PyTorch eager
Matmul Sum Max AvgPool LogSumExp LogSumExp · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f838…
●passedReported evidence
Not re-observed recently.
Primary measurement
4.80ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 4.87 · min ms 4.75 · std ms 0.032 · mean ms 4.8
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:5c1004b6e5821db2…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/18_Matmul_Sum_Max_AvgPool_LogSumExp_LogSumExp.py
sha256:b99120107e040519c1209de…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size1024
in_features8192
input_1fp32 [1024, 8192]
definition comparatornot_asserted
Measurements
latency · max4.87 ms · n=100
latency · mean4.80 ms · n=100
latency · min4.75 ms · n=100
latency · std32.0 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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}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for Matmul Sum Max AvgPool LogSumExp LogSumExp →JSON