REVIEW 7 cited by
Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts
read the original abstract
Expert parallelism has emerged as a key strategy for distributing the computational workload of sparsely-gated mixture-of-experts (MoE) models across multiple devices, enabling the processing of increasingly large-scale models. However, the All-to-All communication inherent to expert parallelism poses a significant bottleneck, limiting the efficiency of MoE models. Although existing optimization methods partially mitigate this issue, they remain constrained by the sequential dependency between communication and computation operations. To address this challenge, we propose ScMoE, a novel shortcut-connected MoE architecture integrated with an overlapping parallelization strategy. ScMoE decouples communication from its conventional sequential ordering, enabling up to 100% overlap with computation. Compared to the prevalent top-2 MoE baseline, ScMoE achieves speedups of 1.49 times in training and 1.82 times in inference. Moreover, our experiments and analyses indicate that ScMoE not only achieves comparable but in some instances surpasses the model quality of existing approaches.
Forward citations
Cited by 7 Pith papers
-
MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
MONA integrates Nesterov acceleration into Muon's orthogonalization framework, reporting better convergence than Muon and AdamW on MoE models up to 68B parameters trained on 1T tokens and SOTA fine-tuning results.
-
MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference
MACS improves MoE MLLM inference efficiency via entropy-weighted token loads and dynamic modality-adaptive expert capacity allocation.
-
MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference
MACS improves inference speed in multimodal MoE models by entropy-weighted balancing of visual tokens and real-time modality-adaptive expert capacity allocation.
-
HD-MoE: Hybrid and Dynamic Parallelism for Mixture-of-Expert LLMs with 3D Near-Memory Processing
HD-MoE combines an offline linear-programming placement search with online expert pre-broadcast, cutting simulated MoE inference latency on 3D near-memory processors by 1.1-1.8x over tensor parallelism.
-
Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection
Chameleon provides adaptive fault tolerance for distributed training by real-time selection of optimal recovery policies via a unified performance model, demonstrated with low overhead on a 32-card cluster.
-
MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference
MACS reduces Expert Parallelism stragglers in MoE MLLMs via entropy-weighted visual token load and dynamic modality-adaptive expert capacity, without retraining.
-
A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.