PyTorch eager
Matmul with irregular shapes · k = 2949 · m = 8205 · n = 5921 · fp32 · run 01a03f0e-f82f…
●passedReported evidence
Not re-observed recently.
Primary measurement
7.69ms±0.01 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 7.89 · min ms 7.42 · std ms 0.0631 · mean ms 7.69
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadk = 2949 · m = 8205 · n = 5921 · fp32
comparison keysha256:a18844b0b21d9073…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/8_Matmul_with_irregular_shapes_.py
sha256:be2c9320ae972d1f86f2605…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
k2949
m8205
n5921
afp32 [8205, 2949]
bfp32 [2949, 5921]
definition comparatornot_asserted
Measurements
latency · max7.89 ms · n=100
latency · mean7.69 ms · n=100
latency · min7.42 ms · n=100
latency · std63.1 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": [
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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 irregular shapes →JSON