| --- |
| 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 |
|
|
| The tiers match the layout Dr.Sparse task discovery scans, so downloading |
| straight into `level_sparse/otf_test_set/` needs no staging step: |
|
|
| ```bash |
| hf download KinGeorge/Dr.Sparse-OTF-test-set --repo-type dataset \ |
| --local-dir level_sparse/otf_test_set # all 100, 11.6 GB |
| |
| ./run_b200_eval_parallel.sh --model qwen3.8 --dry-run |
| ``` |
|
|
| `level4_huge` is 8.6 GB of the total, so for a quick smoke test pull only the |
| small tiers. Note that `--include` takes **one pattern per flag** — passing |
| several after a single `--include` silently matches nothing: |
|
|
| ```bash |
| hf download KinGeorge/Dr.Sparse-OTF-test-set --repo-type dataset \ |
| --local-dir level_sparse/otf_test_set \ |
| --include "level1_small/*" --include "level2_medium/*" # 158 MB |
| ``` |
|
|
| If you already have the `.bin` files elsewhere on disk, point the staging |
| script at them instead and it will lay the tiers out as symlinks: |
|
|
| ```bash |
| python level_sparse/stage_otf_test_set.py --src /path/to/flat/bins |
| ``` |
|
|
| ## 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. |
|
|
