PyTorch eager
Conv2d Divide LeakyReLU · width = 128 · height = 128 · batch_size = 128 · in_channels = 8 · fp32 · run 01a03f0e-f6d6…
●passedReported evidence
Not re-observed recently.
Primary measurement
1.80ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 1.8 · min ms 1.79 · std ms 0.00387 · mean ms 1.8
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 128 · height = 128 · batch_size = 128 · in_channels = 8 · fp32
comparison keysha256:1d9b5850f107e324…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/71_Conv2d_Divide_LeakyReLU.py
sha256:c63ccb02a29677dd4a8717c…
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 · max1.80 ms · n=100
latency · mean1.80 ms · n=100
latency · min1.79 ms · n=100
latency · std3.87 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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"timing": {
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"latencyNs": {
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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 Divide LeakyReLU →JSON