Presentation
Enabling Real-Time, Extreme-Scale Bayesian Inference: FFT-Based GPU-Accelerated Matrix-Vector Products for Block-Triangular Toeplitz Matrices
DescriptionAdjoint-based, matrix-free Newton-Krylov methods have long been the gold standard for solving high-dimensional, ill-posed inverse problems. These methods require a pair of forward and adjoint PDE solves per iteration, usually making them intractable for real-time inference and prediction. We present FFTMatvec, an FFT-based GPU-accelerated algorithm that exploits intrinsic problem structure to enable real-time, high-fidelity, extreme-scale inference and prediction for linear autonomous dynamical systems. This algorithm was used to solve a Bayesian inverse problem for tsunami early warning with over one billion parameters in under 0.2 seconds. The application is performance-portable and open-source; scaling results are presented for up to 4,096 GPUs on OLCF's Frontier and NERSC's Perlmutter supercomputers. On 512 GPUs, FFTMatvec achieves more than a 200,000x speedup over state-of-the-art matrix-free adjoint-based methods. Communication-aware partitioning and dynamic mixed precision provide additional performance boosts. Other application areas include nuclear treaty verification and monitoring atmospheric CO2.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
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