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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
TEMPO: Makespan-Aware Expert-Parallel Load Balancing Across Memory- and Compute-Bound Regimes
TL;DR
TEMPO is a makespan-aware dispatcher designed for expert-parallel (EP) model serving, optimizing load balancing across memory- and compute-bound tasks. It achieves up to 15.5% performance improvement in mixed regime scenarios while maintaining near-optimal efficiency.
✦ Why It Matters
Implement TEMPO to enhance load balancing in your EP model serving for better performance today.
Key Takeaways
- Utilize TEMPO for efficient load balancing in EP model serving.
- Monitor token counts to optimize performance across GPU regimes.
- Leverage SGLang integration for streamlined dispatch processes.
- Apply phase diagram insights to predict performance outcomes before deployment.
How It Works
TEMPO operates by analyzing the relationship between token counts and expert activation, using a max-affine profile to optimize load balancing. It dynamically adjusts dispatch strategies based on the current GPU regime, ensuring that both memory and compute resources are utilized effectively.
By integrating with SGLang, TEMPO fuses dispatch and count collection into a single in-graph kernel, minimizing overhead and improving efficiency.