PyTorch eager
Matmul Swish Sum GroupNorm · batch_size = 32768 · in_features = 1024 · fp32 · run 01a03f0e-f839…
●passedReported evidence
Not re-observed recently.
Primary measurement
12.1ms±0.09 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 12.8 · min ms 11.2 · std ms 0.455 · mean ms 12.1
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 32768 · in_features = 1024 · fp32
comparison keysha256:63d2b25bc00b7605…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/37_Matmul_Swish_Sum_GroupNorm.py
sha256:8eb6f4972561c40dd65b73c…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size32768
in_features1024
input_1fp32 [32768, 1024]
definition comparatornot_asserted
Measurements
latency · max12.8 ms · n=100
latency · mean12.1 ms · n=100
latency · min11.2 ms · n=100
latency · std455.0 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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"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
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"metadata": {
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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 Matmul Swish Sum GroupNorm →JSON