PyTorch eager
Conv2d Subtract HardSwish MaxPool Mish · width = 128 · height = 128 · batch_size = 128 · in_channels = 64 · fp32 · run 01a03f0e-f839…
●passedReported evidence
Not re-observed recently.
Primary measurement
7.93ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 8.23 · min ms 7.85 · std ms 0.0535 · mean ms 7.93
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 128 · height = 128 · batch_size = 128 · in_channels = 64 · fp32
comparison keysha256:8e1996f0560e30d2…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/35_Conv2d_Subtract_HardSwish_MaxPool_Mish.py
sha256:50eb954d46196a36671e421…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width128
height128
batch_size128
in_channels64
input_1fp32 [128, 64, 128, 128]
definition comparatornot_asserted
Measurements
latency · max8.23 ms · n=100
latency · mean7.93 ms · n=100
latency · min7.85 ms · n=100
latency · std53.5 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.
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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 Conv2d Subtract HardSwish MaxPool Mish →JSON