Hyperspectral video using CUDAon-demand.gputechconf.com/gtc/2013/presentations/S3030...Title...
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Hyperspectral video using CUDA Robert Dunn, PhD Candidate
Transcript of Hyperspectral video using CUDAon-demand.gputechconf.com/gtc/2013/presentations/S3030...Title...
Hyperspectral video using CUDA Robert Dunn, PhD Candidate
Outline Hyperspectral Imaging
Our Research
Problems
Results
Future Work
Imaging Spectroscopy
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Linear Mixing Model
wawsaxM
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Endmembers
Abundances
Pixel Spectrum
Dimension Reduction Principal Components
Analysis
1. Covariance Matrix
2. Eigendecomposition
3. Transformation
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Data
Simplex 1
Simplex 2
Simplex 3
Endmember Determination
Determinant
LU Decomposition
Semi-Parallelisable Search
Abundance Mapping
2minarg xaa
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Current Research Real-time terrestrial HSI
◦ Forensics
◦ Manufacturing
◦ Military
CUDA Simulations
◦ Visualisation Problems
◦ Algorithm Optimisation
Problem - syrk
2.50E-05
2.70E-05
2.90E-05
3.10E-05
3.30E-05
3.50E-05
3.70E-05
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cublasSsyrk Error
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cublasSsyrk Utilisation
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Syrk performance with ‘fat matrix’
p=200,000
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Problem - syrk
0.00E+00
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2.00E-08
3.00E-08
4.00E-08
5.00E-08
6.00E-08
7.00E-08
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1.00E-07
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Sub-Block Algorithm Error
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Sub-Block vs cublasSsyrk
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p=200,000
GPU inefficient for small matrices.
Host-Device transfers inefficient.
◦ Synchronisation
Batch processing not suitable.
Problem – Small matrix
Problem – MAGMA/CULA Advanced Linear Algebra ◦ EVD, LU, QR etc
Hybrid CPU/GPU ◦ Designed for large matrix
Streaming and Synchronisation
‘Assembling Blocks’ vs ‘Complete Kernel’
Problem - Streaming
CPU
GPU
CPU
GPU
Problem - Overheads CPU
Single Frame
Problem – Testing CUDA + C MATLAB
Fast Performance Poor Performance
Unproven Code Proven Code
Developer Intensive Simple Development
CUDA HSI
MEX
MATLAB
Monitoring
Thread
Named
Pipe
Experimental Setup Nvidia Tesla C2070 ‘fermi’
Intel Xeon W3520 @ 2.67Ghz 12GB System RAM
Simulated Data (Moffet Field)
512x512x190 Frame
Results
Future CUDA Optimisations Kepler Architecture (GK110) ◦ Dynamic Parallelism
◦ Hyper-Q
“Complete Kernels”
Portable Platforms / Tegra
Streaming Variants (EVD, QR, LS, etc)
Conclusion Working ‘real-time’ HSI video chain
Further optimisation required
Visualisation challenges
GK110 will help
