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Graphics Supercomputing Applied to Brain Image Analysis with NiftyReg

Francisco Nurudin Alvarez Gonzalez
Universidad de Malaga, Escuela Tecnica Superior de Ingenieria Informatica
Universidad de Malaga, 2015

@article{gonzalez2016supercomputacion,

   title={Supercomputaci{‘o}n gr{‘a}fica aplicada al an{‘a}lisis de im{‘a}genes cerebrales con niftyreg},

   author={Gonz{‘a}lez, {‘A}lvarez and Nurud{‘i}n, Francisco},

   year={2016}

}

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Medical image processing in general and brain image processing in particular are computationally intensive tasks. Luckily, their use can be liberalized by means of techniques such as GPU programming. In this article we study NiftyReg, a brain image processing library with a GPU implementation using CUDA, and analyse different possible ways of further optimising the existing codes. We will focus on fully using the memory hierarchy and on exploiting the computational power of the CPU. The ideas that lead us towards the different attempts to change and optimize the code will be shown as hypotheses, which we will then test empirically using the results obtained from running the application. Finally, for each set of related optimizations we will study the validity of the obtained results in terms of both performance and the accuracy of the resulting images.
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