PyTorch eager
NetVladWithGhostClusters · batch_size = 2048 · feature_size = 512 · num_features = 100 · fp32 · run 01a03f0e-f842…
●passedReported evidence
Not re-observed recently.
Primary measurement
2.16ms±0.01 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 2.22 · min ms 2.08 · std ms 0.0377 · mean ms 2.16
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 2048 · feature_size = 512 · num_features = 100 · fp32
comparison keysha256:cd90ba28092c03dc…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level3/46_NetVladWithGhostClusters.py
sha256:a964fb9313e4cf96699ef1a…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size2048
feature_size512
num_features100
input_1fp32 [2048, 100, 512]
definition comparatornot_asserted
Measurements
latency · max2.22 ms · n=100
latency · mean2.16 ms · n=100
latency · min2.08 ms · n=100
latency · std37.7 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
Show manifestHide manifest
{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 2160000,
"maximum": 2220000,
"minimum": 2080000,
"confidence95": [
2152611,
2167389
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 37700,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 2.22,
"min_ms": 2.08,
"std_ms": 0.0377,
"mean_ms": 2.16
},
"benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
"externalId": "H100_PCIe_LambdaLabs/torch/level3/46_NetVladWithGhostClusters.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:b1c0ec7f2a4f3940c1b3f54d4a54c7affbe1d09997c0e26ad81252ad997d4fda",
"environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
"implementationDigest": "sha256:378bf9a05ccfc4f4f34cdf89f622d1c34bb135d5654cae81b2678f1025856f02"
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs-torch-l3-46-netvladwithghostclusters",
"title": "NetVladWithGhostClusters · PyTorch eager · Lambda Labs"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
"name": "KernelBench timing scripts",
"repository": "https://github.com/ScalingIntelligence/KernelBench"
},
"measurement": {
"timer": "cuda_events",
"primaryStatistic": "mean"
},
"comparability": {
"notes": "Mean of 100 timed forward passes (CUDA events, warm-up excluded) of the reference module on fixed inputs; the 95% interval is the mean's, from the reported standard deviation. The upstream JSON records no torch or CUDA version. Comparable only within one problem and one timing host.",
"family": "kernelbench_baseline_timing"
}
},
"metadata": {
"name": "kernelbench-timing-v1",
"title": "KernelBench baseline timing"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"environment": {
"kind": "ExecutionEnvironment",
"spec": {
"hardware": {
"vendor": "nvidia",
"product": "NVIDIA H100",
"formFactor": "PCIe",
"architecture": "sm_90"
},
"software": {}
},
"metadata": {
"name": "kernelbench-h100-pcie-lambdalabs",
"title": "KernelBench host · H100 PCIe (Lambda Labs)"
},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
Report an issue with this run
Published 2026-08-26 · KernelBench baseline timings · MITAll results for NetVladWithGhostClusters →JSON