  Efficient distributed training is a principal driver of recent advances in
deep learning. However, communication often proves costly and becomes the
primary bottleneck in these systems. As a result, there is a demand for the
design of efficient communication mechanisms that can empirically boost
throughput while providing theoretical guarantees. In this work, we introduce
Global-QSGD, a novel family of quantization operators, engineered to accelerate
distributed training based on global scaling. We demonstrate that Global-QSGD
is the first theoretically rigorous Allreduce-compatible compression mechanism
that achieves a provable speed-up by striking a balance between compression
error and communication savings. Importantly, Global-QSGD does not rely on
costly error feedback due to its inherent unbiasedness and offers up to
$O(\sqrt{n})$ additional compression ratio compared to the popular QSGD
quantization ($n$ represents the number of workers). To obtain theoretical
guarantees, we generalize the notion of standard unbiased compression operators
to incorporate Global-QSGD. We show that this wider class permits standard
analysis for unbiased compressors and thus ensures convergence for popular
optimization algorithms (e.g., distributed SGD) under typical settings. For the
empirical component of our work, we carry out a performance modeling analysis
to determine if Global-QSGD can enhance training throughput under specific
hardware configurations. We also conduct extensive empirical evaluations on
various tasks, testing our theory on both NVLink and PCIe connections as well
as a large-scale cloud system.
