GPU-acceleration of tensor renormalization with PyTorch using CUDA

被引：5

作者：

Jha, Raghav G. ^{[1
]}

Samlodia, Abhishek ^{[2
]}

机构：

[1] Thomas Jefferson Natl Accelerator Facil, Newport News, VA 23606 USA

[2] Syracuse Univ, Dept Phys, Syracuse, NY 13244 USA

来源：

COMPUTER PHYSICS COMMUNICATIONS | 2024年 / 294卷

关键词：

Tensor; Lattice field theory; Spin models; CUDA; !text type='Python']Python[!/text; Speedup;

D O I：

10.1016/j.cpc.2023.108941

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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 (for a given accuracy) and its scaling with bond dimension for two-dimensional systems. Our results establish that utilization of GPU resources is essential for future precision computations with TRG. Published by Elsevier B.V.

引用

页数：8

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