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license: apache-2.0
tags:
- cuda
- gpu-optimization
- kernels
- gemm
- hpc
---
# PyC CUDA Kernels
Twenty CUDA `.cu` prototype files from the PyC kernel lab. The collection
covers baseline and experimental matmul, Ada FP32/Tensor Core variants, Hopper
WMMA BF16/FP16 variants, asynchronous staging, CTA/warp/K-stage experiments,
and cuBLASLt control lanes.
These are source artifacts for inspection, compilation, and performance
engineering—not precompiled model weights and not a claim of universal
benchmark superiority. The 19 documented lab lanes are:
| Kernel | Short description | Source |
|---|---|---|
| `matrix_mult` | Compile-only CUDA matmul baseline. | `kernels/prototypes/baseline/matmul/kernel.cu` |
| `tokenizer_kernel` | Experimental tokenizer/matrix kernel. | `kernels/prototypes/experimental/tokenizer_matmul/kernel.cu` |
| `ada_gemm` | Ada FP32 shared-memory GEMM. | `kernels/prototypes/ada/gemm/kernel.cu` |
| `ada_gemm_k64_warp32_async` | Ada FP32 GEMM with `cp.async` double buffering. | `kernels/prototypes/ada/gemm_k64_warp32_async/kernel.cu` |
| `ada_tensor_core_fp16` | Ada WMMA FP16 GEMM. | `kernels/prototypes/ada/tensor_core/kernel.cu` |
| `ada_tensor_core_bf16` | Ada WMMA BF16 GEMM. | `kernels/prototypes/ada/tensor_core/kernel.cu` |
| `hopper_tensor_core_fp16` | Hopper 64×64×16 WMMA FP16 GEMM. | `kernels/prototypes/hopper/tensor_core/kernel.cu` |
| `hopper_tensor_core_bf16` | Hopper 64×64×16 WMMA BF16 GEMM. | `kernels/prototypes/hopper/tensor_core/kernel.cu` |
| `hopper_tensor_core_bf16_perf` | Large-shape Hopper BF16 throughput lane. | `kernels/prototypes/hopper/tensor_core/kernel.cu` |
| `hopper_tensor_core_bf16_warp2n` | Hopper lane with two N fragments per warp. | `kernels/prototypes/hopper/tensor_core/kernel.cu` |
| `hopper_tensor_core_bf16_k32` | Hopper lane with 32-wide K staging. | `kernels/prototypes/hopper/tensor_core/kernel.cu` |
| `hopper_tensor_core_bf16_warp2n_k32` | Combined warp2n and K32 experiment. | `kernels/prototypes/hopper/tensor_core/kernel.cu` |
| `hopper_cublaslt_bf16` | cuBLASLt BF16 hardware-ceiling control. | `kernels/prototypes/hopper/cublaslt_bf16/kernel.cu` |
| `hopper_cublaslt_bf16_check` | cuBLASLt BF16 correctness lane. | `kernels/prototypes/hopper/cublaslt_bf16/kernel.cu` |
| `hopper_tensor_core_bf16_async` | Hopper BF16 WMMA with async double buffering. | `kernels/prototypes/hopper/tensor_core_async/kernel.cu` |
| `hopper_tensor_core_bf16_async_wide` | Async WMMA with a wider 64×128 CTA. | `kernels/prototypes/hopper/tensor_core_async/kernel.cu` |
| `hopper_tensor_core_bf16_async_wide_k64` | Wide async lane with K64 stages. | `kernels/prototypes/hopper/tensor_core_async/kernel.cu` |
| `hopper_tensor_core_bf16_async_square` | Async WMMA with a 128×128 CTA. | `kernels/prototypes/hopper/tensor_core_async/kernel.cu` |
| `hopper_tensor_core_bf16_async_square_k64` | Square async WMMA with 128×128×64 tiles. | `kernels/prototypes/hopper/tensor_core_async/kernel.cu` |
The source files are organized by prototype family; several documented lanes
share one parameterized `.cu` implementation. See the companion
[performance Space](https://huggingface.co/spaces/AethronPhantom/pyc-kernel-lab)
for the optimization story and selected measurements.
|