Zihan Liu

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.

3D Gaussian Splatting (3DGS) has vastly advanced the pace of neural rendering, but it remains computationally demanding on today's mobile SoCs. To address this challenge, we propose Lumina, a hardware-algorithm co-designed system, which integrates two principal optimizations: a novel algorithm, S^2, and a radiance caching mechanism, RC, to improve the efficiency of neural rendering. S2 algorithm exploits temporal coherence in rendering to reduce the computational overhead, while RC leverages the color integration process of 3DGS to decrease the frequency of intensive rasterization computations. Coupled with these techniques, we propose an accelerator architecture, LuminCore, to further accelerate cache lookup and address the fundamental inefficiencies in Rasterization. We show that Lumina achieves 4.5x speedup and 5.3x energy reduction against a mobile Volta GPU, with a marginal quality loss (< 0.2 dB peak signal-to-noise ratio reduction) across synthetic and real-world datasets.