5 papers
MetaKernelBench: Measuring GPU Kernel Knowledge Transfer Beyond Code
Xueyi Chen, Shiyu Liu, Xin Jin +5
Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implementation for correctness and sp…
MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?
Xingze Zou, Jing Wang, Yuhua Zheng +8
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile devices remains largel…
Position: Vector Prompt Interfaces Should Be Exposed to Enable Customization of Large Language Models
Liangwei Yang, Shiyu Wang, Haolin Chen +12
As large language models (LLMs) transition from research prototypes to real-world systems, customization has emerged as a central bottleneck. While text prompts can already customi…
DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels
Haolei Bai, Lingcheng Kong, Xueyi Chen +3
Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation. This paradigm i…
ConCuR: Conciseness Makes State-of-the-Art Kernel Generation
Lingcheng Kong, Jiateng Wei, Hanzhang Shen +1
GPU kernel generation by LLMs has recently experienced rapid development, leveraging test-time scaling and reinforcement learning techniques. However, a key challenge for kernel ge…