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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

Computation of Mutual Information Metric for Image Registration on Multiple GPUs