Network Load Balancing with Parallel Flowlets for AI Training Clusters
Peirui Cao, Wenxue Cheng, Shizhen Zhao, Yongqiang Xiong · 2024
Unlike traditional data center traffic, AI training traffic primarily consists of large-size flows that are fewer in number. This characteristic poses a challenge in balancing routing granularity with reorder overhead in existing routing strategies. Existing serial flowlet schemes aim to achieve a better trade-off in TCP scenarios than flow-level or packet spraying load balancing. However, they are not well-suited for AI training clusters with high-performance RDMA networks.