# CGX: Adaptive System Support for Communication-Efficient Deep Learning

arXiv: https://arxiv.org/abs/2111.08617 · HTML: https://mlsanity.com/paper/2111.08617/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Error Feedback Fixes SignSGD and other Gradient Compression Schemes](https://mlsanity.com/paper/1901.09847/) (2019)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning](https://mlsanity.com/paper/1705.07878/) (2017)
- [PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization](https://mlsanity.com/paper/1905.13727/) (2019)
- [ATOMO: Communication-efficient Learning via Atomic Sparsification](https://mlsanity.com/paper/1806.04090/) (2018)
- [Adaptive Gradient Quantization for Data-Parallel SGD](https://mlsanity.com/paper/2010.12460/) (2020)
- [Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect](https://mlsanity.com/paper/1903.04611/) (2019)
- [SparCML: High-Performance Sparse Communication for Machine Learning](https://mlsanity.com/paper/1802.08021/) (2018)
- [AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training](https://mlsanity.com/paper/1712.02679/) (2017)
- [On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning](https://mlsanity.com/paper/1911.08250/) (2019)
- [An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems](https://mlsanity.com/paper/2101.10761/) (2021)
- [On the Utility of Gradient Compression in Distributed Training Systems](https://mlsanity.com/paper/2103.00543/) (2021)
- [Priority-based Parameter Propagation for Distributed DNN Training](https://mlsanity.com/paper/1905.03960/) (2019)
- [NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization](https://mlsanity.com/paper/1908.06077/) (2019)
- [3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning](https://mlsanity.com/paper/1802.07389/) (2018)
- [BAGUA: Scaling up Distributed Learning with System Relaxations](https://mlsanity.com/paper/2107.01499/) (2021)
- [Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning](https://mlsanity.com/paper/2104.08364/) (2021)

## Built on by (most cited first, 4 of 4)

- [Quantized Distributed Training of Large Models with Convergence Guarantees](https://mlsanity.com/paper/2302.02390/) (2023)
- [Kimad: Adaptive Gradient Compression with Bandwidth Awareness](https://mlsanity.com/paper/2312.08053/) (2023)
- [L-GreCo: Layerwise-Adaptive Gradient Compression for Efficient and Accurate Deep Learning](https://mlsanity.com/paper/2210.17357/) (2022)
- [Layer-wise Quantization for Quantized Optimistic Dual Averaging](https://mlsanity.com/paper/2505.14371/) (2025)

## Abstract (arXiv)

The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth overprovisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between "cloud-grade" servers with such support, relative to their popular "consumer-grade" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes. In this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: At the system level, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. At the application level, it provides seamless, parameter-free integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a layer-wise adaptive compression technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.