P30 - High Performance Computing Meets Approximate Bayesian Inference
DescriptionDespite the ongoing advancements in Bayesian computing, large-scale inference tasks continue to pose a computational challenge that often requires a trade-off between accuracy and computation time. Combining solution strategies from the field of high-performance computing with state-of-the-art statistical learning techniques, we present a highly scalable approach for performing spatial-temporal Bayesian modelling based on the methodology of integrated nested Laplace approximations (INLA). The spatial-temporal model component is reformulated as the solution to a discretized stochastic partial differential equation which induces sparse matrix representations for increased computational efficiency. We leverage the power of today’s distributed compute architectures by introducing a multi-level parallelism scheme throughout the algorithm. Moreover, we rethink the computational kernel operations and derive GPU-accelerated linear algebra solvers for fast and reliable model predictions.
TimeTuesday, June 2710:00 - 10:01 CEST