  Coded computation is an emerging research area that leverages concepts from
erasure coding to mitigate the effect of stragglers (slow nodes) in distributed
computation clusters, especially for matrix computation problems. In this work,
we present a class of distributed matrix-vector multiplication schemes that are
based on codes in the Rosenbloom-Tsfasman metric and universally decodable
matrices. Our schemes take into account the inherent computation order within a
worker node. In particular, they allow us to effectively leverage partial
computations performed by stragglers (a feature that many prior works lack). An
additional main contribution of our work is a companion matrix-based embedding
of these codes that allows us to obtain sparse and numerically stable schemes
for the problem at hand. Experimental results confirm the effectiveness of our
techniques.
