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arXiv · 2608.24307

An HPC Approach to Accelerate Tensor Decompositions

Markus Hellgren·Erna Begovic Kovac·Hans O. Karlsson·Roman Iakymchuk
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Abstract

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.

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Markus Hellgren, Erna Begovic Kovac, Hans O. Karlsson, Roman Iakymchuk. 2026-08-25. An HPC Approach to Accelerate Tensor Decompositions. https://arxiv.org/abs/2608.24307

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