KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

📝 Original Info

• Title: KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
• ArXiv ID: 2512.23236
• Date: 2025-12-29
• Authors: Gang Liao, Hongsen Qin, Ying Wang, Alicia Golden, Michael Kuchnik, Yavuz Yetim, Jia Jiunn Ang, Chunli Fu, Yihan He, Samuel Hsia, Zewei Jiang, Dianshi Li, Uladzimir Pashkevich, Varna Puvvada, Feng Shi, Matt Steiner, Ruichao Xiao, Nathan Yan, Xiayu Yu, Zhou Fang, Roman Levenstein, Kunming Ho, Haishan Zhu, Alec Hammond, Richard Li, Ajit Mathews, Kaustubh Gondkar, Abdul Zainul-Abedin, Ketan Singh, Hongtao Yu, Wenyuan Chi, Barney Huang, Sean Zhang, Noah Weller, Zach Marine, Wyatt Cook, Carole-Jean Wu, Gaoxiang Liu

📝 Abstract

Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges -model architecture diversity, kernel primitive diversity, and hardware generation and architecture heterogeneity. The combination of the three diversity dimensions leads to a complex optimization space. This paper presents KernelEvolve -an agentic kernel coding framework -to tackle heterogeneity at-scale for DLRM training and inference. KernelEvolve is designed to take kernel specifications as input and automate the process of kernel generation and optimization for recommendation model across heterogeneous hardware architectures through multiple programming abstractions, including Triton, CuTe DSL, and low-l...

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