Computer Science ›› 2020, Vol. 47 ›› Issue (4): 25-29.doi: 10.11896/jsjkx.190500029

• Computer Architecture • Previous Articles     Next Articles

Extraction Algorithm of NDVI Based on GPU Multi-stream Parallel Model

ZUO Xian-yu1,2, ZHANG Zhe1,3, SU Yue-han1, LIU Yang1,2, GE Qiang1,2, TIAN Jun-feng1,2   

  1. 1 Institute of Data and Knowledge Engineering,College of Computer and Information Engineering,Henan University,Kaifeng,Henan 475004,China;
    2 Henan Key Laboratory of Big Data Analysis and Processing,Henan University,Kaifeng,Henan 475004,China;
    3 Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
  • Received:2019-05-06 Online:2020-04-15 Published:2020-04-15
  • Contact: TIAN Jun-feng,born in 1980,Ph.D,lecturer.His main research interests include remote sensing image proces-sing and image encryption.
  • About author:ZUO Xian-yu,born in 1979,Ph.D,associate professor.His research interests include parallel computing and remote sensing image processing.
  • Supported by:
    This work was supported by the National Key Research and Development Program (2017YFD0301105),National Natural Science Foundation of China(U1704122,U1604145) and Key R&D and Promotion Projects of Henan Province (182102210242,182102110065,192102210096).

Abstract: In general,the Normalized Differential Vegetation Index (NDVI) extraction algorithm optimized by GPU usually adopts GPU multi-thread parallel model,and there are problems such as data transmission between CPU and GPU and weak correlation calculations taking more time,which affect the further improvement of performance.Aiming at the above problems and the characteristics of NDVI,a NDVI extraction algorithm based on GPU multi-stream parallel model was proposed.Through the features of CUDA stream and Hyper-Q,the GPU multi-stream parallel model can overlap not only data transmission and kernel execution,but also kernel execution and kernel execution,and further improve parallelism and resources utilization of GPU.Firstly,the NDVI algorithm is optimized by the GPU multi-thread parallel model,and the optimized procedures are decomposed to find out the parts of the algorithm with data transmission or weak correlation calculation.Secondly,parts of data transmission and weak correlation calculation are reconstructed and optimized by GPU multi-stream parallel model to achieve overlapping between weak correlation calculation and weak correlation calculation,or weak correlation calculation and data transmission.Finally,expe-riments of NDVI algorithm that based on both GPU parallel models respectively were carried out,and the remote sensing image taken by the GF1 satellite were used as experimental data.The experimental results show that the proposed algorithm,when the image is larger than 12000x13400 pixels,achieves about 1.5 times acceleration compared with the traditional parallel NDVI algorithm based on the GPU multi-thread parallel model,and about 260 times acceleration compared with the NDVI sequential extraction algorithm,which has better performance and parallelism.

Key words: NDVI, GPU parallel model, Remote sensing information extraction, Overlap, Parallel acceleration

CLC Number: 

  • TP751
[1]ZUO X Y,SHANG D D,LI B B,et al.Parallel Computing Reasearch of Normalized Difference Vegetation Index Based on OpenMP and OpenCV[J].Remote Sensing Science,2017,5:33-40.
[2]MENG H.Research on remote sensing image normalized difference vegetation index based on GPU[D].Kaifeng:HeNan University,2016.
[3]ALVAREZ C J,HERRERA L J,RIVERA Z I,et al.Implementation Strategy of NDVI Algorithm with Nvidia Thrust [C]//6th Pacific-Rim Symposium on Image and Video Technology.2013:184-193.
[4]JI X,LU H Y,ZHAO T J,et al.Study on climate mean based on MODIS vegetation index[J].Journal of Guangxi University(Nat SciEd),2018,43(3):1111-1117.
[5]DI P,HU C J,LI J J,et al.Efficient method for histogram generation on GPU[J].Computer Science,2012,39(3):304-307.
[6]AHAMD L,EHSAN A,AMIRALI B,et al.TELEPORT:Hardware/software alternative to CUDA shared memory progra-mming[J].Micro processors and Microsystems,2018,63:169-181.
[7]CAO J,HUANG K J,WANG J H,et al.Particle filter multispeakers tracking algorithm based on GPU and its application[J].Application Research of Computers,2018,35(7):1965-1969.
[8]SHEN X J,HOU B C,HAN D J,et al.Calculation of enhanced vegetation index based on GPU and matrix partition[J].Remote Sensing Information,2018,33(3):63-69.
[9]GRUBOV V,MAKSIMENKO V A,NEDAI-VOZOV V,et al.Real-Time Big EEG Data Processing With CUDA Parallel Computing Technology [C]//2018 2nd School on Dynamics of Complex Networks and their Application in Intellectual Robotics (DCNAIR).2018:49-52.
[10]KEI I,FUMIHIKO I,KENICHI H,et al.An OpenACC Optimizer for Accelerating Histogram Computation on a GPU[C]//Euromicro International Conference on Parallel,Distributed,and Network-Based Processing.2016:468-477.
[11]WU Q J,CHEN Y M,JOHN P W,et al.An effective parallelization algorithm for DEM generalization based on CUDA[J].Environmental Modelling & Software,2019,114:64-74.
[12]LIU Z T,YAN B X,DONG M L,et al.Application of parallel computing in edge extraction algorithm in dynamic photogrammetry[J].Computer Engineering and Design,2019,40(1):97-102 .
[13]HONG H C,ZHENG L X,PAN S W,et al.Computation of Gray Level Co-Occurrence Matrix Based on CUDA and Optimization for Medical Computer Vision Application [J].IEEE Access,2018,6:67762-67770.
[14]RENAN P,CRISTIANA B,RICARDO F,et al.Video Processing on GPU:Analysis of Data Transfer Overhead[C]//IEEE International Symposium on Computer Architecture and High Performance Computing Workshops.2016:18-23.
[15]MA X,HAN W,et al.A Parallel Multi-swarm Particle Swarm Optimization Algorithm Based on CUDA Streams[C]//2018 Chinese Automation Congress (CAC).2018:3002-3007.
[16]LI H,YU D,KUMAR A,et al.Performance modeling in CUDA streams — A means for high-throughput data processing[C]//IEEE International Conference on Big Data (Big Data).2014:301-310.
[17]RYAN S L,QIU Q R,et al.Effective Utilization of CUDA Hyper-Q for Improved Power and Performance Efficiency[C]//IEEE International Parallel and Distributed Processing Sympo-sium Workshops.2016:1160-1169.
[18]SREEPATHI P,THAZHUTHAVEETIL M J,GOVINDARAJAN R,et al.Improving GPGPU concurrency with elastic kernels[C]//ASPLOS’13.ACM,2013:407-418.
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