Individual skill
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.
skills/tilegym-improve-cutile-kernel-perf/SKILL.md
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- cutile Declared skill tag
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BENCHMARK.mddoc · 3,632 bytesSKILL.mdcore · 7,812 bytesSKILL.mdcore · 7,812 bytesSKILL.mdcore · 7,812 bytesSKILL.mdcore · 7,812 bytesSKILL.mdcore · 7,812 bytesevals/evals.jsonasset · 5,377 bytesreferences/cutile-api-reference.mddoc · 31,179 bytesreferences/cutile-patterns-reference.mddoc · 7,788 bytesreferences/ir-dump-guide.mddoc · 8,042 bytesreferences/optimization-playbook.mddoc · 15,106 bytesreferences/perf-knobs-catalog.mddoc · 6,686 bytesreferences/performance-model.mddoc · 19,029 bytesskill-card.mddoc · 4,270 bytesskill.oms.sigasset · 6,097 bytes
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# 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-<kernel_name>-<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 `ct_experimental.autotune_launch()`), the `@register_impl` registration, and current autotune configs (if any)
3. Classify the kernel: