hopper_tensor_core_bf16_warp2n
Hopper BF16 perf lane with 8 warps per CTA and each warp computing two N fragments to increase work per warp.
This repository contains the standalone CUDA source for the hopper_tensor_core_bf16_warp2n lane from
the PyC kernel lab. It is a source artifact for inspection and benchmarking;
it is not a precompiled binary and the result below is not a universal ranking.
Performance
| Kernel | GPU / architecture | Shape | Best recorded result | Evidence |
|---|---|---|---|---|
hopper_tensor_core_bf16_warp2n |
sm90 | 4096x4096x4096 | 5.571 ms / 24.672 TFLOPS | Measured on sm90, shape 4096x4096x4096; evidence hopper-bf16-nextloop-wmma-20260421T193831Z.json. |
The result is reported with the original campaign's timing and correctness context. Compare kernels only when GPU, CUDA version, matrix shape, warmup, repeats, and reference/correctness mode match.
Source
kernel.cu— copied fromkernels/prototypes/hopper/tensor_core/kernel.cu.- Original lane tags:
cuda, matmul, hopper, sm90, prototype, tensor-core, bf16, perf, next-loop, warp2n.
Build/run contract
{nvcc} -O3 -std=c++17 -lineinfo -DPYC_HOPPER_TENSOR_CORE_USE_BF16=1 -DPYC_HOPPER_TC_WARP_COL_TILES=2 -DPYC_HOPPER_TC_WARP_ROW_GROUPS=4 -DPYC_HOPPER_TC_WARP_COL_GROUPS=2 -DPYC_HOPPER_TC_SHARED_PAD_A=8 -DPYC_HOPPER_TC_SHARED_PAD_B=8 -gencode arch=compute_90,code=sm_90 -gencode arch=compute_90,code=compute_90 {source} -o {build_dir}/{name}
{build_dir}/{name} 4096 4096 4096 3 30 1
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