STI Publications - View Publication Form #19379
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Publication Information
| Title | GPU-Acceleration of Tensor Renormalization with PyTorch using CUDA | ||||
| 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. | ||||
| Author(s) | Raghav Jha, Abhishek Samlodia | ||||
| Publication Date | January 2024 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Theory & Comp Physics / THEORY CENTER / THEORY CENTER | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | No | ||||
| Supported by Jefferson Lab LDRD Funding | No |
Journal Article
| Journal Name | Computer Physics Communication |
| Refereed | No |
| Volume | 294 |
| Issue | |
| Page(s) | 108941 |
Attachments/Datasets/DOI Link
| Document(s) |
2306.00358.pdf
(STI Document)
2306.00358.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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