| # KernelSight Dataset v4.0 |
|
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| Camera-ready dataset for the CS231N project report. |
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|
| ## Overview |
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| | | | |
| |---|---| |
| | **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 | |
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|
| ## Corpus composition |
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| | 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 | |
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| ## Per-L1 distribution (snapshots containing each class) |
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| | 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 |
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| 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 |
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| - 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 |
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| ## Collection environment |
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| - 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) |
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