Abstract
We present a CUDA implementation for Kepler and Maxwell GPU generations of neuroimaging registration based on the NiftyReg open-source library [1]. A wide number of strategies are deployed to accelerate the code, providing insightful guidelines to exploit the massive parallelism and memory hierarchy within emerging GPUs. Our efforts are analyzed from different perspectives: Acceleration, numerical accuracy, power consumption and energy efficiency, to identify potential scenarios where performance per watt can be optimal in large-scale biomedical applications. Experimental results suggest that parallelism and arithmetic intensity represent the most rewarding ways on the road to high performance bioinformatics when power is a major concern.
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Acknowledgments
This work was supported by the Ministry of Education of Spain under Project TIN2013-42253-P and by the Junta de Andalucia under Project of Excellence P12-TIC-1741. We thank Javier Cabero and Pablo Sánchez for their work on preliminary versions of these CUDA implementations. We also thank Marc Modat from University College London, for his support when using the NiftyReg library. We also thank Nvidia for hardware donations within GPU Education Center 2011–2016 and GPU Research Center 2012–2016 awards.
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Álvarez, F.N., Cabrera, J.A., Chico, J.F., Pérez, J., Ujaldón, M. (2016). Neuroimaging Registration on GPU: Energy-Aware Acceleration. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_55
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DOI: https://doi.org/10.1007/978-3-319-31744-1_55
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