# Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach

arXiv: https://arxiv.org/abs/1901.08716 · HTML: https://mlsanity.com/paper/1901.08716/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier](https://mlsanity.com/paper/2101.11653/) (2021)
- [Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent](https://mlsanity.com/paper/2201.12990/) (2022)
- [Random Alloy Codes and the Fundamental Limits of Coded Distributed Tensors](https://mlsanity.com/paper/2202.03469/) (2022)

## Abstract (arXiv)

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.