PyTorch eager
Average Pooling 3D · depth = 128 · width = 256 · height = 128 · channels = 32 · batch_size = 16 · fp32 · run 01a03f0e-f6cd…
●passedReported evidence
Not re-observed recently.
Primary measurement
8.69ms±0.05 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 11.2 · min ms 8.65 · std ms 0.257 · mean ms 8.69
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddepth = 128 · width = 256 · height = 128 · channels = 32 · batch_size = 16 · fp32
comparison keysha256:17f3c00416720486…
sourceKernelBench baseline timings
external idH100_Modal/torch/level1/46_Average_Pooling_3D.py
sha256:726f1d5c818f0ba50b4254b…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
depth128
width256
height128
channels32
batch_size16
xfp32 [16, 32, 128, 128, 256]
definition comparatornot_asserted
Measurements
latency · max11.2 ms · n=100
latency · mean8.69 ms · n=100
latency · min8.65 ms · n=100
latency · std257.0 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": 8690000,
"maximum": 11200000,
"minimum": 8650000,
"confidence95": [
8650000,
8740372
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 257000,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
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"min_ms": 8.65,
"std_ms": 0.257,
"mean_ms": 8.69
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level1/46_Average_Pooling_3D.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:b75ddf8ce039808b5d77a5b3ee471992334dc9da6d474f915112260844af1dad",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:f66f5f3976d8a2d9130435189049e54d7c3927bb8b03471a1e825523e169c6a3"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l1-46-average-pooling-3d",
"title": "Average Pooling 3D · PyTorch eager · Modal"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
"name": "KernelBench timing scripts",
"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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"title": "KernelBench baseline timing"
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"environment": {
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"spec": {
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},
"software": {}
},
"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 Average Pooling 3D →JSON