Efficient Implementation of Bayesian Hierarchical Model to Study Space Time Variability of Latent Heat Flux

This paper describes an efficient implementation of Bayesian hierarchical model for space time variability of latent heat flux. Model parameters are estimated using Gibbs sampler. Many computations in the Gibbs sampling scheme are time intensive. Some time intensive computations in the model are matrix operations such as matrix multiplication, matrix inverse, matrix LU decomposition. We have used graphical processing unit (GPU) to run time intensive matrix operations. Some time intensive operations are transformed to matrix operations to take advantage of GPU. To run the model in GPU model is implemented in Compute Unified Device Architecture (CUDA). Two GPUs are used to run the model one is NVIDIA Graphics card Tesla C2075 with 448 cores and other is NVIDIA Graphics card GT 520 with 48 cores. To study the gain in speedup on the GPU we have implemented the model using single threaded C to run on CPU. A comparative study of implementation on CPU and the two GPUs is carried out for 100 iterations of the Gibbs sampler. We found 30-130 fold speedup on Tesla GPU as compared to single threaded CPU code.

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