# Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping

arXiv: https://arxiv.org/abs/2404.19429 · HTML: https://mlsanity.com/paper/2404.19429/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Efficient MoE training and inference systems, one of 12 directions in Mixture-of-experts.
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Placed in

- Mixture-of-experts / [Efficient MoE training and inference systems](https://mlsanity.com/moe/efficient-moe-training-and-inference-systems/)

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://mlsanity.com/paper/2310.01889/) (2023)
- [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://mlsanity.com/paper/2201.05596/) (2022)
- [Hash Layers For Large Sparse Models](https://mlsanity.com/paper/2106.04426/) (2021)
- [MegaBlocks: Efficient Sparse Training with Mixture-of-Experts](https://mlsanity.com/paper/2211.15841/) (2022)
- [Tutel: Adaptive Mixture-of-Experts at Scale](https://mlsanity.com/paper/2206.03382/) (2022)
- [Taming Sparsely Activated Transformer with Stochastic Experts](https://mlsanity.com/paper/2110.04260/) (2021)
- [Accelerating Distributed MoE Training and Inference with Lina](https://mlsanity.com/paper/2210.17223/) (2022)
- [Glow: Graph Lowering Compiler Techniques for Neural Networks](https://mlsanity.com/paper/1805.00907/) (2018)
- [Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers](https://mlsanity.com/paper/2303.01610/) (2023)
- [HetuMoE: An Efficient Trillion-scale Mixture-of-Expert Distributed Training System](https://mlsanity.com/paper/2203.14685/) (2022)
- [M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining](https://mlsanity.com/paper/2110.03888/) (2021)
- [Priority-based Parameter Propagation for Distributed DNN Training](https://mlsanity.com/paper/1905.03960/) (2019)
- [Oases: Efficient Large-Scale Model Training on Commodity Servers via Overlapped and Automated Tensor Model Parallelism](https://mlsanity.com/paper/2305.16121/) (2023)

## Built on by (most cited first, 22 of 22)

- [A Survey on Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2407.06204/) (2024)
- [Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging](https://mlsanity.com/paper/2506.23266/) (2025)
- [MoETuner: Optimized Mixture of Expert Serving with Balanced Expert Placement and Token Routing](https://mlsanity.com/paper/2502.06643/) (2025)
- [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025)
- [TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference](https://mlsanity.com/paper/2505.11329/) (2025)
- [FlashMoE: Fast Distributed MoE in a Single Kernel](https://mlsanity.com/paper/2506.04667/) (2025)
- [Efficient and Adaptable Overlapping for Computation and Communication via Signaling and Reordering](https://mlsanity.com/paper/2504.19519/) (2025)
- [Lazarus: Resilient and Elastic Training of Mixture-of-Experts Models](https://mlsanity.com/paper/2407.04656/) (2024)
- [Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping](https://mlsanity.com/paper/2409.15241/) (2024)
- [LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training](https://mlsanity.com/paper/2602.11686/) (2026)
- [SYMI: Efficient Mixture-of-Experts Training via Model and Optimizer State Decoupling](https://mlsanity.com/paper/2504.19925/) (2025)
- [X-MoE: Enabling Scalable Training for Emerging Mixture-of-Experts Architectures on HPC Platforms](https://mlsanity.com/paper/2508.13337/) (2025)
- [FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training](https://mlsanity.com/paper/2510.00207/) (2025)
- [MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving](https://mlsanity.com/paper/2605.02960/) (2026)
- [Searching for Plans You Can Actually Build: A Realizability-Aware Full-Space Optimizer for MoE Training and Serving](https://mlsanity.com/paper/2607.18631/) (2026)
- [MoE-Hub: Taming Software Complexity for Seamless MoE Overlap with Hardware-Accelerated Communication on Multi-GPU Systems](https://mlsanity.com/paper/2605.05888/) (2026)
- [EMO: Frustratingly Easy Progressive Training of Extendable MoE](https://mlsanity.com/paper/2605.13247/) (2026)
- [HyperParallel-MoE: Multi-Core Interleaved Scheduling for Fast MoE Training on Ascend NPUs](https://mlsanity.com/paper/2605.23764/) (2026)
- [Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads](https://mlsanity.com/paper/2606.09200/) (2026)
- [CLEAR-MoE: Shared-Basis Expert Extraction from Frozen Vision Transformers via Calibration-Driven Layer Selection](https://mlsanity.com/paper/2606.28516/) (2026)
- [UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods](https://mlsanity.com/paper/2607.06202/) (2026)
- [Weave: Fine-Grained Dynamic SM Scheduling in an MoE Megakernel for Compute-Communication Overlap](https://mlsanity.com/paper/2609.21483/) (2026)

## Abstract (arXiv)

The Mixture-of-Expert (MoE) technique plays a crucial role in expanding the size of DNN model parameters. However, it faces the challenge of extended all-to-all communication latency during the training process. Existing methods attempt to mitigate this issue by overlapping all-to-all with expert computation. Yet, these methods frequently fall short of achieving sufficient overlap, consequently restricting the potential for performance enhancements. In our study, we extend the scope of this challenge by considering overlap at the broader training graph level. During the forward pass, we enable non-MoE computations to overlap with all-to-all through careful partitioning and pipelining. In the backward pass, we achieve overlap with all-to-all by scheduling gradient weight computations. We implement these techniques in Lancet, a system using compiler-based optimization to automatically enhance MoE model training. Our extensive evaluation reveals that Lancet significantly reduces the time devoted to non-overlapping communication, by as much as 77%. Moreover, it achieves a notable end-to-end speedup of up to 1.3 times when compared to the state-of-the-art solutions.