Efficient tensor network contraction with GPU acceleration and parallel strategies

Wei Xu; Weiqin Tong; Xiaoyang Liu

doi:10.1117/12.3031095

19 July 2024 Efficient tensor network contraction with GPU acceleration and parallel strategies

Wei Xu, Weiqin Tong, Xiaoyang Liu

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 1318177 (2024) https://doi.org/10.1117/12.3031095
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Tensor network contraction is a fundamental operation for dealing with high-dimensional data, which is now widely used in classical simulations of quantum circuits. To improve the performance of simulations, existing methods are implemented by optimizing contraction methods. However, with the increasing size of the tensor network, the contraction efficiency of these methods is difficult to satisfy the performance requirements and also suffers from the challenge of contracting tensor tasks that exceed GPU memory. In this paper, we propose a high-performance chunking method for tensor contraction. By chunking the given tensor, we obtain small tensors or vectors assign the corresponding threads, and contract them in parallel through the GPU. Solving high-order tensor contractions using distributed tensor contraction methods. Compared to the Einsum, our method achieves an average speedup of 22.28×. We also have experiments for large-scale tensor network contractions and our method achieves an average speedup of 13.56× compared to Tensordot.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Xu, Weiqin Tong, and Xiaoyang Liu "Efficient tensor network contraction with GPU acceleration and parallel strategies", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 1318177 (19 July 2024); https://doi.org/10.1117/12.3031095

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KEYWORDS

Simulations

Quantum circuits

Quantum processes

Distributed computing

Matrices

Quantum operations

Quantum speedup

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