Pipeline-parallel distributed optimization is essential for large-scale machine learning but is challenged by significant communication overhead from transmitting high-dimensional activations and gradients between workers. Existing approaches often depend on impractical unbiased gradient assumptions or incur sample-size memory overhead. This paper introduces Clapping, a Communication compression algorithm with LAzy samPling for Pipeline-parallel learnING. Clapping adopts a lazy sampling strategy that reuses data samples across steps, breaking sample-wise memory barrier and supporting convergence in few-epoch or online training regimes. Clapping comprises two variants including Clapping-FC and Clapping-FU, both of which achieve convergence without unbiased gradient assumption, effectively addressing compression error propagation in multi-worker settings. Numerical experiments validate the performance of Clapping across different learning tasks.
@article{arxiv.2509.19029,
title = {Clapping: Removing Per-sample Storage for Pipeline Parallel Distributed Optimization with Communication Compression},
author = {Boao Kong and Xu Huang and Yuqi Xu and Yixuan Liang and Bin Wang and Kun Yuan},
journal= {arXiv preprint arXiv:2509.19029},
year = {2025}
}