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Distributed matrix computations over large clusters can suffer from theproblem of slow or failed worker nodes (called stragglers) which can dominatethe overall job execution time. Coded computation utilizes concepts fromerasure coding to mitigate the effect of stragglers by running 'coded' copiesof tasks comprising a job; stragglers are typically treated as erasures. Whilethis is useful, there are issues with applying, e.g., MDS codes in astraightforward manner. Several practical matrix computation scenarios involvesparse matrices. MDS codes typically require dense linear combinations ofsubmatrices of the original matrices which destroy their inherent sparsity.This is problematic as it results in significantly higher worker computationtimes. Moreover, treating slow nodes as erasures ignores the potentially usefulpartial computations performed by them. Furthermore, some MDS techniques alsosuffer from significant numerical stability issues. In this work we presentschemes that allow us to leverage partial computation by stragglers whileimposing constraints on the level of coding that is required in generating theencoded submatrices. This significantly reduces the worker computation time ascompared to previous approaches and results in improved numerical stability inthe decoding process. Exhaustive numerical experiments on Amazon Web Services(AWS) clusters support our findings.

Coded sparse matrix computation schemes that leverage partial stragglers