Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a low-complexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings.
This is a pre-print of the article Das, Anindya Bijoy and Aditya Ramamoorthy. "Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach." arXiv preprint arXiv:1901.08716 (2019). Posted with permission.