# Dynamic backup workers for parallel machine learning

arXiv: https://arxiv.org/abs/2004.14696 · HTML: https://mlsanity.com/paper/2004.14696/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

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

- [Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers](https://mlsanity.com/paper/2102.06280/) (2021)
- [Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates](https://mlsanity.com/paper/2306.06559/) (2023)

## Abstract (arXiv)

The most popular framework for distributed training of machine learning models is the (synchronous) parameter server (PS). This paradigm consists of n workers, which iteratively compute updates of the model parameters, and a stateful PS, which waits and aggregates all updates to generate a new estimate of model parameters and sends it back to the workers for a new iteration. Transient computation slowdowns or transmission delays can intolerably lengthen the time of each iteration. An efficient way to mitigate this problem is to let the PS wait only for the fastest n-b updates, before generating the new parameters. The slowest b workers are called backup workers. The optimal number b of backup workers depends on the cluster configuration and workload, but also (as we show in this paper) on the hyper-parameters of the learning algorithm and the current stage of the training. We propose DBW, an algorithm that dynamically decides the number of backup workers during the training process to maximize the convergence speed at each iteration. Our experiments show that DBW 1) removes the necessity to tune b by preliminary time-consuming experiments, and 2) makes the training up to a factor 3 faster than the optimal static configuration.