Quantize Once, Train Fast: Allreduce-Compatible Compression with Provable Guarantees

TL;DR

This paper presents Global-QSGD, a novel gradient quantization method compatible with Allreduce, offering theoretical guarantees and practical acceleration for distributed deep learning.

Contribution

We introduce Global-QSGD, an Allreduce-compatible quantization scheme with provable convergence guarantees, addressing limitations of prior heuristic methods.

Findings

Accelerates distributed training by up to 3.51% over baseline methods

Provides rigorous theoretical analysis extending unbiased compressor frameworks

Demonstrates effectiveness across various hardware configurations

Abstract

Distributed training enables large-scale deep learning, but suffers from high communication overhead, especially as models and datasets grow. Gradient compression, particularly quantization, is a promising approach to mitigate this bottleneck. However, existing quantization schemes are often incompatible with Allreduce, the dominant communication primitive in distributed deep learning, and many prior solutions rely on heuristics without theoretical guarantees. We introduce Global-QSGD, an Allreduce-compatible gradient quantization method that leverages global norm scaling to reduce communication overhead while preserving accuracy. Global-QSGD is backed by rigorous theoretical analysis, extending standard unbiased compressor frameworks to establish formal convergence guarantees. Additionally, we develop a performance model to evaluate its impact across different hardware configurations. Extensive experiments on NVLink, PCIe, and large-scale cloud environments show that Global-QSGD accelerates distributed training by up to 3.51% over baseline quantization methods, making it a practical and efficient solution for large-scale deep learning workloads.