MultiKernelBench: A Multi-Platform Benchmark for Kernel Generation

• URL: http://arxiv.org/abs/2507.17773v2
• Date: Sat, 26 Jul 2025 08:05:03 GMT
• Title: MultiKernelBench: A Multi-Platform Benchmark for Kernel Generation
• Authors: Zhongzhen Wen, Yinghui Zhang, Zhong Li, Zhongxin Liu, Linna Xie, Tian Zhang,
• Abstract summary: MultiKernelBench is a benchmark for the generation of deep learning kernels using large language models (LLMs)<n>It spans 285 tasks across 14 well-defined kernel categories and supports three major hardware platforms.<n>We show significant variation in task difficulty, poor generalization to platforms with less training exposure, and the effectiveness of targeted prompting strategies.
• Score: 17.461533973039064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automatic generation of deep learning (DL) kernels using large language models (LLMs) has emerged as a promising approach to reduce the manual effort and hardware-specific expertise required for writing high-performance operator implementations. However, existing benchmarks for evaluating LLMs in this domain suffer from limited hardware support, coarse-grained kernel categorization, and imbalanced task coverage. To address these limitations, we introduce MultiKernelBench, the first comprehensive, multi-platform benchmark for LLM-based DL kernel generation. MultiKernelBench spans 285 tasks across 14 well-defined kernel categories and supports three major hardware platforms: Nvidia GPUs, Huawei NPUs, and Google TPUs. To enable future extensibility, we design a modular backend abstraction layer that decouples platform-specific logic from the core benchmarking infrastructure, allowing easy integration of new hardware platforms. We further propose a simple yet effective category-aware one-shot prompting method that improves generation quality by providing in-category exemplars. Through systematic evaluations of seven state-of-the-art LLMs, we reveal significant variation in task difficulty, poor generalization to platforms with less training exposure, and the effectiveness of targeted prompting strategies. MultiKernelBench is publicly available at https://github.com/wzzll123/MultiKernelBench.

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