---
license: apache-2.0
tags:
- cuda
- gpu-optimization
- kernels
- gemm
- hpc
---

# 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.