zhang B (2016) Research on imaging and motion compensation method of circular sar. Master’s thesis, University Of Electronic Science And Technology Of China

  55. Hu K (2017) Research on high-precision imaging methods for synthetic aperture radar. PhD. thesis, University Of Electronic Science And Technology Of China

  56. Zhang X, He M, He Z, Zhang J (2017) Missile borne squinted Sar back projection algorithm and implementation. Modern Defence Technol 45(06):48–53. https://doi.org/10.3969/j.issn.1009-086x.2017.06.009

    Article  Google Scholar 

  57. Wu P (2017) Sar rapid imaging and target detection method and gpu implementation. Master’s thesis, Nanjing University Of Science And Technology

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grants 71671178 and 62176249. It is also supported by the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

    Hao Gong, Ying Liu & Xiaoying Chen

  2. Beijing Raying Technology Co., Ltd, Beijing, China

    Cheng Wang

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  1. Hao Gong
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  2. Ying Liu
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Gong, H., Liu, Y., Chen, X. et al. Scene optimization of GPU-based back-projection algorithm. J Supercomput 79, 4192–4214 (2023). https://doi.org/10.1007/s11227-022-04785-w

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