PyTorch eager
ConvTranspose2d GlobalAvgPool BiasAdd LogSumExp Sum Multiply · width = 512 · height = 512 · batch_size = 16 · in_channels = 64 · fp32 · run 01a03f0e-f6d4…
●passedReported evidence
Not re-observed recently.
Primary measurement
7.17ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 7.22 · min ms 7.14 · std ms 0.0118 · mean ms 7.17
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 512 · height = 512 · batch_size = 16 · in_channels = 64 · fp32
comparison keysha256:686910cd32c3dbbb…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/42_ConvTranspose2d_GlobalAvgPool_BiasAdd_LogSumExp_Sum_Multiply.py
sha256:43e0cbe2145a26a67b793ea…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width512
height512
batch_size16
in_channels64
input_1fp32 [16, 64, 512, 512]
definition comparatornot_asserted
Measurements
latency · max7.22 ms · n=100
latency · mean7.17 ms · n=100
latency · min7.14 ms · n=100
latency · std11.8 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": 7220000,
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"confidence95": [
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]
},
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},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 11800,
"metric": "latency",
"statistic": "std"
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},
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"metadata": {
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"title": "ConvTranspose2d GlobalAvgPool BiasAdd LogSumExp Sum Multiply · PyTorch eager · Modal"
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"comparability": {
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"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 ConvTranspose2d GlobalAvgPool BiasAdd LogSumExp Sum Multiply →JSON