In prior work, Gupta et al. (SPAA 2022) presented a distributed algorithm for multiplying sparse $n\times n$ matrices, using $n$ computers. They assumed that the input matrices are uniformly sparse—there are at most $d$ non-zeros in each row and column—and the task is to compute a uniformly sparse part of the product matrix. The sparsity structure is globally known in advance (this is the supported setting). As input, each computer receives one row of each input matrix, and each computer needs to output one row of the product matrix. In each communication round each computer can send and receive one $O(\log n)$-bit message. Their algorithm solves this task in $O(d^{1.907})$ rounds, while the trivial bound is $O(d^2)$.

We improve on the prior work in two dimensions: First, we show that we can solve the same task faster, in only $O(d^{1.832})$ rounds. Second, we explore what happens when matrices are not uniformly sparse. We consider the following alternative notions of sparsity: row-sparse matrices (at most $d$ non-zeros per row), column-sparse matrices, matrices with bounded degeneracy (we can recursively delete a row or column with at most $d$ non-zeros), average-sparse matrices (at most $dn$ non-zeros in total), and general matrices.

We present a near-complete classification of the complexity of matrix multiplication for all combinations of these notions of sparsity. We show that almost all cases fall in one of these classes:

1. We have an upper bound of $O(d^{1.832})$ rounds for computing $X=AB$. An example is the case where $X$ and $A$ are uniformly sparse but $B$ is average-sparse; this is a generalization of the prior work beyond the uniformly-sparse case.
2. We have a lower bound of $\mathrm{\Omega }(\log n)$ and an upper bound of $O(d^2+\log n)$ rounds for computing $X=AB$. An example is the case where $X$, $A$, and $B$ have bounded degeneracy.
3. We have a lower bound of $n^{\mathrm{\Omega }(1)}$ rounds for computing $X=AB$. An example is the case where $X$ and $A$ have bounded degeneracy but $B$ is a general matrix.
4. We have a conditional lower bound: a fast algorithm for computing $X=AB$ would imply major improvements in dense matrix multiplication. An example is the case where $X$, $A$, and $B$ are average-sparse matrices.

Our work highlights the role that bounded degeneracy has in the context of distributed matrix multiplication: it is a natural class of sparse matrices that is restrictive enough to admit very fast algorithms—much faster than what we can expect for the average-sparse case.