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
"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/:
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.