A New Coding Scheme for Matrix-Vector Multiplication via Universal Decodable Matrices
Hongru Cao, Wei Yan, Sian-Jheng Lin, Weiming Zhang · 2022 IEEE International Symposium on Information Theory (ISIT) · 2022
In this paper, we study the straggler mitigation via coded computing in distributed computations. In particular, we consider the coded matrix-vector multiplication where the matrix is sparse and coding may break the sparsity of the matrix. We construct a class of sparse universal decodable matrices (UDMs) for coded computing. In simulations, it shows that the proposed code possesses better sparsity than other schemes with randomly generated sparse matrices. Besides, the proposed code performs well in numerical stability.