GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

Abhishek Samlodia; Raghav G. Jha

GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA

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arxiv 2306.00358 v2 pith:4FVIE2IH submitted 2023-06-01 hep-lat physics.comp-ph

Raghav G. Jha , Abhishek Samlodia This is my paper · ORCID

classification hep-latphysics.comp-ph

keywords computationscudapytorchrenormalizationtensoracceleratedarchitecturebond

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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.

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Applying the Triad network representation to four-dimensional ATRG method
hep-lat 2024-12 conditional novelty 5.0 of 10

Triad-ATRG applies the triad and MDTRG decomposition to four-dimensional ATRG, reducing the contraction cost to O(r^2 χ^7) while reproducing ATRG free energies and transition temperatures.

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