Coded Sparse Matrix Multiplication


Sinong Wang, Jiashang Liu, Ness Shroff ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5152-5160, 2018.


In a large-scale and distributed matrix multiplication problem $C=A^{\intercal}B$, where $C\in\mathbb{R}^{r\times t}$, the coded computation plays an important role to effectively deal with “stragglers” (distributed computations that may get delayed due to few slow or faulty processors). However, existing coded schemes could destroy the significant sparsity that exists in large-scale machine learning problems, and could result in much higher computation overhead, i.e., $O(rt)$ decoding time. In this paper, we develop a new coded computation strategy, we call sparse code, which achieves near optimal recovery threshold, low computation overhead, and linear decoding time $O(nnz(C))$. We implement our scheme and demonstrate the advantage of the approach over both uncoded and current fastest coded strategies.

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