# Slow and Stale Gradients Can Win the Race

arXiv: https://arxiv.org/abs/2003.10579 · HTML: https://mlsanity.com/paper/2003.10579/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

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

- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training](https://mlsanity.com/paper/2406.02613/) (2024)
- [Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent](https://mlsanity.com/paper/2201.12990/) (2022)

## Abstract (arXiv)

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in runtime as it waits for the slowest workers (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect the convergence error. In this work, we present a novel theoretical characterization of the speedup offered by asynchronous methods by analyzing the trade-off between the error in the trained model and the actual training runtime(wallclock time). The main novelty in our work is that our runtime analysis considers random straggling delays, which helps us design and compare distributed SGD algorithms that strike a balance between straggling and staleness. We also provide a new error convergence analysis of asynchronous SGD variants without bounded or exponential delay assumptions. Finally, based on our theoretical characterization of the error-runtime trade-off, we propose a method of gradually varying synchronicity in distributed SGD and demonstrate its performance on CIFAR10 dataset.