Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Xe-Forge is a new multi-stage pipeline designed to optimize kernel performance for Intel GPUs. It automates the repetitive process of applying low-level optimizations to deep learning algorithms, significantly reducing the manual effort required. The system has demonstrated impressive speedups across various kernels, showcasing its potential to streamline algorithm deployment on new hardware accelerators.
- ▪Xe-Forge automates the optimization of Triton kernels for Intel GPUs through a multi-stage process.
- ▪The system applies up to nine optimization stages, driven by a Chain-of-Verification-and-Refinement agent.
- ▪Evaluation results show a 1.17x geometric mean speedup over PyTorch eager, with 67% of kernels improving.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26118 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
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Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2605.26118 (cs) [Submitted on 16 Apr 2026] Title:Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU Authors:Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky, Gabriela Ben-Melech Stan, Shira Guskin, Weilin Xu, Adam Siemieniuk, Alexander Heinecke View a PDF of the paper titled Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU, by Marcin Spoczynski and 7 other authors View PDF HTML (experimental) Abstract:Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, tile size tuning, and architecture-specific workarounds -- to every Triton kernel in their code-base.
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