Enabling GPU-accelerated High Performance Geospatial Line-of-Sight Calculations

Transcription

1 Enabling GPU-accelerated High Performance Geospatial Line-of-Sight Calculations Bart Adams, Ph.D. Frank Suykens, Ph.D. Intro text for this chapter

2 Intro text for this chapter

3 Luciad Confidential - do not distribute externally Agenda Introduction Luciad LuciadLightspeed Webinar Use Case: Line-of-Sight Problem Statement Demo GPGPU LoS Algorithm Challenges: Big Data! Recap and Q&A 3

4 Luciad Confidential - do not distribute externally Introduction 4

5 Who is Luciad? Software product company Technology for Geospatial Situational Awareness Focus on Defense, Security (C4ISR, Dismounted soldier) Aviation (ATC, ATM) Maritime (ECDIS, Harbor protection, ) Strong investment in R&D and innovation 5

6 Luciad Vision Imagery,Terrain Weather,... Radar, Sensors Charts, Tracks Crowd sourced Connect Analyze Visualize Share Real-time Accurate Situational Awareness Big Data 6

7 Key Product: LuciadLightspeed Visualize and analyze high volumes of static and moving data, such as Tracks and trajectories Satellite imagery Terrain elevation Road networks Buildings No preprocessing 2D & 3D Mission critical 7

8 Key Product: LuciadLightspeed Performance obtained through intelligent use of Graphics Processing Unit (GPU) Visualization Analysis 8

9 Webinar Use Case Line-of-Sight 9

10 Problem Statement Helicopter mission planning Maintain visibility with target area Optimal radar placement Cover region with as little radars possible UAV path planning Maintain communication link Cross-terrain routing Remain invisible to enemy 10

11 Problem Statement Helicopter mission planning Maintain visibility with target area Optimal radar placement Cover region with as little radars possible UAV path planning Maintain communication link Cross-terrain routing Remain invisible to enemy Important constraints Must often be interactive Can cover large regions Used in optimization algorithms 11

12 Line-of-Sight Calculations Determine visible volume in 3D Taking terrain into account This amounts to computing For each point x, a minimal altitude h x h 12

13 Line-of-Sight Calculations Determine visible volume in 3D Taking terrain into account This amounts to computing For each point x, a minimal altitude h Basically a 2D problem in polar coordinates Find surface h(r, ) x h h(r, ) h(r, ) x = (r, ) 13

14 Line-of-Sight Visualization Often slice of 3D volume visualized Visibility at fixed altitude Visibility above terrain 14

15 Line-of-Sight Visualization Often slice of 3D volume visualized Visibility at fixed altitude Visibility above terrain Result color-coded Color mapped altitude Binary visibility 15

16 Demo 16

17 17

18 GPGPU LoS Algorithm 18

19 Line-of-Sight Calculations Find surface h(r, ) h(r, ) h(r, ) x = (r, ) 19

20 Terrain Data and the Geoid Point-sampled elevation data (e.g., DTED) Reference ellipsoid (WGS84) LOS coverage region not circular Sweep lines ( ) not straight 20

21 Terrain Data and the Geoid Point-sampled elevation data (e.g., DTED) Reference ellipsoid (WGS84) Elevation defined with respect to geoid (EGM96) geoid ellipsoid LOS coverage region not circular Sweep lines ( ) not straight Input data with respect to geoid Result often needed with respect to ellipsoid 21

22 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n 22

23 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n C B A Sequentially walk over single sweep line Keep track of increasing angle 23

24 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n C B A r1 Sequentially walk over single sweep line Keep track of increasing angle 24

25 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n C B A r1 r2 Sequentially walk over single sweep line Keep track of increasing angle 25

26 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n C B A r1 r2 r3 Sequentially walk over single sweep line Keep track of increasing angle 26

27 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n C B A r1 r2 r3 r4 Sequentially walk over single sweep line Keep track of increasing angle 27

28 Parallelize! Sampling approach r1 r2 r3 rm Store result in polar matrix n C B A r1 r2 r3 r4 r5 Sequentially walk over single sweep line Keep track of increasing angle 28

29 Parallelize! Sampling approach r1 r2 r3 A B C rm Store result in polar matrix n C B A r1 r2 r3 r4 r5 Sequentially walk over single sweep line Keep track of increasing angle Every sweep line independent Parallelize! 29

30 GPGPU LOS Algorithm Transfer elevation and geoid data of ROI to GPU Rectangular buffers in WGS84 reference for ( =0; <=360; +=d ) for (r=0; r<=max_r; r+=dr) Determine point x(r, ) on sweep line Sample elevation and geoid buffers Update angle Store resulting elevation h in polar matrix r1 r2 r3 rm n r1 r2 r3 r4 r5 30

31 GPGPU LOS Algorithm Transfer elevation and geoid data of ROI to GPU Rectangular buffers in WGS84 reference for ( =0; <=360; +=d ) for (r=0; r<=max_r; r+=dr) Determine point x(r, ) on sweep line Sample elevation and geoid buffers Update angle Store resulting elevation h in polar matrix r1 r2 r3 rm n r1 r2 r3 r4 r5 31

32 NVIDIA Tesla K40 Source: Large data sets Memory size: 12 GB Memory bandwidth: 288 GB/sec Interactive results Peak performance: 4.29 Tflops CUDA cores:

33 GPGPU LOS Algorithm Comparison with CPU implementation: 75 times speedup Diameter LoS region: 16km Radial step size: 25m (cf. DTED level 2 resolution) Angular step size: 0.25 degrees 33

34 Challenges 34

35 Multiple Coordinate References Elevation rasters can be defined in different coordinate reference systems (CRS) CRS 2 LOS region CRS 1 35

36 Multiple Coordinate References Elevation rasters can be defined in different coordinate reference systems (CRS) CRS 2 LOS region Quad-tree based adaptive lookup table Maps x in CRS 1 to x in CRS 2 CRS 1 Before sampling CRS 2 elevation raster 36

37 Multiple Coordinate References For each CRS Compute transformation lookup table Upload lookup tables to GPU Transfer elevation and geoid data of ROI to GPU Rectangular buffers in different CRS s for ( =0; <=360; +=d ) for (r=0; r<=max_r; r+=dr) Determine point x(r, ) on sweep line For each CRS Transform x->x using lookup table Sample elevation at x Update angle Store resulting elevation h in polar matrix 37

38 Big Data! Handling Large Regions E.g., deal with full Europe: 5000km x 5000km 52 GB of elevation data (30m elevation spacing, 2 bytes per sample) LOS region 38

39 Big Data! Handling Large Regions E.g., deal with full Europe: 5000km x 5000km 52 GB of elevation data (30m elevation spacing, 2 bytes per sample) Out-of-core algorithm operating on arc sectors LOS region 39

40 Big Data! Data management becomes main difficulty For each arc sector CPU: Load needed tiles from disk CPU->GPU: Remove old tiles, upload new tiles to GPU GPU: Perform LoS on arc sector GPU->CPU: Read back arc sector results CPU: Store results on disk 40

41 HF/VHF/UHF Propagation K-factor for HF radar propagation 41

42 HF/VHF/UHF Propagation K-factor for HF radar propagation K=4/3 common Flattens rays Look beyond horizon 42

43 HF/VHF/UHF Propagation K-factor for HF radar propagation K=4/3 common Flattens rays Look beyond horizon Looking at VHF/UHF propagation as well 43

44 HF/VHF/UHF Propagation K-factor for HF radar propagation K=4/3 common Flattens rays Look beyond horizon Looking at VHF/UHF propagation as well Temperature inversion warm cold 44

45 HF/VHF/UHF Propagation K-factor for HF radar propagation K=4/3 common Flattens rays Look beyond horizon Looking at VHF/UHF propagation as well Temperature inversion Results in channels 45

46 HF/VHF/UHF Propagation K-factor for HF radar propagation K=4/3 common Flattens rays Look beyond horizon Looking at VHF/UHF propagation as well Temperature inversion Results in channels Increased communication distance 46

47 Recap 47

48 Recap Line-of-Sight important in many defense use-cases Highly parallelizable Need to deal with Propagation effects Heterogeneous data Big data sets NVIDIA Tesla benefits Performance Many cores Large memory High bandwidth 48

49 Thank you! 49

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