Understanding Gradient Compression in Distributed Training

As deep learning models continue to grow in size, distributed training has become essential for reducing training time. However, the communication overhead between nodes can become a significant bottleneck. This is where gradient compression comes into play.

What is Gradient Compression?

Gradient compression techniques aim to reduce the amount of data that needs to be communicated between nodes during distributed training, without significantly impacting model convergence.

Common Approaches

1. Sparsification

Only transmit the most significant gradients, typically the top-k largest values.

2. Quantization

Reduce the precision of gradient values (e.g., from 32-bit to 8-bit).

3. Error Feedback

Accumulate compression errors locally and add them to future iterations.

My Research Focus

In my current research, I’m exploring chunk-wise gradient sparsification combined with pipelined communication to achieve better compression ratios while maintaining model accuracy.

Stay tuned for more technical details and experimental results!