Agent skill · NVIDIA
tilegym-improve-cutile-kernel-perf
Iteratively optimize cuTile kernel performance through systematic profiling, bottleneck analysis, IR comparison, and targeted tuning. Covers tile sizes, occupancy, autotune configs, TMA, latency hints, persistent scheduling, num_ctas, flush_to_zero, and IR-level debugging. Use when asked to "optimize cutile kernel", "improve kernel perf", "tune cutile performance", "make kernel faster", or iteratively benchmark and refine a cuTile GPU kernel in the TileGym project.
What it needs
About 4k tokens when loaded.
What this skill does
Iterative cuTile Kernel Performance Optimization Systematically profile, diagnose bottlenecks, and iteratively tune a cuTile kernel's performance in the TileGym repository. Instructions Follow the three phases in order: Setup the environment and baseline, run the Experimentation loop with a tracked log, then iterate The experiment loop until perf goals are met or further gains plateau. Setup Work with user to prepare optimization environment: 1. Create a fresh git branch: Propose a branch name, e.g., cutile-perf-<kernelname>-<date> from current branch. Checkout git checkout -b <branch name> 2. Locate the target kernel: cuTile kernels live under src/tilegym/suites/<suite>/cutile/ or src/tilegym/ops/cutile/ Read the kernel file and identify: the @ct.kernel decorated function(s), the launch wrapper (ct.launch() or ctexperimental.autotunelaunch()), the @registerimpl registration, and current autotune configs (if any) 3. Classify the kernel: Arithmetic Intensity < 10 -> Memory-bound Arithmetic Intensity 10-50 -> Balanced Arithmetic Intensity > 50 -> Compute-bound Note: classification is only used to pick the optimization priority order in the experiment loop. The core metric is always latency (ms). 4. Check GPU environment: Ensure a GPU node (Blackwell or Ampere GPU) is available All subsequent benchmark commands should run on the GPU node 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tilegym-improve-cutile-kernel-perf