# gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters

arXiv: https://arxiv.org/abs/2308.05199 · HTML: https://mlsanity.com/paper/2308.05199/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2023.

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

- [SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training](https://mlsanity.com/paper/2410.15526/) (2024)
- [KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving](https://mlsanity.com/paper/2605.13734/) (2026)
- [EQuARX: Efficient Quantized AllReduce in XLA for Distributed Machine Learning Acceleration](https://mlsanity.com/paper/2506.17615/) (2025)
- [AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs](https://mlsanity.com/paper/2605.00539/) (2026)
- [NCCLZ: Compression-Enabled GPU Collectives with Decoupled Quantization and Entropy Coding](https://mlsanity.com/paper/2605.12396/) (2026)
- [CIERA: Cross-Iteration Exponent Reuse for Lossless Allgather in Sharded MoE Training](https://mlsanity.com/paper/2609.04609/) (2026)

## Abstract (arXiv)

GPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. A traditional approach is to directly integrate lossy compression into GPU-aware collectives, which can lead to serious performance issues such as underutilized GPU devices and uncontrolled data distortion. In order to address these issues, in this paper, we propose gZCCL, a first-ever general framework that designs and optimizes GPU-aware, compression-enabled collectives with an accuracy-aware design to control error propagation. To validate our framework, we evaluate the performance on up to 512 NVIDIA A100 GPUs with real-world applications and datasets. Experimental results demonstrate that our gZCCL-accelerated collectives, including both collective computation (Allreduce) and collective data movement (Scatter), can outperform NCCL as well as Cray MPI by up to 4.5X and 28.7X, respectively. Furthermore, our accuracy evaluation with an image-stacking application confirms the high reconstructed data quality of our accuracy-aware framework.