[R] ZeRO++: Extremely Efficient Collective Communication for Giant Model Training - Microsoft 2023

Paper: [https://arxiv.org/abs/2306.10209](https://arxiv.org/abs/2306.10209)

Github: [https://github.com/microsoft/DeepSpeed](https://github.com/microsoft/DeepSpeed)

Abstract:

>Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limited because of high communication volume from gathering weights in forward pass, backward pass, and averaging gradients. This paper introduces three communication volume …

abstract communication efficiency good gpu gpus language language models large language large language models low machinelearning massive per redundancy scalability scale small training