PyTorch eager
Conv2d InstanceNorm Divide · width = 128 · height = 128 · batch_size = 128 · in_channels = 64 · fp32 · run 01a03f0e-f838…
●passedReported evidence
Not re-observed recently.
Primary measurement
7.45ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 7.49 · min ms 7.38 · std ms 0.0185 · mean ms 7.45
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 128 · height = 128 · batch_size = 128 · in_channels = 64 · fp32
comparison keysha256:56e15004fa63c50a…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/17_Conv2d_InstanceNorm_Divide.py
sha256:a6fb67fdba8b115d08270b4…
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 · max7.49 ms · n=100
latency · mean7.45 ms · n=100
latency · min7.38 ms · n=100
latency · std18.5 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 InstanceNorm Divide →JSON