Communication Lower Bounds for Nested Bilinear Algorithms via Rank Expansion of Kronecker Products

Caleb Ju, Yifan Zhang, Edgar Solomonik

Research output: Contribution to journalArticlepeer-review


We develop lower bounds on communication in the memory hierarchy or between processors for nested bilinear algorithms, such as Strassen’s algorithm for matrix multiplication. We build on a previous framework that establishes communication lower bounds by use of the rank expansion, or the minimum rank of any fixed size subset of columns of a matrix, for each of the three matrices encoding a bilinear algorithm. This framework provides lower bounds for a class of dependency directed acyclic graphs (DAGs) corresponding to the execution of a given bilinear algorithm, in contrast to other approaches that yield bounds for specific DAGs. However, our lower bounds only apply to executions that do not compute the same DAG node multiple times. Two bilinear algorithms can be nested by taking Kronecker products between their encoding matrices. Our main result is a lower bound on the rank expansion of a matrix constructed by a Kronecker product derived from lower bounds on the rank expansion of the Kronecker product’s operands. We apply the rank expansion lower bounds to obtain novel communication lower bounds for nested Toom-Cook convolution, Strassen’s algorithm, and fast algorithms for contraction of partially symmetric tensors.

Original languageEnglish (US)
JournalFoundations of Computational Mathematics
StateAccepted/In press - 2023
Externally publishedYes


  • Bilinear algorithm
  • Communication lower bounds
  • Convolution
  • Kronecker product
  • Rank expansion
  • Strassen’s algorithm
  • Tensor contraction

ASJC Scopus subject areas

  • Analysis
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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