An HPC Approach to Accelerate Tensor Decompositions
Uppsala University, Department of Information Technology, Sweden
arXiv:2608.24307 [cs.DC], (25 Aug 2026)
@misc{hellgren2026an,
title={An HPC Approach to Accelerate Tensor Decompositions},
author={Markus Hellgren and Erna Begovic Kovac and Hans O. Karlsson and Roman Iakymchuk},
year={2026},
eprint={2608.24307},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2608.24307}
}
Quantum systems grow in complexity so rapidly that even modest models become difficult to simulate, creating a strong need for methods that can handle high-dimensional data, also known as tensors. In this work, we investigate a novel Jacobi-type tensor algorithm for tensor decomposition and develop a CUDA-based algorithm that supports tensors of arbitrary order on a single GPU. We test the implementation on NVIDIA H100 GPUs and show that the algorithm converges correctly for diagonalizable tensors up to nine dimensions, with runtime scaling in a predictable way as tensor order grows. Finally, our general algorithm outperforms the original MATLAB reference by more than two orders of magnitude.
August 30, 2026 by hgpu
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