# NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion

arXiv: https://arxiv.org/abs/1312.0193 · HTML: https://mlsanity.com/paper/1312.0193/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2013.

## Built on by (most cited first, 2 of 2)

- [Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout](https://mlsanity.com/paper/2210.16105/) (2022)
- [GPU accelerated matrix factorization of large scale data using block based approach](https://mlsanity.com/paper/2304.13724/) (2023)

## Abstract (arXiv)

We develop an efficient parallel distributed algorithm for matrix completion, named NOMAD (Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion). NOMAD is a decentralized algorithm with non-blocking communication between processors. One of the key features of NOMAD is that the ownership of a variable is asynchronously transferred between processors in a decentralized fashion. As a consequence it is a lock-free parallel algorithm. In spite of being an asynchronous algorithm, the variable updates of NOMAD are serializable, that is, there is an equivalent update ordering in a serial implementation. NOMAD outperforms synchronous algorithms which require explicit bulk synchronization after every iteration: our extensive empirical evaluation shows that not only does our algorithm perform well in distributed setting on commodity hardware, but also outperforms state-of-the-art algorithms on a HPC cluster both in multi-core and distributed memory settings.