Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation
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.
Cite
@article{arxiv.2603.08957,
title = {Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation},
author = {Yuxin Tang and Zhiyuan Xin and Zhimin Ding and Xinyu Yao and Daniel Bourgeois and Tirthak Patel and Chris Jermaine},
journal= {arXiv preprint arXiv:2603.08957},
year = {2026}
}