| --- |
| pretty_name: KernelBench-M |
| language: |
| - en |
| tags: |
| - mutation-testing |
| - cuda |
| - gpu-kernels |
| - benchmark-auditing |
| - code-generation |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # KernelBench-M |
|
|
| The measurement artifact for *Measuring the Checker: Mutation Analysis for |
| GPU-Kernel Benchmark Oracles*: the mutation operators, the verified CUDA |
| substrates they mutate, the kill witnesses, and the pipeline that produced |
| every number in the paper. |
|
|
| ## Layout |
|
|
| ``` |
| rules/ 124 mutation rules, six families (mutator.py loads all of them) |
| substrates/ 208 gate-verified CUDA implementations, one per KernelBench |
| problem: the mutation targets. Never used as oracle. |
| witnesses/ per-problem kill records: for every mutant, its rule, family, |
| mutated site, whether the official protocol kills it, and — when |
| it survives — a suite that does |
| pipeline/ the measurement code (screening, kill matrix, audit, set cover) |
| summary.json per-problem mutant / witnessed / official-kill counts |
| ``` |
|
|
| Supporting records: `mechanism_curves.json` (Fig. 2b–c), `holdout.json` |
| (dev/test split), `ladder_summary.json` (knowledge ladder), |
| `gate_report.json` (substrate admission), `invalid_suites.json` (suites |
| rejected for crossing the validity ceiling). |
|
|
| ## What a substrate is, and is not |
|
|
| KernelBench ships PyTorch references whose GPU execution bottoms out in closed |
| cuDNN/cuBLAS binaries, so there is no source to mutate. A *substrate* is a |
| correct CUDA implementation of the same computation, used **only** as a |
| mutation target. The oracle stays the benchmark's own PyTorch reference. Each |
| substrate is admitted by `pipeline/gate_candidates.py`, which compares it |
| against that reference on every suite before it may enter the pool |
| (`gate_report.json` records admissions and rejections). |
|
|
| ## Witness format |
|
|
| ```json |
| "softmax:barrier-drop:3": { |
| "rule": "barrier-drop", "family": "sync", |
| "site": "__syncthreads();", |
| "cls": "high_value", |
| "killed_by_official": false, |
| "witness": "T2_misaligned_batch" |
| } |
| ``` |
|
|
| `cls` is `killed_by_T0` (the official inputs detect it), `high_value` (survives |
| the official inputs, a targeted input detects it), or `no_witness` (nothing we |
| have detects it — an equivalence or oracle-blindness candidate, excluded from |
| every denominator in the paper). |
|
|
| ## Reproducing |
|
|
| Requires an NVIDIA GPU with CUDA 12.x and PyTorch. From `pipeline/`: |
|
|
| ```bash |
| python screen_mutants.py # generate, filter, and screen mutants |
| python full_matrix.py # kill matrix over all suites |
| python audit_matrix.py # score competing protocols (§5) |
| python analyze_cover.py # set-cover suite synthesis (§6) |
| python analyze_holdout.py # dev/test holdout (§6) |
| ``` |
|
|
| Each stage writes append-only JSONL journals and resumes from them, so a |
| crashed or preempted run can simply be restarted. `SHARD_ID`/`SHARD_N` split |
| problems across GPUs; `STEAL=1` lets an idle worker pick up unclaimed problems. |
|
|
| ## Caveats |
|
|
| Mutation score is adequacy relative to this fault model, not absolute |
| correctness: these operators cannot express faults living in structures the |
| substrates do not contain (tensor-core paths, double-buffered pipelines), nor |
| multi-site interactions. The artifact supports comparative claims between |
| protocols and existence claims about specific faults. It cannot certify that a |
| kernel passing these suites is correct. |
|
|
