PyTorch eager
conv depthwise 2D asymmetric input asymmetric kernel · width = 256 · height = 128 · batch_size = 32 · in_channels = 128 · fp32 · run 01a03f0e-f836…
●passedReported evidence
Not re-observed recently.
Primary measurement
3.03ms±0.00 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 3.05 · min ms 3.02 · std ms 0.00298 · mean ms 3.03
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 256 · height = 128 · batch_size = 32 · in_channels = 128 · fp32
comparison keysha256:35fb60770db0dd9f…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/85_conv_depthwise_2D_asymmetric_input_asymmetric_kernel.py
sha256:268d686646a75d43747e769…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
width256
height128
batch_size32
in_channels128
xfp32 [32, 128, 128, 256]
definition comparatornot_asserted
Measurements
latency · max3.05 ms · n=100
latency · mean3.03 ms · n=100
latency · min3.02 ms · n=100
latency · std2.98 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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"timing": {
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"latencyNs": {
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},
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},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 2980,
"metric": "latency",
"statistic": "std"
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"metadata": {
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"comparability": {
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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 conv depthwise 2D asymmetric input asymmetric kernel →JSON