Cover Image

Staleness-Centric Optimizations for Parallel Diffusion MoE Inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Luo, Jiajun, Luo, Lizhuo, Xu, Jianru, Song, Jiajun, Lu, Rongwei, Tang, Chen, Wang, Zhi
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
by: Song, Jiajun, et al.
Published: (2024)

Beyond A Single AI Cluster: A Survey of Decentralized LLM Training
by: Dong, Haotian, et al.
Published: (2025)

Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
by: Luo, Shuqing, et al.
Published: (2025)

MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching
by: Xu, Tairan, et al.
Published: (2025)

Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference
by: Sun, Xun, et al.
Published: (2026)

OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
by: Wang, Liujianfu, et al.
Published: (2025)

SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference
by: Chen, Liangkun, et al.
Published: (2025)

Accelerating Distributed MoE Training and Inference with Lina
by: Li, Jiamin, et al.
Published: (2022)

Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
by: Wu, Tian, et al.
Published: (2025)

Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
by: Pan, Xinglin, et al.
Published: (2025)

Janus: Disaggregating Attention and Experts for Scalable MoE Inference
by: Zhang, Zhexiang, et al.
Published: (2025)

Hexa-MoE: Efficient and Heterogeneous-aware Training for Mixture-of-Experts
by: Luo, Shuqing, et al.
Published: (2024)

GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
by: Han, Yu, et al.
Published: (2025)

FlowSpec: Continuous Pipelined Speculative Decoding for Efficient Distributed LLM Inference
by: Liu, Xing, et al.
Published: (2025)

HarMoEny: Efficient Multi-GPU Inference of MoE Models
by: Doucet, Zachary, et al.
Published: (2025)

MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
by: Zhang, Zheng, et al.
Published: (2025)

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
by: Ma, Songkai, et al.
Published: (2025)

EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
by: Qian, Yulei, et al.
Published: (2024)

ReviveMoE: Fast Recovery for Hardware Failures in Large-Scale MoE LLM Inference Deployments
by: Li, Haley, et al.
Published: (2026)

Fine-grained MoE Load Balancing with Linear Programming
by: Zhao, Chenqi, et al.
Published: (2025)

ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
by: Wang, Yingping, et al.
Published: (2026)

BrownoutServe: SLO-Aware Inference Serving under Bursty Workloads for MoE-based LLMs
by: Hu, Jianmin, et al.
Published: (2025)

UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training
by: Zheng, Size, et al.
Published: (2026)

MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, 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)

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024)

MemFine: Memory-Aware Fine-Grained Scheduling for MoE Training
by: Zhao, Lu, et al.
Published: (2025)

Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models
by: Wang, Wei, et al.
Published: (2024)

Efficient CPU-GPU Collaborative Inference for MoE-based LLMs on Memory-Limited Systems
by: Huang, En-Ming, et al.
Published: (2025)

Multi-Layer Scheduling for MoE-Based LLM Reasoning
by: Sun, Yifan, et al.
Published: (2026)

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
by: Wang, Haodong, et al.
Published: (2025)

DuoServe-MoE: Dual-Phase Expert Prefetch and Caching for LLM Inference QoS Assurance
by: Zhang, Yuning, et al.
Published: (2025)

MixServe: An Automatic Distributed Serving System for MoE Models with Hybrid Parallelism Based on Fused Communication Algorithm
by: Zhou, Bowen, et al.
Published: (2026)

Sparse Checkpointing for Fast and Reliable MoE Training
by: Gandhi, Swapnil, et al.
Published: (2024)

Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
by: Dutta, Ankita, et al.
Published: (2025)

Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
by: Li, Yan, et al.
Published: (2025)

EC2MoE: Adaptive End-Cloud Pipeline Collaboration Enabling Scalable Mixture-of-Experts Inference
by: Yang, Zheming, et al.
Published: (2025)

HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference
by: Tang, Peng, et al.
Published: (2024)

ProMoE: Fast MoE-based LLM Serving using Proactive Caching
by: Song, Xiaoniu, et al.
Published: (2024)

Making MoE-based LLM Inference Resilient with Tarragon
by: Zhang, Songyu, et al.
Published: (2026)