improving mars rover image compression via...
TRANSCRIPT
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Improving Mars Rover Image Compression Via GPUs and Genetic Algorithms – S0133 GPU Technology Conference 2012
Brendan J. Babb & Frank Moore University of Alaska, Anchorage Shawn Aldridge, USC, CA Michael Peterson, U of Hilo, HI
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Overview
Mars Rover Images
Image Compression using Wavelets
Genetic Algorithms
AccelerEyes' Jacket with Matlab
Improvements
Results
Future work
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Death Star Blueprints
Noticed possible vulnerability with the
exhaust port
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Mars Images
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Wavelet Image Compression
Forward
Wavelet
Transform
Inverse
Wavelet
Transform
Quantizer
Dequantizer
Encoder
Decoder
Decompressor 10011…
Original Image
Lossy Image
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Multiresolution Analysis
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Mars Rover Challenges
No on-site tech support
35 MIPS 33 MHZ IBM RAD 6000 Processor
128 MB of memory
Noisy environment requires Error Detection and Correction (EDC)
12,000 bits/sec modem
Need low complexity, EDC and compression
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Genetic Algorithms
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Genetic Algorithms
Solutions are made up of 50 circles
X-position
Y-position
Radius of circle
Color of circle
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Genetic Algorithms
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Genetic Algorithms
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Magnified Region
Original – Wavelet – Evolved
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High Level vs. Low Level Language
Program was originally in C++ and took 3 days to do a run
Ported to Matlab by a collaborator
Many people can write better CUDA and underlying functions than I can
Working with students and professors who are already using Matlab
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Matlab GA
GA are embarrassingly parallel
Biggest cost is usually the fitness calculation
Previously ran on a supercomputer at Fairbanks, AK using Matlab in parallel
GPU is a natural fit for GA and GPUs
Accelereye's Jacket
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Acclereyes' Jacket
Easy to change code – double to gdouble
GFORs have lots of restrictions but can improve speed
CONV2 used a lot in Wavelet DWT and accelerated in Jacket
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Techniques
GFOR can't have nested loops or IF statements
Rewrite Wavelet Toolbox code to remove loops and IF statements
Many IF statements for special cases of wavelets – can be ignored
2-5 times speed up
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Memory Considerations
We are constrained by the memory on the GPU
Memory transfers are a big hit
The GFOR allows us to tile the problem on the GPU and on the Tesla and use up to 100 fitness calculations on a 512 x 512 image
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Speed up versus # of images
Jacket 2.1 GTX 560 TI vs. Intel Quad Core Q95550 2.83Ghz
512x512
256x256
1024x1024
Number of images
Spe
ed u
p
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Future Work
Test on Multi GPU Tesla system
Accelerate ICER scheme which uses a modified lifting format
Island GA model where the DWT and GA is done completely on the GPU
Extending to cloud based GPUs through Amazon Web Services
Fairbanks is getting Fish (a GPU supercomputer)
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Conclusions
GPUs can be used to accelerate wavelet based image compression
Jacket allows high level code changes in Matlab
Allows for faster fitness calculations and larger GA populations
Wavelets can be evolved that improve the image quality of reconstructed Mars Rover images
Archived data from Rovers can be used to improve images and increase science content
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Thanks
AccelerEyes - John, James, Scott
JPL – especially Aaron B Kiely and Matt
Frank Moore and Kenrick Mock at UAA
Link to Genetic Algorithm art: http://www.cosc.brocku.ca/~bross/JNetic/
NASA antenna link: http://scienceblogs.com/goodmath/2008/11/evolution_produces_better_ante.php
ARSC Supercomputing at University of Alaska, Fairbanks
NASA EPSCoR