PyTorch eager
ConvTranspose3d Scale BatchNorm GlobalAvgPool · depth = 16 · width = 32 · height = 32 · batch_size = 16 · in_channels = 64 · fp32 · run 01a03f0e-f6d7…
●passedReported evidence
Not re-observed recently.
Primary measurement
5.42ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 5.51 · min ms 5.33 · std ms 0.035 · mean ms 5.42
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddepth = 16 · width = 32 · height = 32 · batch_size = 16 · in_channels = 64 · fp32
comparison keysha256:15ed10ea20fd87c9…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/77_ConvTranspose3d_Scale_BatchNorm_GlobalAvgPool.py
sha256:2a449ecc7f0262fdd303db1…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
depth16
width32
height32
batch_size16
in_channels64
input_1fp32 [16, 64, 16, 32, 32]
definition comparatornot_asserted
Measurements
latency · max5.51 ms · n=100
latency · mean5.42 ms · n=100
latency · min5.33 ms · n=100
latency · std35.0 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": {
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"spec": {
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"timing": {
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"latencyNs": {
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"confidence95": [
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},
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},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
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"statistic": "std"
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},
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"metadata": {
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"title": "ConvTranspose3d Scale BatchNorm GlobalAvgPool · 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 ConvTranspose3d Scale BatchNorm GlobalAvgPool →JSON