hipfire-kernel-tuning
hipfire-kernel-tuning is a Go, Rust & systems languages skill for AI agents, published by Kaden-Schutt in the hipfire repository on GitHub. Skillspace rates hipfire-kernel-tuning C (72/100) and it passed the safety scan; the rating is based on static analysis only.
Optimize hipfire HIP/compute kernels — pick a tuning lever (multi-row, K-tile depth, prefetch, wave-size port, WMMA/MFMA, fused projections, ISA flags) and validate the win across the supported RDNA arch matrix. Use when you've identified a hot kernel, want to land a real perf win, and need to NOT regress on archs you don't have hardware for. Codifies the methodology from this repo's actual perf history — wave64 CDNA3 port (commit 4105035, 2× decode), nontemporal-load revert (34eb024, -13% caught only by clean-baseline bisect), gfx12 WMMA port (PR
- Grade
- C
- Quality
- 72 / 100
- Safety
- Passed
- GitHub stars
- 482
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 96 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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