Folded Polynomial Codes for Coded Distributed AA^⊤-Type Matrix Multiplication

by   Jingke Xu, et al.

In this paper, due to the important value in practical applications, we consider the coded distributed matrix multiplication problem of computing AA^⊤ in a distributed computing system with N worker nodes and a master node, where the input matrices A and A^⊤ are partitioned into p-by-m and m-by-p blocks of equal-size sub-matrices respectively. For effective straggler mitigation, we propose a novel computation strategy, named folded polynomial code, which is obtained by modifying the entangled polynomial codes. Moreover, we characterize a lower bound on the optimal recovery threshold among all linear computation strategies when the underlying field is real number field, and our folded polynomial codes can achieve this bound in the case of m=1. Compared with all known computation strategies for coded distributed matrix multiplication, our folded polynomial codes outperform them in terms of recovery threshold, download cost and decoding complexity.


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