GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

TL;DR

This paper demonstrates how tensor renormalization group computations can be significantly accelerated using PyTorch and CUDA on GPUs, improving runtime and scalability for 2D systems, crucial for future high-precision calculations.

Contribution

The paper introduces GPU acceleration for TRG methods using PyTorch and CUDA, enhancing performance and scalability for tensor computations in physics.

Findings

Significant runtime improvements on GPUs

Enhanced scalability with bond dimension

GPU utilization is essential for future TRG precision

Abstract

We show that numerical computations based on tensor renormalization group (TRG) methods can be significantly accelerated with PyTorch on graphics processing units (GPUs) by leveraging NVIDIA's Compute Unified Device Architecture (CUDA). We find improvement in the runtime and its scaling with bond dimension for two-dimensional systems. Our results establish that the utilization of GPU resources is essential for future precision computations with TRG.