Jingwen Leng

Large language models show promise for code generation, yet their effectiveness in producing high-performance GPU kernels remains insufficiently understood. Recent agentic workflows improve over direct prompting through specialized generation roles and execution feedback, but our characterization of a representative workflow reveals failures in optimization-strategy selection, hardware-specific instruction realization, and shape-dependent parameter selection. We present AlphaCu, a transformation-driven synthesis framework for LLM-based GPU kernel generation. AlphaCu treats kernel generation as structured synthesis over explicit optimization decisions rather than end-to-end code emission. Specifically, AlphaCu represents these decisions as scoped transformation passes maintained across agent-coordinated synthesis stages, enabling compatibility checking and failure attribution. AlphaCu binds selected hardware-sensitive operations to pre-validated microkernels and uses target-device measurements to revise transformation choices and tune runtime configurations. We evaluate AlphaCu on 104 test cases covering single and fused operators across four GPU architectures: NVIDIA H100, A100, V100, and AMD MI210. Under comparable generation budgets, AlphaCu achieves 100% functional correctness on our benchmark suite and delivers an average speedup of 5.64× over prior LLM workflows, while remaining competitive with vendor libraries and tensor compilers.