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PyTorch eager

Gemm Scale BatchNorm · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f839…
●passedReported evidence

Not re-observed recently.

Primary measurement
4.86ms±0.01 · mean of 100

Rank 1 in its comparison group · source-native comparison · observed 2026-03-05

max ms 4.91 · min ms 4.75 · std ms 0.0364 · mean ms 4.86

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:a8a971c500df34dc…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/33_Gemm_Scale_BatchNorm.py
sha256:f7e0ceafc1ef0b19caf12a6…

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 · max4.91 ms · n=100
latency · mean4.86 ms · n=100
latency · min4.75 ms · n=100
latency · std36.4 µs

Protocol

harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing

Environment

gpuNVIDIA H100 (sm_90)

Artifacts

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Replications and notes

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Canonical manifest

Show manifest
{
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    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
          "mean": 4860000,
          "maximum": 4910000,
          "minimum": 4750000,
          "confidence95": [
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          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 36400,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
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        "metrics": {
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          "min_ms": 4.75,
          "std_ms": 0.0364,
          "mean_ms": 4.86
        },
        "benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
        "externalId": "H100_PCIe_LambdaLabs/torch/level2/33_Gemm_Scale_BatchNorm.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:0e94c4ca22a5c0099d4f8c7658409aa803fb6d25c8af9d93185d5db443bfeab1",
      "environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
      "implementationDigest": "sha256:07ceb23d9168ecbd3f9717a2fa4065479e7e081810fc7136c27a3d0de3f71d65"
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs-torch-l2-33-gemm-scale-batchnorm",
      "title": "Gemm Scale BatchNorm · PyTorch eager · Lambda Labs"
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    "apiVersion": "kernelindex.dev/v1alpha1"
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  "protocol": {
    "kind": "BenchmarkProtocol",
    "spec": {
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      },
      "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"
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    },
    "metadata": {
      "name": "kernelbench-timing-v1",
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    "kind": "ExecutionEnvironment",
    "spec": {
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      "title": "KernelBench host · H100 PCIe (Lambda Labs)"
    },
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  }
}
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