# Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling

**Authors:** Jialong Li, Shreyansh Tripathi, Lakshay Rastogi, Yiming Lei, Rui Pan, Yiting Xia  
**arXiv:** [2410.17043](https://arxiv.org/abs/2410.17043)  
**Date:** 2024-10-22  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.17043v1/

## Key points

- Machine learning scalability and complexity present computational challenges
- Mixture-of-Experts (MoE) models selectively engage relevant experts to overcome obstacles
- Challenges faced by MoE models include high communication overhead, limited GPU utilization, and heterogeneous GPU environments
- Aurora optimizes model deployment and all-to-all communication scheduling in MoE inference processes
- Aurora minimizes communication times by strategically ordering token transmissions
- Aurora enhances GPU utilization by colocating experts from different models on the same device
- Optimization strategies of Aurora analyzed across exclusive versus colocated models on GPUs, and homogeneous versus heterogeneous GPUs settings
- Aurora offers optimal solutions for three cases and a sub-optimal solution with marginal degradation in NP-hard scenario
- Aurora minimizes MoE inference time through optimal model deployment and communication scheduling across diverse scenarios
- Evaluations show significant performance enhancements with speedups up to 2.38x in homogeneous clusters and 3.54x in heterogeneous environments
- Aurora boosts GPU utilization by up to 1.5x compared to existing methods

## Summary

In the realm of machine learning, the scalability and complexity of models pose significant computational challenges. <br/>
Mixture-of-Experts (MoE) models have emerged as a solution by selectively engaging relevant experts to tackle these obstacles. However, MoE models encounter hurdles such as high communication overhead from all-to-all operations, limited GPU utilization due to synchronous communication constraints, and complications arising from heterogeneous GPU environments. <br/>
To address these issues, a groundbreaking approach called Aurora has been introduced. Aurora focuses on optimizing both model deployment and all-to-all communication scheduling in MoE inference processes. By strategically ordering token transmissions in all-to-all communications, Aurora minimizes communication times effectively. Furthermore, it enhances GPU utilization by colocating experts from different models on the same device, thereby circumventing the limitations imposed by synchronous all-to-all communication. The optimization strategies employed by Aurora are rigorously analyzed across four prevalent GPU cluster settings: exclusive versus colocated models on GPUs, and homogeneous versus heterogeneous GPUs.<br/>
Remarkably, Aurora offers optimal solutions for three cases and provides a polynomial-time sub-optimal solution with only a marginal 1.07x degradation from the optimal in the remaining NP-hard scenario.<br/>
Aurora stands out as the pioneer in minimizing MoE inference time through optimal model deployment and communication scheduling across diverse scenarios. Evaluations demonstrate its remarkable performance enhancements, showcasing speedups of up to 2.38x in homogeneous clusters and an impressive 3.54x in heterogeneous environments.<br/>
Additionally, Aurora significantly boosts GPU utilization by up to 1.5x compared to existing methods.<br/>
Authored by Jialong Li, Shreyansh Tripathi, Lakshay Rastogi, Yiming Lei, Rui Pan, and Yiting Xia; this research paper titled "Optimizing Mixture-of-Experts Inference Time Combining Model Deployment and Communication Scheduling" sheds light on a cutting-edge approach that revolutionizes MoE inference processes for enhanced efficiency and performance in machine learning applications.