cuda

CUDA C++ (.cu) kernel reference — TVM-FFI direct-export vs Python-binding entry points, the chevron-launch null-stream pitfall under CUDA-graph capture, the sm_100 fp8→bf16 cvt PTX gap, the load_inline name-cache trap, and the __launch_bounds__ register-spill lever. Also the single-source for generic per-call-overhead / CUDA-graph-capture / PDL theory (Waves-Per-SM decision table) regardless of DSL. Use when writing or debugging hand-written CUDA C++ kernels, or whenever you need generic CUDA-graph / sync-audit / PDL reasoning.

Jump to install

Repository: TongmingLAIC/AKO4X
Last source activity: May 31, 2026 at 08:57
Detected SKILL.md language: English
Stars: 67
Forks: 12

Install options

The review-first prompt is selected by default. You can switch to a direct command or download a local copy.

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

A direct command skips the review prompt. Inspect the source before running it.

npx skills add https://github.com/TongmingLAIC/AKO4X --skill cuda

The command stays on one line. Scroll horizontally to inspect it before copying.

Prefer a local copy? Download the files currently available to SkillsMP.

Review the source files

Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.

File Explorer

2 files

SKILL.md

Source instructions · Read-only preview

Back to search Back to installation

bench

Run performance benchmarks to get a verdict on whether a kernel change actually helped. Use --ab-compare for sub-1x deltas (drift cancels in same container), --variance-check for noise floor, and the subset modes (--first, --smoke; --extremes is modal-backend only) for compile-correctness probes only — those are NOT performance verdicts. The only authoritative source for "is this change real". Benchmark specifics (config.toml schema, status enum, scoring formula, baseline rule, fresh-inputs contract) live in the `benchmark` skill.

2026-05-31

benchmark

Reference for the active benchmark harness — what it IS and how it behaves (the active benchmark is flashinfer-bench). Covers config.toml structure (`[solution]`/`[build]`/`[benchmark]` tables), the status enum, the workload / fresh-inputs model, reference-baseline + scoring mechanics, tolerance keys, TVM-FFI builder link limits, and the silent-skip cascade. Invoke whenever you decode a bench status string, hit an unfamiliar config.toml field, suspect "correctness passed but the headline is implausible", or need to know what's frozen before proposing a bench edit — do NOT guess field or status semantics. Bench commands and noise methodology (A/B compare, variance check, drift cancellation) live in the `bench` skill.

2026-05-31

cpp

Host-side C++ kernels (`[build].language = cpp`) — the `solution/{kernel.cpp, binding.py}` two-file TVM-FFI binding pattern, and when to reach for it (host-side orchestration of a kernel you write, or composing CUTLASS headers into it). Distinct from the `cuda` skill which covers `.cu` device kernels compiled by nvcc. Use when writing a host-side C++ kernel or deciding cpp-vs-cuda.

2026-05-31

View all from this repository

name

cuda

description

CUDA

Reference for CUDA C++ kernel writers. Detailed guide: cuda.md.

When to consult

Writing a CUDA .cu kernel (TVM-FFI direct export or Python binding).
Resolving a RUNTIME_ERROR traceable to chevron-launch stream binding (legacy null-stream is NOT capture-aware — see cuda.md "Kernel-launch stream is NOT optional under CUDA graph capture").
Reducing per-call overhead: GPU↔CPU sync audit, CUDA-graph capture, or PDL kernel→kernel overlap — generic theory + the Waves-Per-SM decision table live here; per-DSL PDL bindings live in the DSL skills.

COUPLED references

None directly to runtime. Per-operator CUDA wins / traps live under docs/prior/ (when your operator has a prior archive).

cuda

Source facts

Install options

Review the source files

More from this repository

More from this repository

CUDA

When to consult

COUPLED references

CUDA

When to consult

COUPLED references