2046

Orders-of-magnitude performance increases in GPU-accelerated correlation of images from the International Space Station

Peter Lu, Hidekazu Oki, Catherine Frey, Gregory Chamitoff, Leroy Chiao, Edward Fincke, C. Foale, Sandra Magnus, William McArthur, Daniel Tani, Peggy Whitson, Jeffrey Williams, William Meyer, Ronald Sicker, Brion Au, Mark Christiansen, Andrew Schofield, David Weitz
Department of Physics and SEAS, Harvard University, Cambridge, MA, USA
Journal of Real-Time Image Processing, Volume 5, Number 3, 179-193

@article{luorders,

   title={Orders-of-magnitude performance increases in GPU-accelerated correlation of images from the International Space Station},

   author={Lu, P.J. and Oki, H. and Frey, C.A. and Chamitoff, G.E. and Chiao, L. and Fincke, E.M. and Foale, C.M. and Magnus, S.H. and McArthur, W.S. and Tani, D.M. and others},

   journal={Journal of Real-Time Image Processing},

   pages={1–15},

   issn={1861-8200},

   publisher={Springer}

}

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We implement image correlation, a fundamental component of many real-time imaging and tracking systems, on a graphics processing unit (GPU) using NVIDIA’s CUDA platform. We use our code to analyze images of liquid-gas phase separation in a model colloid-polymer system, photographed in the absence of gravity aboard the International Space Station (ISS). Our GPU code is 4,000 times faster than simple MATLAB code performing the same calculation on a central processing unit (CPU), 130 times faster than simple C code, and 30 times faster than optimized C++ code using single-instruction, multiple-data (SIMD) extensions. The speed increases from these parallel algorithms enable us to analyze images downlinked from the ISS in a rapid fashion and send feedback to astronauts on orbit while the experiments are still being run.
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