ProfessorGPTProfessorGPT
NVIDIA
Multi-file
NVIDIANVIDIA· v1.0.0
codingOfficial
Official Provider SkillView repo

Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.

Files17 files
BENCHMARK.md103 lines
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Skill details

Versionv1.0.0
AuthorNVIDIA
Categorycoding
Skill IDNVIDIA/TileGym/skills/tilegym-converting-cutile-to-julia
Files17 files

Related skills

Tilegym Adding Cutile KernelAdd a new cuTile GPU kernel operator to TileGym. Covers dispatch registration in ops.py, cuTile backend implementation, __init__.py exports, test creation, and benchmark in tests/benchmark. Use when adding, creating, or implementing a new cuTile operator/kernel in TileGym, or when asking how to register a new cuTile op.Tilegym Converting Cutile To TritonConverts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translatTilegym Converting Cutile Triton To Cutile RsUse this skill to convert, port, or translate Triton-TileIR or cuTile-Python GPU kernels to cutile-rs (Rust). The orchestrator runs scripts/preflight.sh, then drives a bounded Agent A -> B -> D -> E pipeline (Agent C is diagnostic, Agent F optional), delegating all kernel/host/correctness/perf work to sub-agents and routing by each stage's single-line VERDICT.Tilegym Cutile AutotuningUse when adding, modifying, optimizing, or debugging CuTile autotuning code. Trigger signals: `exhaustive_search` / `replace_hints` / `hints_fn` / `cuda.tile.tune` in code, `autotune` in filenames, or correctness/performance issues in autotuned CuTile kernels. Covers: tune-once/cache/launch pattern, per-architecture configs (sm80–sm120), parameter space design (tile sizes, occupancy, num_ctas), and 7 common pitfalls with solutions.Tilegym Cutile OptimizingQuestion-gated router for the curated cuTile optimization wiki: kernel-family playbooks, measured techniques with caveats, performance patterns, and practical language knowledge for Blackwell and Hopper. Use when an agent has inspected the current kernel and has a concrete question that could change its plan, when measurements point to a specific bottleneck, or when a cuTile language or API detail is blocking implementation. Do not use as mandatory onboarding, as the source of the first design, Tilegym Cutile PythonExpert cuTile programming assistant. Write high-performance GPU kernels using cuTile's tile-based programming model with proper validation and optimization. Supports deep agent orchestration for complex multi-kernel tasks.