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- W2155157029 abstract "We present an efficient method for mutual information (MI) computation between images (2D or 3D) for NVIDIA's `compute unified device architecture' (CUDA) compatible devices. Efficient parallelization of MI is particularly challenging on a `graphics processor unit' (GPU) due to the need for histogram-based calculation of joint and marginal probability mass functions (pmfs) with large number of bins. The data-dependent (unpredictable) nature of the updates to the histogram, together with hardware limitations of the GPU (lack of synchronization primitives and limited memory caching mechanisms) can make GPU-based computation inefficient. To overcome these limitation, we approximate the pmfs, using a down-sampled version of the joint- histogram which avoids memory update problems. Our CUDA implementation improves the efficiency of MI calculations by a factor of 25 compared to a standard CPU- based implementation and can be used in MI-based image registration applications." @default.
- W2155157029 created "2016-06-24" @default.
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- W2155157029 date "2007-12-01" @default.
- W2155157029 modified "2023-09-23" @default.
- W2155157029 title "Speeding up Mutual Information Computation Using NVIDIA CUDA Hardware" @default.
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- W2155157029 doi "https://doi.org/10.1109/dicta.2007.4426846" @default.
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