Automatic Kernel Generation with Large Language Models: Taxonomies, Evaluations, and Cross-Platform Challenges

Large language models (LLMs) have recently emerged as a promising tool for automatic generation and optimization of high-performance compute kernels. This rapidly growing area spans across hardware platforms and targets programming models such as AscendC, CUDA, and Triton. However, the literature remains fragmented, with substantial variation in generation workflows, training strategies, evaluation protocols, benchmark assumptions, hardware platforms, and reported metrics. This survey provides a structured review of LLM-based automatic kernel generation. We introduce a taxonomy based on the generation-time workflow, separating direct generation, refinement via structured feedback, search-based generation, and adaptive tool-orchestrating agentic systems. We further analyze orthogonal design axes including learning strategy, input representation, output representation, and hardware platform. Beyond method classification, we examine evaluation practices, including benchmark data, harnesses, integrity policies, as well as performance metrics, and discuss why reported results are often difficult to compare. Finally, we use Ascend NPU kernel generation as a cross-platform case study, highlighting challenges introduced by AscendC, host-device co-design, toolchain constraints, and limited public data. We conclude by identifying open problems in robust evaluation, higher-level task optimization, cross-platform transfer, production deployment, and benchmark design.