Efficient Statistical Computing with Mixed-Precision Power and GPUs Acceleration

Music City Center 

Mixed-precision computing optimizes large-scale computing by dynamically adjusting precision levels, reducing memory usage, computational time, and energy consumption without sacrificing accuracy. The NVIDIA Blackwell GB200 Superchip demonstrates this with FP16 achieving a 27.78x speedup over FP32 and 55.55x over FP64. This approach is increasingly vital as data sizes grow and computational demands escalate. Additionally, mixed precision enhances parallel computing efficiency, enabling faster processing in high-performance computing environments.

Statistical computing benefits by using lower precision for routine tasks and higher precision for critical operations, enhancing efficiency in large-scale models. This talk covers two applications-spatial data modeling and climate model emulation-showcasing mixed-precision performance. We will also introduce two R packages, MPCR (mixed/multi-precision computing) and TLAR (tile-based linear algebra), leveraging mixed precision for greater computational efficiency in R.

Mixed-precision computing

High-performance computing (HPC)

Parallel computing 

Section on Statistical Computing