KernelSight Dataset v4.0
Camera-ready dataset for the CS231N project report.
Overview
|
|
| Snapshots |
1,444 |
| Tensor shape |
[24, 512] (24 channels, 512 time bins) |
| Label vocab |
12 L1 classes, 73 L2 classes |
| Segments |
45,860 labeled segments |
| Overlap ground truth |
472 snapshots (has_overlap=1) |
| Splits |
train 1,124 / val 160 / test 160 |
| CI |
26,996 passed, 0 failed |
Corpus composition
| Source |
Motif |
Snapshots |
L1 classes |
Notes |
| Microbenchmark |
vector_add |
20 |
elementwise |
coalesced BW-bound |
| Microbenchmark |
gather |
17 |
memory_movement |
random-indexed |
| Microbenchmark |
reduction |
16 |
reduction |
tree + atomic |
| Microbenchmark |
scatter |
31 |
memory_movement |
atomic histogram |
| Microbenchmark |
wgmma |
1 |
matmul |
tensor-core GEMM |
| KernelBench |
kernelbench |
480 |
all 11 populated |
PyTorch L1+L2 ops |
| CUTLASS |
cutlass_gemm |
278 |
matmul |
ex48 TF32 WS-GEMM |
| CUTLASS |
cutlass_fmha |
85 |
attention |
ex88 FA3 |
| CUTLASS |
cutlass_ws_overlap |
472 |
matmul (overlap) |
ex48 + %globaltimer markers |
| CUTLASS |
cutlass_fp8_gemm |
14 |
matmul |
ex54 FP8 WS-GEMM |
| CUTLASS |
cutlass_sparse_gemm |
18 |
matmul |
ex62 2:4 structured sparsity |
| CUTLASS |
cutlass_grouped_gemm |
12 |
matmul |
ex57 grouped GEMM |
Per-L1 distribution (snapshots containing each class)
| L1 class |
snapshots |
segments |
| matmul |
849 |
3,257 |
| activation |
147 |
11,654 |
| reduction |
125 |
7,155 |
| conv |
98 |
6,418 |
| attention |
92 |
295 |
| elementwise |
86 |
2,887 |
| normalization |
79 |
6,546 |
| pooling |
62 |
2,019 |
| memory_movement |
48 |
48 |
| loss |
42 |
4,860 |
| softmax |
28 |
721 |
Per-snapshot artifacts
Each snapshot directory contains:
<variant>/
input/tensor_input.npz 24-channel [24, 512] profiler heatmap
labels/labels.npz L1+L2 workload labels, segments, multi-hot
fingerprint/fingerprint.npz 32-D instruction-mix fingerprint
Splits
| Split |
n |
Description |
train.json |
1,124 |
80% L2-stratified training set |
val.json |
160 |
10% validation |
test.json |
160 |
10% test |
iid.json |
433 |
30% random IID sample (overlapping tag) |
param_ood.json |
956 |
geometry-sweep variants (overlapping tag) |
composed.json |
1,124 |
multi-kernel traces (overlapping tag) |
Tools included
workload_taxonomy.py — L1/L2 vocab, anchor overrides (single source of truth)sass_dataloader_stub.py — PyTorch Dataset for loading tensor+labels
Changes from v3.1
- Deleted
megakernel (PoC, 1 snapshot) and tiled_gemm_poc (hand-written PoC, 590 snapshots)
- Added 3 new CUTLASS Hopper GEMM datapaths: FP8 (ex54), 2:4 sparse (ex62), grouped (ex57)
- Selective KernelBench expansion: activation, normalization, pooling, reduction, elementwise
- Microbenchmark geometry sweep: vector_add, gather, reduction, scatter
- CUTLASS geometry sweep: cutlass_gemm (264), cutlass_ws_overlap (456), cutlass_fmha (72)
- Corpus grew from 262 (v3.1) to 1,444 (v4.0); overlap ground truth from 29 to 472
- CI: 26,996 passed, 0 failed
Collection environment
- GPU: NVIDIA H100 80GB HBM3 (sm_90a)
- Profilers: CUPTI Range Profiler (19-metric warp-stall set), NVBit region profiler, Nsight Systems
- CUTLASS: Hopper examples from the CUTLASS 3.x tree (build_ex48 / build_ex88)
