Abstract
Because of their computational power, GPUs are widely used in the field of image processing. Registration of brain images has already been successfully accelerated with GPUs, but registration of high-resolution human brain images presents new challenges due to large amounts of data and images not fitting in the memory of a single device.
In this paper, we address this issue with two approaches. The first approach replicates image data in system memory of each node and distributes only a part of the data over multiple GPUs. The second approach splits image data between multiple GPUs, and overlaps computation and communication to hide latency. For both approaches, we present a performance analysis and comparison.
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Adinetz, A., Kraus, J., Axer, M., Huysegoms, M., Köhnen, S., Pleiter, D. (2014). Computation of Mutual Information Metric for Image Registration on Multiple GPUs. In: an Mey, D., et al. Euro-Par 2013: Parallel Processing Workshops. Euro-Par 2013. Lecture Notes in Computer Science, vol 8374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54420-0_21
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DOI: https://doi.org/10.1007/978-3-642-54420-0_21
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