Dataset card, metadata, and level1_small tier

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - other
+tags:
+  - sparse-matrix
+  - cuda
+  - gpu-kernels
+  - spmv
+  - spmm
+  - spgemm
+  - suitesparse
+  - benchmark
+pretty_name: Dr.Sparse OTF Test Set
+size_categories:
+  - n<1K
+configs:
+  - config_name: summary
+    data_files: summary.csv
+---
+
+# Dr.Sparse OTF Test Set
+
+100 sparse matrices from the [SuiteSparse Matrix Collection](https://sparse.tamu.edu/),
+converted to the flat binary format the [Dr.Sparse](https://github.com/NKU-Yang/Dr.Sparse)
+benchmark harness reads. This is the held-out evaluation set for LLM-generated
+CUDA sparse kernels (SpMV / SpMM / SpGEMM), kept separate from the matrices the
+models were developed against.
+
+## Layout
+
+Matrices are grouped into size tiers by row count, the convention Dr.Sparse task
+discovery scans for:
+
+| tier | rows | matrices | size |
+|---|---|---:|---:|
+| `level1_small`  | < 3,500            |  8 | 2.6 MB |
+| `level2_medium` | 3,500 – 34,000     | 35 | 155 MB |
+| `level3_large`  | 34,000 – 1,100,000 | 38 | 2.1 GB |
+| `level4_huge`   | > 1,100,000        | 19 | 8.6 GB |
+
+`summary.csv` and `manifest.json` carry per-matrix `rows`, `cols`, `nnz`, `kind`,
+`square`, `avg_row` and `density`. Row counts span 800 to 67.7M and nnz spans
+10.4K to 138.8M.
+
+## Binary format
+
+Each `.bin` is CSR plus a dense vector, little-endian, no padding:
+
+```
+int32   num_rows
+int32   num_cols
+int32   nnz
+int32   row_ptr[num_rows + 1]
+int32   col_ind[nnz]
+float32 values[nnz]
+float32 x[num_cols]          # dense RHS vector, for SpMV
+```
+
+Reading one in Python:
+
+```python
+import numpy as np
+
+def read_bin(path):
+    with open(path, "rb") as f:
+        rows, cols, nnz = np.fromfile(f, dtype=np.int32, count=3)
+        row_ptr = np.fromfile(f, dtype=np.int32, count=rows + 1)
+        col_ind = np.fromfile(f, dtype=np.int32, count=nnz)
+        values  = np.fromfile(f, dtype=np.float32, count=nnz)
+        x       = np.fromfile(f, dtype=np.float32, count=cols)
+    return rows, cols, nnz, row_ptr, col_ind, values, x
+```
+
+The C++ side of the harness reads the same layout in `level_sparse/data_loader.h`.
+
+## Use with Dr.Sparse
+
+```bash
+hf download KinGeorge/Dr.Sparse-OTF-test-set --repo-type dataset \
+    --local-dir /path/to/otf_test_set
+
+# point the harness at it
+python level_sparse/stage_otf_test_set.py --src /path/to/otf_test_set
+./run_b200_eval_parallel.sh --model qwen3.8 --dry-run
+```
+
+The tiers here already match the layout discovery expects, so you can also
+unpack straight into `level_sparse/otf_test_set/` and skip the staging step.
+
+## Provenance and licence
+
+Matrices are redistributed from the SuiteSparse Matrix Collection (Davis & Hu,
+*The University of Florida Sparse Matrix Collection*, ACM TOMS 38(1), 2011),
+whose entries are CC BY 4.0. Only the storage format was changed: the `.mtx`
+sources were converted to CSR (single precision) and a dense `x` vector drawn
+from a standard normal was appended so SpMV has a fixed right-hand side. That
+vector is baked into each file, so results are reproducible across runs, but it
+was not generated from a recorded seed and is not reproducible from the `.mtx`
+source alone. Matrix values are otherwise unmodified. Please cite SuiteSparse if
+you use this set.

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+}

summary.csv

@@ -0,0 +1,101 @@
+matrix,rows,cols,nnz,kind,tier,square,avg_row,density
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+GHS_indef_bloweybq,10001,10001,69991,materials problem,10000-1000000,True,7.0,0.0006997700389945007
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+AG-Monien_se,32768,32768,98300,undirected graph sequence,10000-1000000,True,3.0,9.154900908470154e-05
+SNAP_wiki-Vote,8297,8297,103689,directed graph,10000-1000000,True,12.5,0.0015062272690078557
+JGD_Margulies_cat_ears_4_4,19020,44448,132888,combinatorial problem,10000-1000000,False,7.0,0.00015718931759907157
+DRIVCAV_cavity19,4562,4562,138187,subsequent computational fluid dynamics problem,10000-1000000,True,30.3,0.006639824899706148
+DRIVCAV_cavity16,4562,4562,138187,computational fluid dynamics problem sequence,10000-1000000,True,30.3,0.006639824899706148
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+Mallya_lhr10c,10672,10672,232633,chemical process simulation problem,10000-1000000,True,21.8,0.0020425828830337453
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+Nemeth_nemeth13,9506,9506,474472,subsequent theoretical/quantum chemistry problem,10000-1000000,True,49.9,0.005250672955776345
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+GHS_indef_olesnik0,88263,88263,744216,2D/3D problem,10000-1000000,True,8.4,9.553040815143145e-05
+Lourakis_bundle1,10581,10581,770901,computer graphics/vision problem,10000-1000000,True,72.9,0.006885653752317809
+DIMACS10_vsp_mod2_pgp2_slptsk,101364,101364,778736,random unweighted graph,10000-1000000,True,7.7,7.579189602451269e-05
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+Li_pli,22695,22695,1350309,duplicate electromagnetics problem,1000000-10000000,True,59.5,0.002621638390758102
+Botonakis_thermomech_dM,204316,204316,1423116,thermal problem,1000000-10000000,True,7.0,3.40906707478806e-05
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+VLSI_mosfet2,46994,46994,1499460,semiconductor device problem,1000000-10000000,True,31.9,0.0006789691779870956
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+Simon_venkat01,62424,62424,1717792,computational fluid dynamics problem sequence,1000000-10000000,True,27.5,0.00044082618946248277
+Boeing_pcrystk03,24696,24696,1751178,duplicate materials problem,1000000-10000000,True,70.9,0.0028712900079741277
+JGD_GL7d_GL7d14,171375,47271,1831183,combinatorial problem,1000000-10000000,False,10.7,0.0002260421816933049
+Simon_appu,14000,14000,1853104,directed weighted random graph,1000000-10000000,True,132.4,0.009454612244897959
+GHS_indef_darcy003,389874,389874,2101242,2D/3D problem,1000000-10000000,True,5.4,1.3823802655215408e-05
+FEMLAB_poisson3Db,85623,85623,2374949,computational fluid dynamics problem,1000000-10000000,True,27.7,0.0003239465860221328
+ANSYS_Delor295K,295734,1823928,2401323,least squares problem,1000000-10000000,False,8.1,4.4518613766851075e-06
+DIMACS10_vsp_msc10848_300sep_100in_1Kout,21996,21996,2442056,random unweighted graph,1000000-10000000,True,111.0,0.005047405501259781
+Bodendiek_CurlCurl_1,226451,226451,2472071,model reduction problem,1000000-10000000,True,10.9,4.820726071118466e-05
+Freescale_nxp1,414604,414604,2656177,circuit simulation problem,1000000-10000000,True,6.4,1.545218988311903e-05
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+Ronis_xenon2,157464,157464,3866688,materials problem,1000000-10000000,True,24.6,0.00015594683739093623
+Oberwolfach_t3dh_e,79171,79171,4352105,duplicate model reduction problem,1000000-10000000,True,55.0,0.0006943318760478372
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+Schenk_ISEI_para-5,155924,155924,5416358,semiconductor device problem sequence,1000000-10000000,True,34.7,0.00022278268146573585
+SNAP_com-Youtube,1134890,1134890,5975248,undirected graph with communities,1000000-10000000,True,5.3,4.639256625391026e-06
+DIMACS10_rgg_n_2_19_s0,524288,524288,6539532,undirected random graph,1000000-10000000,True,12.5,2.3790678824298084e-05
+Mittelmann_cont1_l,1918399,1921596,7031999,linear programming problem,1000000-10000000,False,3.7,1.9075581018724525e-06
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+BenElechi_BenElechi1,245874,245874,13150496,2D/3D problem,10000000-60000000,True,53.5,0.00021752887825918059
+Bodendiek_CurlCurl_3,1219574,1219574,13544618,model reduction problem,10000000-60000000,True,11.1,9.106478087191435e-06
+TSOPF_TSOPF_RS_b2383_c1,38120,38120,16171169,power network problem,10000000-60000000,True,424.2,0.011128474420535361
+Schenk_AFE_af_shell6,504855,504855,17588875,subsequent structural problem,10000000-60000000,True,34.8,6.900884185463665e-05
+DIMACS10_333SP,3712815,3712815,22217266,undirected graph,10000000-60000000,True,6.0,1.611699216330274e-06
+Freescale_FullChip,2987012,2987012,26621990,circuit simulation problem,10000000-60000000,True,8.9,2.9837785070487592e-06
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+Schenk_nlpkkt80,1062400,1062400,28704672,optimization problem,10000000-60000000,True,27.0,2.5431762819349687e-05
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+JGD_GL7d_GL7d20,1437547,1911130,29893084,combinatorial problem,10000000-60000000,False,20.8,1.088074017964895e-05
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+Hardesty_Hardesty3,8217820,7591564,40451632,computer graphics/vision problem,10000000-60000000,False,4.9,6.484077030699295e-07
+DIMACS10_kron_g500-logn19,524288,524288,43562265,undirected multigraph,10000000-60000000,True,83.1,0.00015847859685891308
+Dziekonski_dielFilterV2real,1157456,1157456,48538952,electromagnetics problem,10000000-60000000,True,41.9,3.623109119606349e-05
+GAP_GAP-road,23947347,23947347,57708624,directed weighted graph,10000000-60000000,True,2.4,1.0062963668243868e-07
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+LAW_ljournal-2008,5363260,5363260,79023142,directed graph,60000000-150000000,True,14.7,2.747239935813516e-06
+DIMACS10_rgg_n_2_23_s0,8388608,8388608,127002786,undirected random graph,60000000-150000000,True,15.1,1.804818140271891e-06
+Janna_Cube_Coup_dt0,2164760,2164760,127206144,structural problem,60000000-150000000,True,58.8,2.714492080882763e-05
+GenBank_kmer_U1a,67716231,67716231,138778562,undirected graph,60000000-150000000,True,2.0,3.026473164694778e-08