Multi- and Mixed-Precision Computations for Spatial and Spatio-Temporal Statistics

Dr. Mary Lai Salvaña

Department of Statistics, University of Connecticut, USA

Computational statistics has traditionally utilized double-precision (64-bit) data structures and full-precision operations, resulting in higher-than-necessary accuracy for certain applications. Recently, there has been a growing interest in exploring low-precision options that could reduce computational complexity while still achieving the required level of accuracy. This trend has been amplified by new hardware such as NVIDIA's Tensor Cores in their V100, A100, and H100 GPUs, which are optimized for mixed-precision computations, Intel CPUs with Deep Learning (DL) boost, Google Tensor Processing Units (TPUs), Field Programmable Gate Arrays (FPGAs), ARM CPUs, and others. However, using lower precision may introduce numerical instabilities and accuracy issues. Nevertheless, some applications have shown robustness to low-precision computations, leading to new multi- and mixed-precision algorithms that balance accuracy and computational cost. To address this need, we introduce MPCR, a novel R package that supports three different precision types (16-, 32-, and 64-bit) and their combinations, along with its usage in commonly-used Frequentist/Bayesian statistical examples. The MPCR package is written in C++ and integrated into R through the Rcpp package, enabling highly optimized operations in various precisions. Moreover, we show how to leverage low precision computations for spatial and spatio-temporal statistics.