megakernel
A megakernel compiles a whole computation — up to an entire model's forward pass — into a single persistent GPU kernel: one launch, one resident working set, an in-kernel scheduler, instead of thousands of separate kernel launches bouncing activations through device memory.
Today this package ships a probe that measures, on your GPU, the two overheads a megakernel removes:
pip install megakernel
python -m megakernel
device : <your GPU>
kernel enqueue cost : x.xx us (CPU-side submit)
kernel launch tax : x.xx us (end-to-end, tiny kernel)
device copy bandwidth: xxxx.x GB/s
A decode step composed of ~500 kernel launches pays roughly y.yy ms/token
in launch tax alone, before any math or memory traffic. A megakernel pays
it once.
Requires a CUDA build of PyTorch (the probe reports a clear message
otherwise). Also usable as a library: from megakernel import probe.
Roadmap
This name is the future home of a residency-first megakernel compiler currently in development: real models compiled to single persistent kernels, adoptable without rewriting anything. Watch this package for the first public release.
Contact: goinyadeng976@gmail.com
1 maintainer
Metadata
1 maintainer
Release files for megakernel 0.1.0
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