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Compiler Feedback Turned Kernel Writing Into a Search Party
MaxKernel combines collaborative, autonomous and graph-search modes with specialized agents for planning, profiling, testing and self-debugging on TPUs.
Summary
MaxKernel combines collaborative, autonomous and graph-search modes with specialized agents for planning, profiling, testing and self-debugging on TPUs.
The system uses real-time compiler and hardware feedback to generate accelerator kernels under three modes: human-in-the-loop design, an autonomous metric-driven loop and graph-based exploration. It shares specialized planning, implementation, debugging, testing and profiling agents across the modes. Evaluations cover 50 JaxBench tasks plus larger open-source workloads, where the authors report performance matching expert-tuned baselines. Those claims are benchmark-specific and do not establish optimal kernels for every TPU workload.
Why it matters
MaxKernel combines collaborative, autonomous and graph-search modes with specialized agents for planning, profiling, testing and self-debugging on TPUs.
Limits and context
- Those claims are benchmark-specific and do not establish optimal kernels for every TPU workload.
Key claims
MaxKernel combines collaborative, autonomous and graph-search modes with specialized agents for planning, profiling, testing and self-debugging on TPUs.
Qualification: Those claims are benchmark-specific and do not establish optimal kernels for every TPU workload.
Evidence: source-2026-09-07-013
Sources
- arXiv preprint 2609.04523arXiv · primary research
Corrections
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