cuTile Python → cuTile.jl (Julia) Conversion

Convert @ct.kernel Python kernels to Julia function ... end cuTile.jl kernels.

Workflow Selection

• Standard conversion → Full workflow: [translations/workflow.md](translations/workflow.md)
• Errors (MethodError, IRError, numerical mismatch) → [references/debugging.md](references/debugging.md)
• Quick reference → [references/api-mapping.md](references/api-mapping.md) + [references/critical-rules.md](references/critical-rules.md)
• Test patterns → [references/testing.md](references/testing.md)

Architecture

Julia kernels are standalone — no Python bridge, no pytest integration. The Julia sub-project lives in julia/ at the repo root with its own Project.toml for dependency management.

julia/                          # Self-contained Julia sub-project
├── Project.toml                # Dependencies: CUDA.jl, cuTile.jl, NNlib.jl, Test
├── kernels/                    # cuTile.jl kernel implementations
│   ├── add.jl                  # ← Ground-truth: 1D element-wise with alpha scaling (tensor+tensor, tensor+scalar)
│   ├── matmul.jl               # ← Ground-truth: 2D tiled MMA, standard Julia layout (M,K)×(K,N)→(M,N)
│   └── softmax.jl              # ← Ground-truth: 3 strategies (TMA, online, chunked) using ct.load/ct.store
└── test/                       # Julia-native tests (using Test stdlib)
    ├── runtests.jl             # Test runner entry point
    ├── test_add.jl
    ├── test_matmul.jl
    └── test_softmax.jl

Ground-truth reference: Always consult julia/kernels/.jl and julia/test/.jl for patterns that compile and pass tests. These are the canonical examples of working cuTile.jl code.

Instructions

1. Analyze the Python kernel: identify patterns, shapes, dtypes, operations
2. Write Julia kernel — julia/kernels/<op>.jl with cuTile.jl kernel + bridge function(s)
3. Convert kernel signature (see translations/workflow.md Phase 2)
4. Convert kernel body (apply references/api-mapping.md + references/critical-rules.md)
5. Write Julia test — julia/test/test_<op>.jl using Test stdlib + NNlib.jl for reference
6. Register test — add include(...) in julia/test/runtests.jl
7. Validate — run the bundled validator: python <skill-dir>/scripts/validatecutilejl.py <file.jl>
8. Test — run julia --project=julia/ julia/test/runtests.jl

Full conversion checklist with post-conversion verification → [translations/workflow.md](translations/workflow.md)

⚠️ Top Pitfalls

The most dangerous translation errors. Full rules (17 total) in [references/critical-rules.md](references/critical-rules.md).

Worked Examples

Side-by-side Python → Julia conversions matching the released Julia kernels in julia/kernels/. Each directory contains cutilepython.py (before) and cutilejulia.jl (after).

These match the released kernels in julia/kernels/ (add.jl, matmul.jl, softmax.jl). The examples are simplified teaching versions — always consult julia/kernels/*.jl for the canonical, tested implementations.

Reference Documents

Environment Setup

Prerequisite — Julia: this skill requires the Julia version declared in julia/Project.toml under [compat] julia. If julia --version is missing or older than that, install from the official Julia site at <https://julialang.org/install/>; following the verified installer instructions for your OS. Resume below once julia --version is compatible.

Then, from the repo root:

# Install Julia dependencies declared in julia/Project.toml
julia --project=julia/ -e 'using Pkg; Pkg.instantiate()'

# Run tests
julia --project=julia/ julia/test/runtests.jl

Requirements:

• Julia (minimum version declared in julia/Project.toml under [compat] julia)
• CUDA 13.1+ driver
• Blackwell GPU (compute capability 10+)
• Dependencies managed via julia/Project.toml: CUDA.jl, cuTile.jl, NNlib.jl, Test