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.KeywordsMutual InformationImage RegistrationSystem MemoryDevice MemoryMoving ImageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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