TopoEP: Topology-Aware Load Balancing for Expert-Parallel MoE Training
Dynamic routing creates severe load imbalance in large-scale expert-parallel Mixture-of-Experts (MoE) training, turning GPUs that host hot experts into stragglers. As each MoE layer waits for its slowest rank, these stragglers prolong the expert-parallel stage and reduce overall training efficiency. Existing expert-parallelism load-balancing (EPLB) systems commonly compute load-balancing plans on the CPU, incurring device--host data transfers and cross-rank synchronization that make scheduling at every layer and microbatch expensive. Their planning formulations also overlook the hierarchical communication costs of modern scale-up and scale-out GPU clusters. We present \textit{TopoEP}, a G...
| Authors | Jiacheng Zhu, Xie Zhao, Gongming Zhao et al. |
|---|---|
| Primary category | cs.DC |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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