PyTorch eager
Conv3d Softmax MaxPool MaxPool · depth = 16 · width = 32 · height = 32 · batch_size = 128 · in_channels = 3 · fp32 · run 01a03f0e-f6d2…
●passedReported evidence
Not re-observed recently.
Primary measurement
864.0µs±2.57 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 0.993 · min ms 0.86 · std ms 0.0131 · mean ms 0.864
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddepth = 16 · width = 32 · height = 32 · batch_size = 128 · in_channels = 3 · fp32
comparison keysha256:5d86f64200b19aba…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/6_Conv3d_Softmax_MaxPool_MaxPool.py
sha256:279100f0b4c96a08c517170…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
depth16
width32
height32
batch_size128
in_channels3
input_1fp32 [128, 3, 16, 32, 32]
definition comparatornot_asserted
Measurements
latency · max993.0 µs · n=100
latency · mean864.0 µs · n=100
latency · min860.0 µs · n=100
latency · std13.1 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": {
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"timing": {
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"latencyNs": {
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"maximum": 993000,
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]
},
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},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
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},
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"metadata": {
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"title": "Conv3d Softmax MaxPool MaxPool · PyTorch eager · Modal"
},
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"comparability": {
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}
}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Conv3d Softmax MaxPool MaxPool →JSON