KernelOPT: Dispatch-Aware Agentic Search for GPU Kernel Optimization

TL;DR - KernelOPT is a multi-agent system that optimizes compiler-generated Triton GPU sub-kernels while preserving vendor library calls and validating the reassembled model end-to-end. It improves performance over torch.compile on KernelBench while retaining the compiler baseline when no safe optimization passes verification.

• Five profiling-guided LLM agents search for optimizations within the compiled model’s existing structure.
• A four-gate cascade checks static validity, multi-seed correctness, float64-fallback model behavior, and performance.
• The system supports PyTorch nn.Module models, standalone Triton kernels, and Helion kernels.
• Across 250 KernelBench problems, geometric-mean speedups over torch.compile were 1.40×, 1.15×, and 1.07× for Levels 1, 2, and 3, respectively.