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KernelAgent is an open-source multi-agent system from the PyTorch team that automates GPU kernel optimization by integrating real hardware profiling signals into a closed-loop workflow. The system decomposes the optimization process into specialized agents: a Profiler (using NVIDIA Nsight Compute), a Diagnose Agent (roofline analysis), a Prescriber, an Orchestrator, parallel Optimization Workers, and a Benchmarking Agent. Across 100 KernelBench L1 tasks on H100, it achieves 1.56x speedup over default torch.compile and 89% of hardware roofline efficiency. A detailed matrix-vector multiplication case study shows how the system iteratively discovers architectural changes—like switching to one-row-per-program with scalar accumulators—that LLM-only approaches without hardware feedback miss. Key design principles include grounding decisions in real hardware metrics, parallel exploration with beam search, and shared reflective memory across rounds.

KernelAgent: Hardware-Guided GPU Kernel Optimization via Multi-Agent Orchestration – PyTorch