Abstract
Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires \mathcal{O}(n^{2}) storage and \mathcal{O}(n^{3}) operations, which can be a huge task considering that the number of geographical locations, n , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey {g-} and -h (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagonal Low-Rank (HODLR) matrix approximations. We assess the performance and accuracy of the proposed implementations using synthetic datasets up to 800K and a 300K precipitation data of Germany to demonstrate the advantage of using non-Gaussian over Gaussian random fields. Moreover, by relying on TLR/HODLR matrix computations, we can now solve for larger matrix sizes while preserving the required accuracy for prediction. We show the performance superiority of TLR over HODLR matrix computations when calculating the TGH likelihoods and predictions. Our TLR-based approximation shows a speedup up to 7.29X and 2.96X on shared-memory and distributed-memory systems, respectively, compared to the exact implementation.