What is gradient compression and where is it used?

Gradient compression is a technique for reducing the amount of data transmitted between devices during distributed neural network training. It is used to decrease network traffic and accelerate communication between GPUs or nodes.

In distributed training, after computing gradients on each device, they need to be exchanged and averaged (for example, in the AllReduce algorithm). With large models and many devices, the volume of data transmitted becomes a bottleneck.

Gradient compression addresses this issue by reducing the size of gradients before transmission. This can include:

• Quantization: representing gradients with fewer bits.
• Lossy compression (sparsification): transmitting only significant gradients, with the rest considered zero.
• Using specialized compression algorithms.

It is used in distributed training on GPU clusters, especially when scaling to hundreds and thousands of devices, to increase bandwidth and reduce communication delays.