Add MANIFEST_v4.md

MANIFEST_v4.md

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+# 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)