Publications
Publication Information
| 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) |
|
Journal 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: | |
| Group: | THEORY CENTER |
| Document: | |
| DOI: | https://doi.org/10.1016/j.cpc.2023.108941 |
| Accepted Manuscript: | 2306.00358.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |