auto-gpu-kernel

Automate Triton kernel optimization by running an autonomous agent loop that iteratively profiles, plans, implements, and benchmarks DSA sparse attention kernels using a templated project structure and Modal-based benchmarking harness.

Autonomous kernel optimization uses sub-agents for profiling, workload inspection, and research that communicate through on-disk artifacts to guide iterative improvements, evaluating changes against FlashInfer-Bench definitions within a strict A/B workflow.

Scaffold a kernel optimization project by generating a complete template structure containing CLAUDE.md, config.toml, and solution/triton skeletons with baseline and fused stubs, then start the autonomous optimization loop defined in the CLAUDE.md workflow.

Yes, Modal is required to run the autonomous kernel benchmarking workflow, providing the cloud-based harness needed to evaluate Triton kernel changes under realistic workloads during iterative optimization.

Yes, you can adapt the templated project structure across different kernels and new FlashInfer-Bench definitions, applying customization guidance and hazard considerations to target new sparse attention workloads.

Autonomous Triton kernel development requires strict experimental discipline and depends on predefined config.toml settings and baseline stubs; agents communicate through on-disk artifacts, so workflows break if templated structures are improperly modified.