Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation
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 ...
| Authors | Yuxin Tang, Zhiyuan Xin, Zhimin Ding et al. |
|---|---|
| Primary category | cs.MS |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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