torch.compile (inductor)
Matmul MaxPool Sum Scale · batch_size = 128 · in_features = 32768 · fp32 · run 01a03f0e-f84e…
●passedReported evidence
Not re-observed recently.
Primary measurement
10.7ms±0.09 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 11.5 · min ms 9.46 · std ms 0.463 · mean ms 10.7
Identity
implementationtorch.compile (inductor)
projectPyTorch
revisionunknown
workloadbatch_size = 128 · in_features = 32768 · fp32
comparison keysha256:cd07fbbd2070ca2c…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch-compile-inductor/level2/55_Matmul_MaxPool_Sum_Scale.py
sha256:5d53a2a784911756c38c582…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size128
in_features32768
input_1fp32 [128, 32768]
definition comparatornot_asserted
Measurements
latency · max11.5 ms · n=100
latency · mean10.7 ms · n=100
latency · min9.46 ms · n=100
latency · std463.0 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.
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Canonical manifest
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{
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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 MaxPool Sum Scale →JSON