SC17 Denver, CO

P02: Strassen's Algorithm for Tensor Contraction


Authors: Jianyu Huang (University of Texas), Devin A. Matthews (University of Texas), Robert A. van de Geijn (University of Texas)

Abstract: Tensor contraction(TC) is an important computational kernel widely used in numerous applications. It is a multi-dimensional generalization of matrix multiplication(GEMM). While Strassen's algorithm for GEMM is well studied in theory and practice, extending it to accelerate TC has not been previously pursued. Thus, we believe this to be the first work to demonstrate how one can in practice speed up tensor contraction with Strassen's algorithm. By adopting a Block-Scatter-Matrix format, a novel matrix-centric tensor layout, we can conceptually view TC as GEMM for general stride storage, with an implicit tensor-to-matrix transformation. This insight enables us to tailor a recent state-of-the-art implementation of Strassen's algorithm to TC, avoiding explicit transpositions(permutations) and extra workspace, and reducing the overhead of memory movement that is incurred. Performance benefits are demonstrated with a performance model as well as in practice on modern single core, multicore, and distributed memory parallel architectures, achieving up to 1.3× speedup.
Award: Best Poster Finalist (BP): no

Poster: pdf
Two-page extended abstract: pdf


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