FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training
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| Format: | Preprint |
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2026
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| _version_ | 1866914496315916288 |
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| author | Qi, Shuyao Liu, Haoyuan Zhao, Shizhen |
| author_facet | Qi, Shuyao Liu, Haoyuan Zhao, Shizhen |
| contents | Fine-grained, per-micro-batch load balancing is essential for efficient Mixture-of-Experts (MoE) training, yet every prior dynamic scheduling scheme pays for it with extra communication that is hard to hide. Especially on modern bulk-transfer backends such as DeepEP. We make a simple but consequential observation: on the NVIDIA Hopper architecture the NVLink Copy Engine can move data between intra-node GPUs without consuming any SM cycles, effectively providing a nearly free communication channel that runs in parallel with compute kernels. FEPLB turns this idle hardware into a new parallel dimension for MoE load rebalancing. Its Two-Phase Dispatch first routes tokens across nodes via the standard EP backend, then redistributes dynamic-expert tokens and weights within the NVLink domain through the Copy Engine at nearly zero cost, while a lightweight CPU scheduler runs concurrently with static expert computation. Because FEPLB uses only Copy Engine and CPU that are orthogonal to those consumed by EP and PP, it coexists with existing parallel strategies without reconfiguration.
On GLM-5's MoE layers (128 experts, no auxiliary loss, up to 16 H100 GPUs), FEPLB reduces the token straggler by 51-70% and the GEMM straggler by 50-68% with no measurable EP communication overhead. Its advantage grows with the EP degree: at EP=8, it achieves 2x lower token straggler than FasterMoE. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_19654 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training Qi, Shuyao Liu, Haoyuan Zhao, Shizhen Distributed, Parallel, and Cluster Computing Fine-grained, per-micro-batch load balancing is essential for efficient Mixture-of-Experts (MoE) training, yet every prior dynamic scheduling scheme pays for it with extra communication that is hard to hide. Especially on modern bulk-transfer backends such as DeepEP. We make a simple but consequential observation: on the NVIDIA Hopper architecture the NVLink Copy Engine can move data between intra-node GPUs without consuming any SM cycles, effectively providing a nearly free communication channel that runs in parallel with compute kernels. FEPLB turns this idle hardware into a new parallel dimension for MoE load rebalancing. Its Two-Phase Dispatch first routes tokens across nodes via the standard EP backend, then redistributes dynamic-expert tokens and weights within the NVLink domain through the Copy Engine at nearly zero cost, while a lightweight CPU scheduler runs concurrently with static expert computation. Because FEPLB uses only Copy Engine and CPU that are orthogonal to those consumed by EP and PP, it coexists with existing parallel strategies without reconfiguration. On GLM-5's MoE layers (128 experts, no auxiliary loss, up to 16 H100 GPUs), FEPLB reduces the token straggler by 51-70% and the GEMM straggler by 50-68% with no measurable EP communication overhead. Its advantage grows with the EP degree: at EP=8, it achieves 2x lower token straggler than FasterMoE. |
| title | FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.19654 |