Locally Random Alloy Codes with Channel Coding Theorems for Distributed Matrix Multiplication. (arXiv:2202.03469v5 [cs.IT] UPDATED)

Matrix multiplication is a fundamental operation in machine learning and is
commonly distributed into multiple parallel tasks for large datasets.
Stragglers and other failures can severely impact the overall completion time.
Recent works in coded computing provide a novel strategy to mitigate stragglers
with coded tasks, with an objective of minimizing the number of tasks needed to
recover the overall result, known as the recovery threshold. However, we
demonstrate that this combinatorial definition does not directly optimize the
probability of …

arxiv coding distributed matrix matrix multiplication random