  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 \emph{sparse code}, which
achieves near \emph{optimal recovery threshold}, \emph{low computation
overhead}, and \emph{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.
