LuWu: An End-to-End In-Network Out-of-Core Optimizer for 100B-Scale Model-in-Network Data-Parallel Training on Distributed GPUs
<|reference_start|>LuWu: An End-to-End In-Network Out-of-Core Optimizer for 100B-Scale Model-in-Network Data-Parallel Training on Distributed GPUs: The recent progress made in large language models (LLMs) has brought tremendous application prospects to the world. The growing model size demands LLM training on multiple GPUs, while data parallelism is the most popular distributed training strategy due to its simplicity, efficiency, and scalability. Current systems adopt the model-sharded data parallelism to enable memory-efficient training, however, existing model-sharded data-parallel systems fail to efficiently utilize GPU on a commodity GPU cluster with 100 Gbps (or 200 Gbps) inter-GPU bandwidth due to 1) severe interference between collective operation and GPU computation and 2) heavy CPU optimizer overhead. Recent works propose in-network aggregation (INA) to relieve the network bandwidth pressure in data-parallel training, but they are incompatible with model sharding due to the network design. To this end, we propose LuWu, a novel in-network optimizer that enables efficient model-in-network data-parallel training of a 100B-scale model on distributed GPUs. Such new data-parallel paradigm keeps a similar communication pattern as model-sharded data parallelism but with a centralized in-network optimizer execution. The key idea is to offload the entire optimizer states and parameters from GPU workers onto an in-network optimizer node and to offload the entire collective communication from GPU-implemented NCCL to SmartNIC-SmartSwitch co-optimization. The experimental results show that LuWu outperforms the state-of-the-art training system by 3.98x when training on a 175B model on an 8-worker cluster.<|reference_end|>
@article{sun2024luwu:,
    title={LuWu: An End-to-End In-Network Out-of-Core Optimizer for 100B-Scale
  Model-in-Network Data-Parallel Training on Distributed GPUs},
    author={Mo Sun, Zihan Yang, Changyue Liao, Yingtao Li, Fei Wu, Zeke Wang},
    journal={arXiv preprint arXiv:2409.00918},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00918},
    primaryClass={cs.DC}
}