Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Wei, Lai, Zhiquan, Li, Shengwei, Liu, Weijie, Ge, Keshi, Shen, Ao, Su, Huayou, Li, Dongsheng |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Oases: Efficient Large-Scale Model Training on Commodity Servers via Overlapped and Automated Tensor Model Parallelism
by: Li, Shengwei, et al.
Published: (2023)
by: Li, Shengwei, et al.
Published: (2023)
Fine-grained MoE Load Balancing with Linear Programming
by: Zhao, Chenqi, et al.
Published: (2025)
by: Zhao, Chenqi, et al.
Published: (2025)
FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training
by: Qi, Shuyao, et al.
Published: (2026)
by: Qi, Shuyao, et al.
Published: (2026)
UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
by: Zheng, Size, et al.
Published: (2026)
by: Zheng, Size, et al.
Published: (2026)
ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
by: Wang, Yingping, et al.
Published: (2026)
by: Wang, Yingping, et al.
Published: (2026)
OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)
by: Wang, Liujianfu, et al.
Published: (2025)
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024)
by: Nie, Xiaonan, et al.
Published: (2024)
MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, et al.
Published: (2025)
by: Liu, Dennis, et al.
Published: (2025)
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism
by: Ai, Xin, et al.
Published: (2024)
by: Ai, Xin, et al.
Published: (2024)
Accelerating Distributed MoE Training and Inference with Lina
by: Li, Jiamin, et al.
Published: (2022)
by: Li, Jiamin, et al.
Published: (2022)
Load Balanced Parallel Node Generation for Meshless Numerical Methods
by: Vehovar, Jon, et al.
Published: (2026)
by: Vehovar, Jon, et al.
Published: (2026)
Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
by: Luo, Jiajun, et al.
Published: (2024)
by: Luo, Jiajun, et al.
Published: (2024)
Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)
by: Luo, Shuqing, et al.
Published: (2024)
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
Sparse Checkpointing for Fast and Reliable MoE Training
by: Gandhi, Swapnil, et al.
Published: (2024)
by: Gandhi, Swapnil, et al.
Published: (2024)
Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference
by: Sun, Xun, et al.
Published: (2026)
by: Sun, Xun, et al.
Published: (2026)
MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training
by: Zhao, Lu, et al.
Published: (2025)
by: Zhao, Lu, et al.
Published: (2025)
MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
by: Zhang, Zheng, et al.
Published: (2025)
by: Zhang, Zheng, et al.
Published: (2025)
Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)
by: Luo, Shuqing, et al.
Published: (2025)
Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
by: Li, Haoyang, et al.
Published: (2024)
by: Li, Haoyang, et al.
Published: (2024)
SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference
by: Chen, Liangkun, et al.
Published: (2025)
by: Chen, Liangkun, et al.
Published: (2025)
GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
by: Han, Yu, et al.
Published: (2025)
by: Han, Yu, et al.
Published: (2025)
PROBE: Co-Balancing Computation and Communication in MoE Inference via Real-Time Predictive Prefetching
by: Zhu, Qianchao, et al.
Published: (2026)
by: Zhu, Qianchao, et al.
Published: (2026)
MoEntwine: Unleashing the Potential of Wafer-scale Chips for Large-scale Expert Parallel Inference
by: Tang, Xinru, et al.
Published: (2025)
by: Tang, Xinru, et al.
Published: (2025)
Expert-as-a-Service: Towards Efficient, Scalable, and Robust Large-scale MoE Serving
by: Liu, Ziming, et al.
Published: (2025)
by: Liu, Ziming, et al.
Published: (2025)
ProMoE: Fast MoE-based LLM Serving using Proactive Caching
by: Song, Xiaoniu, et al.
Published: (2024)
by: Song, Xiaoniu, et al.
Published: (2024)
MixServe: An Automatic Distributed Serving System for MoE Models with Hybrid Parallelism Based on Fused Communication Algorithm
by: Zhou, Bowen, et al.
Published: (2026)
by: Zhou, Bowen, et al.
Published: (2026)
Zeppelin: Balancing Variable-length Workloads in Data Parallel Large Model Training
by: Chen, Chang, et al.
Published: (2025)
by: Chen, Chang, et al.
Published: (2025)
Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems
by: Liu, Guowei, et al.
Published: (2026)
by: Liu, Guowei, et al.
Published: (2026)
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)
by: Qian, Yulei, et al.
Published: (2024)
HarMoEny: Efficient Multi-GPU Inference of MoE Models
by: Doucet, Zachary, et al.
Published: (2025)
by: Doucet, Zachary, et al.
Published: (2025)
S-HPLB: Efficient LLM Attention Serving via Sparsity-Aware Head Parallelism Load Balance
by: Liu, Di, et al.
Published: (2026)
by: Liu, Di, et al.
Published: (2026)
Tackling the Data-Parallel Load Balancing Bottleneck in LLM Serving: Practical Online Routing at Scale
by: Bu, Tianci, et al.
Published: (2026)
by: Bu, Tianci, et al.
Published: (2026)
Balancing Pipeline Parallelism with Vocabulary Parallelism
by: Yeung, Man Tsung, et al.
Published: (2024)
by: Yeung, Man Tsung, et al.
Published: (2024)
Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks
by: Shi, Long, et al.
Published: (2025)
by: Shi, Long, et al.
Published: (2025)
DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism
by: Zeng, Zhichen, et al.
Published: (2026)
by: Zeng, Zhichen, et al.
Published: (2026)
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
by: Pan, Xinglin, et al.
Published: (2025)
by: Pan, Xinglin, et al.
Published: (2025)
Efficient CPU-GPU Collaborative Inference for MoE-based LLMs on Memory-Limited Systems
by: Huang, En-Ming, et al.
Published: (2025)
by: Huang, En-Ming, et al.
Published: (2025)
When MoE Meets Blockchain: A Trustworthy Distributed Framework of Large Models
by: Zhu, Weihao, et al.
Published: (2025)
by: Zhu, Weihao, et al.
Published: (2025)
ReviveMoE: Fast Recovery for Hardware Failures in Large-Scale MoE LLM Inference Deployments
by: Li, Haley, et al.
Published: (2026)
by: Li, Haley, et al.
Published: (2026)
Similar Items
-
Oases: Efficient Large-Scale Model Training on Commodity Servers via Overlapped and Automated Tensor Model Parallelism
by: Li, Shengwei, et al.
Published: (2023) -
Fine-grained MoE Load Balancing with Linear Programming
by: Zhao, Chenqi, et al.
Published: (2025) -
FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training
by: Qi, Shuyao, et al.
Published: (2026) -
UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
by: Zheng, Size, et al.
Published: (2026) -
ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
by: Wang, Yingping, et al.
Published: (2026)