PyTorch eager
Conv2d ReLU HardSwish · width = 128 · height = 128 · batch_size = 128 · in_channels = 8 · fp32 · run 01a03f0e-f83b…
●passedReported evidence
Not re-observed recently.
Primary measurement
4.59ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 4.61 · min ms 4.58 · std ms 0.00577 · mean ms 4.59
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 128 · height = 128 · batch_size = 128 · in_channels = 8 · fp32
comparison keysha256:d05a89a80acebc55…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/57_Conv2d_ReLU_HardSwish.py
sha256:9d9395451d137e32ad276be…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width128
height128
batch_size128
in_channels8
input_1fp32 [128, 8, 128, 128]
definition comparatornot_asserted
Measurements
latency · max4.61 ms · n=100
latency · mean4.59 ms · n=100
latency · min4.58 ms · n=100
latency · std5.77 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",
"measurements": [
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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 Conv2d ReLU HardSwish →JSON