Work

PyPOD-GP: Using PyTorch for accelerated chip-level thermal simulation of the GPU

Year: 2025

Type: software-paper

Abstract: The rising demand for high-performance computing (HPC) has made full-chip dynamic thermal simulation in many-core GPUs critical for optimizing performance and extending device lifespans. Proper orthogonal decomposition (POD) with Galerkin projection (GP) has shown to offer high accuracy and massive runtime improvements over direct numerical simulation (DNS). However, previous implementations of POD-GP use MPI-based libraries like PETSc and FEniCS and face significant runtime bottlenecks. We propose a Py Torch-based POD-GP library (PyPOD-GP), a GPU-optimized library for chip-level thermal simulation. PyPOD-GP achieves over 23 . 4 × speedup in training and over 10 × speedup in inference on a GPU with over 13,000 cores, with just 1.2% error over the device layer.

Source: SoftwareX

DOI: 10.1016/j.softx.2025.102147

Authors: Neil He, Ming-Cheng Cheng, Yu Liu

Institutions: Yale University, Clarkson University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: National Science Foundation