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Title GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA
Authors Raghav Jha, Abhishek Samlodia
JLAB number JLAB-THY-23-3903
LANL number arXiv:2306.00358
Other number DOE/OR/23177-7075
Document Type(s) (Journal Article) 
Associated with EIC: No
Supported by Jefferson Lab LDRD Funding: No
Funding Source: Nuclear Physics (NP)

Compiled for Computer Physics Communication
Volume 294
Page(s) 108941
Publication 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.
Experiment Numbers:
Document: pdf
Accepted Manuscript: 2306.00358.pdf
Supporting Documents:
Supporting Datasets: