Mog9 / kernel-fusion

What is kernel-fusion?

This CUDA Python project fuses ReLU and LayerNorm into one GPU kernel using CuPy, slashing global memory traffic compared to separate CuPy ops. Run `pip install cupy-cuda12x matplotlib` then `python main.py` to benchmark both versions on your NVIDIA GPU, printing a results table and saving a plot showing ~5x speedup on RTX 3050. It's a hands-on cuda python example proving kernel fusion boosts throughput by minimizing memory round trips in deep learning pipelines.

Why is it gaining traction?

Unlike scattered cuda github samples, it delivers instant, reproducible benchmarks with CUDA events for precise timing and bandwidth calcs—no setup hassles beyond cuda python pip install. Developers grab it as a cuda python tutorial or github cuda projects starter for kernel fusion, an effective method for better power efficiency on multithreaded GPUs. The plot and table make fusion benefits visually pop without digging into cuda python api docs.

Who should use this?

ML engineers tuning transformer layers where ReLU+LayerNorm chains bottleneck bandwidth. CUDA beginners via this cuda github example to grasp kernel fusion before custom kernels. Researchers validating cuda python version compatibility on consumer GPUs like RTX series.

Verdict

Solid cuda github tutorial for learning kernel fusion—run it to see real speedups—but with 11 stars and 1.0% credibility score, it's an early experiment, not production-ready. Fork and extend if you're prototyping GPU ops.

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