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This paper introduces a fine-grained computation-communication overlap strategy for Mixture-of-Experts (MoE) architectures that enhances the efficiency of distributed execution across multiple GPUs. By employing tile-level signaling and scheduling, the method allows for overlapping expert computation with communication, significantly reducing latency and improving GPU utilization. The results demonstrate up to 2.64x end-to-end speedup and 2.74x MoE-layer speedup on a 4-A100 GPU platform compared to state-of-the-art MoE systems, showcasing the practical benefits of this approach without altering existing computation or communication primitives.
Achieving up to 2.64x speedup in Mixture-of-Experts execution by cleverly overlapping computation and communication could redefine efficiency benchmarks in large-scale AI models.
Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key building block for scaling large language models (LLMs) to trillion-parameter regimes. Efficient deployment of these MoE models relies on distributed execution across multiple GPUs, where each MoE layer involves two all-to-all communications: dispatching tokens to expert ranks and returning the expert outputs to their source ranks. Conventional MoE implementations launch this return all-to-all after expert compute completes, exposing communication latency on the critical path and reducing GPU utilization. We present a fine-grained approach that overlaps expert compute with the second all-to-all via tile-level signaling and scheduling. Our producer-consumer co-design combines: (1) a persistent per-rank computation kernel (producer) that covers all local experts on the rank to eliminate repeated kernel launch overhead and prioritizes remote-critical tiles, and (2) a persistent communication kernel (consumer) on a small dedicated partition of streaming multiprocessors (SMs) that issues segment-granular transfers as tiles become ready. Our co-design avoids intrusive changes to the underlying computation operators or communication primitives, making it practical for improving distributed MoE execution efficiency on multi-GPU systems. On a 4-A100 GPU platform, evaluated on three MoE models against four state-of-the-art MoE systems, our approach achieves up to 2.64x end-to-end speedup and 2.74x MoE-layer speedup. Compared with a conventional non-overlap baseline, our approach consistently improves both operator- and MoE-layer-level performance across varying GEMM shapes, router modes, and a broad range of producer/consumer SM partitions, while preserving correctness.
