PyTorch eager
InstanceNorm · dim1 = 512 · dim2 = 512 · features = 64 · batch_size = 112 · fp32 · run 01a03f0e-f6cc…
●passedReported evidence
Not re-observed recently.
Primary measurement
9.61ms±0.08 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 13.6 · min ms 9.51 · std ms 0.409 · mean ms 9.61
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddim1 = 512 · dim2 = 512 · features = 64 · batch_size = 112 · fp32
comparison keysha256:8dbac24687e49c1e…
sourceKernelBench baseline timings
external idH100_Modal/torch/level1/34_InstanceNorm.py
sha256:df7bd1ae0be644738d223c3…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
dim1512
dim2512
features64
batch_size112
xfp32 [112, 64, 512, 512]
definition comparatornot_asserted
Measurements
latency · max13.6 ms · n=100
latency · mean9.61 ms · n=100
latency · min9.51 ms · n=100
latency · std409.0 µs
Protocol
harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing
Environment
gpuNVIDIA H100 (sm_90)
Artifacts
No artifacts published with this run.
Replications and notes
No attestations yet.
Community attestations. They never change the evidence level; only a KernelIndex-controlled rerun does.
Add a reproduction or note
Canonical manifest
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{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 9610000,
"maximum": 13600000,
"minimum": 9510000,
"confidence95": [
9529836,
9690164
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 409000,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
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"metrics": {
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"min_ms": 9.51,
"std_ms": 0.409,
"mean_ms": 9.61
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level1/34_InstanceNorm.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:4d50cacedc06e4e851ff7f67bcf2e2b893df1edfbeb029ba1cb2e85f2dae0fd6",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:d74a9bc00917bdc521e7445aeca150b3eccdf041f09fa9c69edc14632b088437"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l1-34-instancenorm",
"title": "InstanceNorm · PyTorch eager · Modal"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
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"repository": "https://github.com/ScalingIntelligence/KernelBench"
},
"measurement": {
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"primaryStatistic": "mean"
},
"comparability": {
"notes": "Mean of 100 timed forward passes (CUDA events, warm-up excluded) of the reference module on fixed inputs; the 95% interval is the mean's, from the reported standard deviation. The upstream JSON records no torch or CUDA version. Comparable only within one problem and one timing host.",
"family": "kernelbench_baseline_timing"
}
},
"metadata": {
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"software": {}
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"metadata": {
"name": "kernelbench-h100-modal",
"title": "KernelBench host · H100 80GB HBM3 (Modal)"
},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for InstanceNorm →JSON