Right Precision, Right Place: Rethinking HPC Applications with Adaptive Mixed Precision

The future of large-scale simulations is increasingly tied to hardware features originally designed for AI workloads—especially low-precision arithmetic. Modern GPUs embody this shift, deliver- ing substantial speedups through reduced-precision computations that lower execution time, shrink memory footprints, and cut energy consumption. Building on these capabilities, we design fast mixed-precision linear algebra algorithms that adaptively choose the right precision at the right moment. Beyond computation, we extend this adaptivity to data storage, enabling mixed-precision representations that dynamically adjust to the needs of memory-bound applications. This approach significantly reduces data movement—now a dominant performance bottleneck—while preserving high accuracy only where it truly matters. Our dynamic precision-conversion strategy maintains application-level numerical reliability while improving overall efficiency. This talk will demonstrate how these techniques reshape computational performance for geospatial statisticians and geophysi- cists, with far-reaching benefits for environmental computational statistics, seismic imaging, and beyond.