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GPU-Accelerated Dense Covariance Matrix Generation for Spatial Statistics Applications

Authors: Zipei Geng, Sameh Abdulah, Hatem Ltaief, Ying Sun, Marc Genton, and David Keyes (King Abdullah University of Science and Technology (KAUST))

Abstract: Large-scale parallel computing is crucial in Gaussian regressions to reduce the complexity of spatial statistics applications. The log-likelihood function is utilized to evaluate the Gaussian model for a set of measurements in N geographical locations. Several studies have shown a utilization of modern hardware to scale the log-likelihood function for handling large numbers of locations. ExaGeoStat is an example of software that allows parallel statistical parameter estimation from the log-likelihood function. However, generating a covariance matrix is mandatory and challenging when estimating the log-likelihood function. In ExaGeoStat, the generation process was performed on CPU hardware due to missing math functions in CUDA libraries, e.g., the modified Bessel function of the second kind. This study aims to optimize the generation process using GPU with two proposed generation schemes: pure GPU and hybrid. Our implementations demonstrate up to 6X speedup with pure GPU and up to 1.5X speedup with the hybrid scheme.

Best Poster Finalist (BP): no

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