HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization
High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolves rapidly, developing efficient methods for automated GPU kernel generation and optimization has become increasingly important. Existing LLM-based methods typically optimize within a fixed implementation space, limiting either optimization flexibility or search efficiency. We propose \textsc{HIERA}, a hierarchical search-space planning framework for GPU kernel optimization. \textsc{HIERA} constructs contract-augmented task specifications, selects an appropriate implementation space across PyTorch operators, CUDA libraries, and custom CUDA ker...
| Authors | Jinghao Wang, Qiqi Gu, Chenpeng Wu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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