KernelFlow-ops

KernelFlow-ops / cuda-optimized-skill

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A CUDA kernel optimization toolkit for validation, benchmarking, Nsight Compute profiling, bottleneck analysis, and iterative tuning. It helps improve custom GPU operators with reproducible workflows and evidence-based performance comparison.

13
1
100% credibility
Found Apr 13, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A set of skills and scripts for AI agents to iteratively optimize GPU kernels through correctness validation, benchmarking, profiling, and strategy-guided improvements across multiple backends.

How It Works

1
📖 Discover the speed booster

You hear about a helpful tool that makes your computer's math calculations run much faster by smart testing and tweaking.

2
💻 Prepare your calculation

You take your current math routine that feels slow and get ready to improve it.

3
🗣️ Tell the AI helper to optimize

You simply ask your AI assistant to use this special skill on your routine for a set number of improvement rounds – it feels easy and exciting!

4
✅ AI checks it works right

The AI runs tests to make sure your routine gives correct results and measures its starting speed.

5
📊 AI profiles and improves

The AI deeply analyzes performance, suggests better ways, updates your routine, and repeats tests over several rounds to find the fastest version.

🏆 Enjoy lightning-fast results

You receive the best improved routine that runs way faster, with proof of speedup and details on what made it better – your calculations now fly!

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Star Growth

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AI-Generated Review

What is cuda-optimized-skill?

Python toolkit streamlining CUDA kernel optimization for custom GPU operators. It handles correctness validation against references, precise benchmarking, Nsight Compute profiling for bottleneck analysis, and iterative tuning loops across CUDA, CUTLASS, or Triton backends. Developers get reproducible workflows with detailed reports comparing baseline to optimized kernels, like dropping softmax latency 31% via evidence-based tweaks.

Why is it gaining traction?

Combines full-stack tooling—correctness, benchmarking, targeted/full profiling, and auto-proposals—in one CLI flow, unlike scattered cuda github samples or manual Nsight runs. Strategy memory tracks positive/negative/rejected ideas to avoid repeat failures, enabling smarter cuda kernel optimization. Fits cuda github actions for CI benchmarking and analysis in real projects.

Who should use this?

GPU programmers writing custom CUDA kernels, Triton scripts, or CUTLASS ops for PyTorch extensions. ML engineers tuning operators like matmuls or softmax on specific arches (sm_80+), needing cuda kernel launch overhead reduction or memory bound fixes. Teams evaluating cuda github projects with reproducible profiling.

Verdict

Solid starter at 13 stars and 1.0% credibility—immature but docs shine with CLI examples for cuda kernel benchmarking. Grab it for iterative tuning if you're deep in custom GPU work; contribute to boost stability.

(198 words)

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