PyTorch eager
LSTMCn · batch_size = 10 · input_size = 128 · num_layers = 6 · hidden_size = 256 · sequence_length = 512 · fp32 · run 01a03f0e-f6dc…
●passedReported evidence
Not re-observed recently.
Primary measurement
28.0ms±0.50 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 35.8 · min ms 26.2 · std ms 2.55 · mean ms 28
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 10 · input_size = 128 · num_layers = 6 · hidden_size = 256 · sequence_length = 512 · fp32
comparison keysha256:3dc3ad9c44e32e8b…
sourceKernelBench baseline timings
external idH100_Modal/torch/level3/37_LSTMCn.py
sha256:b87fe23dda2a7c9a2c8c0f8…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size10
input_size128
num_layers6
hidden_size256
sequence_length512
input_1fp32 [10, 512, 128]
input_2fp32 [6, 10, 256]
input_3fp32 [6, 10, 256]
definition comparatornot_asserted
Measurements
latency · max35.8 ms · n=100
latency · mean28.0 ms · n=100
latency · min26.2 ms · n=100
latency · std2.55 ms
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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{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 28000000,
"maximum": 35800000,
"minimum": 26200000,
"confidence95": [
27500200,
28499800
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 2550000,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
"source": "kernelbench",
"metrics": {
"max_ms": 35.8,
"min_ms": 26.2,
"std_ms": 2.55,
"mean_ms": 28
},
"benchmark": "H100_Modal/baseline_time_torch.json",
"externalId": "H100_Modal/torch/level3/37_LSTMCn.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:2d449c21b969e622ee9d2b96be7cc2e1f8d73e6b26669a6e3c0574f4d482de24",
"environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
"implementationDigest": "sha256:e1fe091870421e3c80e8ffedbd7c89c0a411f2c728c7a41e2f8749b0c60db993"
},
"metadata": {
"name": "kernelbench-h100-modal-torch-l3-37-lstmcn",
"title": "LSTMCn · PyTorch eager · Modal"
},
"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"
},
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"environment": {
"kind": "ExecutionEnvironment",
"spec": {
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"formFactor": "80GB HBM3",
"architecture": "sm_90"
},
"software": {}
},
"metadata": {
"name": "kernelbench-h100-modal",
"title": "KernelBench host · H100 80GB HBM3 (Modal)"
},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for LSTMCn →JSON