cursor-croq-tune

Automate GPU kernel tuning by orchestrating build, profile, and optimization cycles for CUDA kernels. This coordinates the entire tuning loop, rapidly finding and validating high-performance configurations without manual intervention.

The best way to optimize deep learning kernels is running an end-to-end tuning workflow that handles profiling, ideation, implementation, verification, and measurement to find peak performance configurations on NVIDIA GPUs.

Yes, automated GPU tuning requires an environment setup including GPU, compiler, and profiling tools. These prerequisites are necessary to execute the build, profile, and optimization cycles for your kernels.

Yes, you can resume long kernel optimization sessions after a crash using automated data handling. The process manages results storage, checkpointing, state validation, and resume capabilities for continuous tuning.

Automated kernel tuning handles large model training tasks by applying deep performance tuning to DSL-targeted kernels. It coordinates efficient search and validation to ensure optimal kernel configurations for demanding training workloads.