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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Jul 23·5 min read
Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts
TL;DR
A new method for overlapping computation and communication in Mixture-of-Experts (MoE) models significantly enhances performance. This approach achieves up to 2.64x speedup on multi-GPU systems without altering existing computation or communication frameworks.
✦ Why It Matters
Implement this overlapping technique to boost the performance of your MoE models on multi-GPU systems today.
Key Takeaways
- Adopt fine-grained computation-communication overlap techniques.
- Utilize persistent kernels to reduce overhead in MoE models.
- Experiment with tile-level scheduling for better resource management.
- Evaluate performance improvements across different GPU configurations.
How It Works
The proposed method integrates a persistent computation kernel that processes all local experts on a GPU rank, minimizing the need for repeated kernel launches. Simultaneously, a dedicated communication kernel handles data transfers as tiles become ready, allowing for overlapping computation and communication.
This co-design effectively reduces latency and enhances GPU utilization.