Matrix-matrix multiplication and its derivatives are fundamental linear-algebraic primitives at the core of many modern optimization and machine learning algorithms. We design a new and novel framework for speeding up large-scale matrix-matrix multiplication when the output matrix is known to be sparse, as is true in many applications of interest. Our solution is based on a novel use of product codes which have been studied in the communications literature. In particular, when multiplying two matrices of sizes n × d and d n where the output matrix is (exactly) K-sparse with support× uniformly distributed, our algorithm requires max(O(dK), O(dn)) computations. We also extend our framework to handle the approximately-sparse setting where the output matrix has K-entries that are significantly larger than the rest. In this case, the computational complexity is max(O(dK log2(n)), O(dn log2(n))). We corroborate our findings with numerical simulations that validate our claims.
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