PyTorch eager
FrobeniusNorm · dim1 = 512 · dim2 = 512 · features = 64 · batch_size = 112 · fp32 · run 01a03f0e-f6cc…
●passedReported evidence
Not re-observed recently.
Primary measurement
8.53ms±0.02 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 8.67 · min ms 8.39 · std ms 0.116 · mean ms 8.53
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddim1 = 512 · dim2 = 512 · features = 64 · batch_size = 112 · fp32
comparison keysha256:9424c363735520ae…
sourceKernelBench baseline timings
external idH100_Modal/torch/level1/37_FrobeniusNorm_.py
sha256:3a3d8e7f2a553634cc1e879…
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 · max8.67 ms · n=100
latency · mean8.53 ms · n=100
latency · min8.39 ms · n=100
latency · std116.0 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": 8530000,
"maximum": 8670000,
"minimum": 8390000,
"confidence95": [
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8552736
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 116000,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
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"metrics": {
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"min_ms": 8.39,
"std_ms": 0.116,
"mean_ms": 8.53
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level1/37_FrobeniusNorm_.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:8853a8ec8031ed61521466198342ec58f3fd2aee79629f80128343b35a74b4f1",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:a2500091d1c22c0b4f74a85271f15a195069d94b76af502082b08c3a5d00bfb4"
},
"metadata": {
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"title": "FrobeniusNorm · PyTorch eager · Modal"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
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"repository": "https://github.com/ScalingIntelligence/KernelBench"
},
"measurement": {
"timer": "cuda_events",
"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 FrobeniusNorm →JSON