PyTorch eager
Matmul with diagonal matrices · m = 4096 · n = 4096 · fp32 · run 01a03f0e-f830…
●passedReported evidence
Not re-observed recently.
Primary measurement
3.70ms±0.00 · mean of 100
Rank 1 in its comparison group · source-native comparison · observed 2026-03-05
max ms 3.77 · min ms 3.67 · std ms 0.013 · mean ms 3.7
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadm = 4096 · n = 4096 · fp32
comparison keysha256:8d893296594218f2…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/12_Matmul_with_diagonal_matrices_.py
sha256:8f038b46ba42e00347127d2…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
m4096
n4096
afp32 [4096]
bfp32 [4096, 4096]
definition comparatornot_asserted
Measurements
latency · max3.77 ms · n=100
latency · mean3.70 ms · n=100
latency · min3.67 ms · n=100
latency · std13.0 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 diagonal matrices →JSON