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arXiv · 2603.08957

Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation

Yuxin Tang·Zhiyuan Xin·Zhimin Ding·Xinyu Yao·Daniel Bourgeois·Tirthak Patel·Chris Jermaine
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Abstract

A \emph{tensor-relational} computation is a relational computation where individual tuples carry vectors, matrices, or higher-dimensional arrays. An advantage of tensor-relational computation is that the overall computation can be executed on top of a relational system, inheriting the system's ability to automatically handle very large inputs with high levels of sparsity while high-performance kernels (such as optimized matrix-matrix multiplication codes) can be used to perform most of the underlying mathematical operations. In this paper, we introduce upper-case-lower-case \texttt{EinSum}, which is a tensor-relational version of the classical Einstein Summation Notation. We study how to automatically rewrite a computation in Einstein Notation into upper-case-lower-case \texttt{EinSum} so that computationally intensive components are executed using efficient numerical kernels, while sparsity is managed relationally.

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Yuxin Tang, Zhiyuan Xin, Zhimin Ding, Xinyu Yao, Daniel Bourgeois, Tirthak Patel, Chris Jermaine. 2026-03-09. Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation. https://arxiv.org/abs/2603.08957

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