# Multi-GPU Training of ConvNets

arXiv: https://arxiv.org/abs/1312.5853 · HTML: https://mlsanity.com/paper/1312.5853/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2013.

## Built on by (most cited first, 6 of 6)

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [Communication-efficient SGD: From Local SGD to One-Shot Averaging](https://mlsanity.com/paper/2106.04759/) (2021)
- [PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks](https://mlsanity.com/paper/2210.09147/) (2022)
- [Differentiable programming: Generalization, characterization and limitations of deep learning](https://mlsanity.com/paper/2205.06898/) (2022)
- [Exploring shared memory architectures for end-to-end gigapixel deep learning](https://mlsanity.com/paper/2304.12149/) (2023)
- [A Survey From Distributed Machine Learning to Distributed Deep Learning](https://mlsanity.com/paper/2307.05232/) (2023)

## Abstract (arXiv)

In this work we evaluate different approaches to parallelize computation of convolutional neural networks across several GPUs.