Large-scale deep neural networks (DNN) exhibit excellent performance for various tasks. As DNNs and datasets grow, distributed training becomes extremely time-consuming and demands larger clusters. A main bottleneck is the resulting gradient aggregation overhead. While gradient compression and sparse collective communication techniques are commonly employed to alleviate network load, many gradient compression schemes do not achieve acceleration of the training process while also preserving accuracy. This paper introduces PacTrain, a novel framework that accelerates distributed training by combining pruning with sparse gradient compression. Active pruning of the neural network makes the model weights and gradients sparse. By ensuring the global knowledge of the gradient sparsity among all distributed training workers, we can perform lightweight compression communication without harming accuracy. We show that the PacTrain compression scheme achieves a near-optimal compression strategy while remaining compatible with the all-reduce primitive. Experimental evaluations show that PacTrain improves training throughput by 1.25 to 8.72 times compared to state-of-the-art compression-enabled systems for representative vision and language models training tasks under bandwidth-constrained conditions.
View on arXiv@article{wang2025_2505.18563,
title={ PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning },
author={ Yisu Wang and Ruilong Wu and Xinjiao Li and Dirk Kutscher },
journal={arXiv preprint arXiv:2505.18563},
year={ 2025 }
}