EasyBalance: Cross-Layer Load Balancing for Distributed MoE Inference
A novel approach named EasyBalance tackles the issue of load imbalance during inference in distributed Mixture-of-Experts (MoE) models. This method, outlined in a paper on arXiv (2608.07964), introduces cross-layer load balancing while keeping expert-device mappings intact, allowing for quick adjustments and minimal overhead. The main concept is that experts from different layers can act as backup resources, enabling cross-layer workloads to be executed together to address individual imbalances. This differs from current techniques that depend on expert replication or migration within layers, which can increase overhead and hinder scalability. Authored by unnamed researchers, this cross-type preprint highlights its potential to enhance efficiency in expert-parallel distributed inference, especially in scenarios with skewed routing distributions. The paper can be accessed via the provided arXiv link.
Key facts
- EasyBalance is a cross-layer load balancing strategy for distributed MoE inference.
- It requires no modifications to the expert-device mapping.
- It incurs essentially no additional overhead.
- It leverages experts from other layers as naturally redundant.
- It jointly executes cross-layer MoE workloads to mitigate imbalance.
- Existing approaches rely on expert replication or migration within each layer.
- The paper is on arXiv with ID 2608.07964.
- The announcement type is cross.
Entities
Institutions
- arXiv