cursor-croq-tune
OfficialUnified GPU kernel tuning automation for optimal performance
Product & Management#deep learning#performance profiling#cuda#kernel optimization#automated tuning#gpu tuning
AuthorLancerLab
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the process of tuning GPU kernels, including build, profiling, and optimization, to achieve peak performance without manual intervention.
Core Features & Use Cases
- End-to-End Tuning Workflow: Coordinates building, profiling, ideation, implementation, verification, measurement, and decision-making sequences.
- Automated Data Handling: Manages results storage, checkpointing, state validation, and resume capabilities for long tuning sessions.
- Use Case: For a developer optimizing a new matrix multiplication kernel, this Skill can run the entire tuning loop, rapidly finding and validating high-performance configurations.
Quick Start
Load this Skill and set parameters for your GPU, specific kernel shape, and object. Then initiate tuning with your desired dataset and target architecture.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferencesassets
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: cursor-croq-tune Download link: https://github.com/LancerLab/croqtile-tuner/archive/main.zip#cursor-croq-tune Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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