PyTorch eager
ConvTranspose2d Add Min GELU Multiply · width = 64 · height = 64 · batch_size = 128 · in_channels = 64 · fp32 · run 01a03f0e-f83e…
●passedReported evidence
Not re-observed recently.
Primary measurement
8.78ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 8.82 · min ms 8.72 · std ms 0.02 · mean ms 8.78
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 64 · height = 64 · batch_size = 128 · in_channels = 64 · fp32
comparison keysha256:caa8848f7748fad4…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/93_ConvTranspose2d_Add_Min_GELU_Multiply.py
sha256:63d316e00f8125571e0c6cf…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width64
height64
batch_size128
in_channels64
input_1fp32 [128, 64, 64, 64]
definition comparatornot_asserted
Measurements
latency · max8.82 ms · n=100
latency · mean8.78 ms · n=100
latency · min8.72 ms · n=100
latency · std20.0 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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{
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"observedAt": "2026-03-05T08:38:15.000Z",
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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 ConvTranspose2d Add Min GELU Multiply →JSON