AethronPhantom/pyc-kernels

PyC CUDA kernel lab

This repository documents 19 CUDA kernel-lab entries from PyC. It is a source and evidence release, not a compiled binary distribution and not a claim that all entries are wired into PyC runtime dispatch.

Contents

• —kernels/prototypes/: standalone CUDA prototype sources.
• —manifests/lab_kernels.json: the 19-entry lab catalog, including build/run commands.
• —manifests/registry_kernels.json: the catalog mirrored into the registry release.
• —PERFORMANCE_SUMMARY.md: selected H100 campaign measurements and the optimization progression.

Optimization themes

The progression covers shared-memory tiling, WMMA Tensor Core execution, BF16 versus FP16, cp.async double buffering, CTA shape, K-stage depth, warp work assignment, and cuBLASLt as a hardware-library ceiling/control.

Performance numbers are campaign-specific measurements. They should be read with the GPU, CUDA toolchain, matrix shape, correctness mode, and timing method from the accompanying evidence; they are not universal benchmarks.

Reproduce

The commands in manifests/kernels.json use {nvcc}, {source}, and {build_dir} placeholders. Replace them with a CUDA 12.x toolchain, a suitable Hopper or Ada GPU, and a local build directory before running.