torch.compile (inductor)
Matmul with small K dimension · k = 64 · m = 32768 · n = 32768 · fp32 · run 01a03f0e-f843…
●passedReported evidence
Not re-observed recently.
Primary measurement
6.34ms±0.05 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 6.87 · min ms 5.43 · std ms 0.248 · mean ms 6.34
Identity
implementationtorch.compile (inductor)
projectPyTorch
revisionunknown
workloadk = 64 · m = 32768 · n = 32768 · fp32
comparison keysha256:e9ded09c20687837…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch-compile-inductor/level1/7_Matmul_with_small_K_dimension_.py
sha256:634979e02cafcf6652a2fc7…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
k64
m32768
n32768
afp32 [32768, 64]
bfp32 [64, 32768]
definition comparatornot_asserted
Measurements
latency · max6.87 ms · n=100
latency · mean6.34 ms · n=100
latency · min5.43 ms · n=100
latency · std248.0 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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}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Matmul with small K dimension →JSON