Cover Image

Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yan, Zhang, Zhenyu, Wang, Zhengang, Chen, Pengfei, Zheng, Pengfei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

A Scheduling Framework for Efficient MoE Inference on Edge GPU-NDP Systems
by: Wu, Qi, et al.
Published: (2026)

ZipMoE: Efficient On-Device MoE Serving via Lossless Compression and Cache-Affinity Scheduling
by: Yang, Yuchen, et al.
Published: (2026)

Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism
by: Dash, Sajal, et al.
Published: (2026)

DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism
by: Zeng, Zhichen, et al.
Published: (2026)

MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
by: Cao, Shiyi, et al.
Published: (2024)

Accelerating MoE Model Inference with Expert Sharding
by: Balmau, Oana, et al.
Published: (2025)

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

ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
by: Shen, Zixu, et al.
Published: (2025)

MoEless: Efficient MoE LLM Serving via Serverless Computing
by: Yu, Hanfei, et al.
Published: (2026)

MoEBlaze: Breaking the Memory Wall for Efficient MoE Training on Modern GPUs
by: Zhang, Jiyuan, et al.
Published: (2026)

Remoe: Towards Efficient and Low-Cost MoE Inference in Serverless Computing
by: Liu, Wentao, et al.
Published: (2025)

MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, et al.
Published: (2025)

HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference
by: Zhong, Shuzhang, et al.
Published: (2025)

Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
by: Yu, Zhongkai, et al.
Published: (2025)

From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill
by: Lee, Gunjun, et al.
Published: (2025)

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

MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints
by: Yuan, Yichao, et al.
Published: (2025)

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

Staleness-Centric Optimizations for Parallel Diffusion MoE Inference
by: Luo, Jiajun, et al.
Published: (2024)

Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference
by: Siavashi, Mohammad, 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)

Taming Latency-Memory Trade-Off in MoE-Based LLM Serving via Fine-Grained Expert Offloading
by: Yu, Hanfei, et al.
Published: (2025)

Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
by: Zhu, Zhanda, et al.
Published: (2025)

MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism
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)

How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
by: Wu, Hanjiang, et al.
Published: (2026)

DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient MoE Inference
by: Zhang, Yujie, et al.
Published: (2024)

TriMoE: Augmenting GPU with AMX-Enabled CPU and DIMM-NDP for High-Throughput MoE Inference via Offloading
by: Pan, Yudong, et al.
Published: (2026)

Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
by: Wang, Zhibin, et al.
Published: (2025)

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

Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design
by: Zhang, Mohan, et al.
Published: (2025)

eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference
by: Tairin, Suraiya, et al.
Published: (2025)

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

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

Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing
by: Seal, Sudip K., 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)

Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
by: Xue, Chunyu, et al.
Published: (2024)

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