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AI Research
arXiv CS.AI·May 27·8 min read
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
TL;DR
Developers manually optimize Triton kernels (GPU compute programs) repeatedly across different Intel GPU hardware, applying the same low-level techniques like quantization and memory coalescing to each kernel. Xe-Forge uses large language models in a multi-stage pipeline to automate kernel optimization for Intel GPUs.
✦ Why It Matters
Engineers can automate repetitive GPU kernel optimization, reducing manual profiling cycles and accelerating deep learning deployment to Intel hardware.
Key Takeaways
- Automate kernel optimizations to save time and reduce errors.
- Utilize Xe-Forge for Intel GPU deployments in deep learning.
- Achieve significant performance improvements with minimal manual effort.
- Explore the nine optimization stages for tailored performance gains.
How It Works
Xe-Forge operates through a multi-stage pipeline that automates kernel optimization. It uses a CoVeR agent to generate optimization candidates based on a curated knowledge base of Intel GPU constraints.
The agent validates these candidates on real hardware, iterating on failures to refine the optimizations. This structured approach allows for systematic improvements across various kernels.