The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: 1st Workshop on Enabling Predictive Science with Optimization and Uncertainty Quantification in HPC
Authors: Jienan Yao and Wei Xue (Tsinghua University, China)
Abstract: The rapid development in machine learning (ML) has prompted demand for low-precision arithmetic hardware that can deliver faster computing speed. Weather simulation applications typically exhibit higher sensitivity towards small perturbation on the input data, but the inherent uncertainty paves the way for opportunities in mixed-precision computing (MPC) by trading accuracy for performance. Additional challenges of balancing between the lower computational cost and accuracy requirements need to be addressed before successful MPC can be applied to weather modeling applications. Determining an acceptable precision allocation for variables involves interacting with an exponential search space of mixed-precision configurations. We propose a mixed-precision code tuning framework for automatic search of suitable precision configurations for weather modeling applications with black-box optimization algorithms. The search results achieve up to 30% performance gain that stays within the tolerance level, offering a workflow to facilitate the identification of variables sensitive to precision change.