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PyTorch eager

Conv3d Multiply InstanceNorm Clamp Multiply Max · depth = 16 · width = 32 · height = 32 · batch_size = 128 · in_channels = 3 · fp32 · run 01a03f0e-f83d…
●passedReported evidence

Not re-observed recently.

Primary measurement
1.64ms±0.00 · mean of 100

Rank 2 in its comparison group · source-native comparison · observed 2026-03-05

Compare with #1 →

max ms 1.71 · min ms 1.63 · std ms 0.0155 · mean ms 1.64

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddepth = 16 · width = 32 · height = 32 · batch_size = 128 · in_channels = 3 · fp32
comparison keysha256:7874426b0fc1b79f…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/79_Conv3d_Multiply_InstanceNorm_Clamp_Multiply_Max.py
sha256:d8a291ff8042867618a6a44…

Correctness

Marked passed by the source; the correctness policy was not published.

Workload

depth16
width32
height32
batch_size128
in_channels3
input_1fp32 [128, 3, 16, 32, 32]
definition comparatornot_asserted

Measurements

latency · max1.71 ms · n=100
latency · mean1.64 ms · n=100
latency · min1.63 ms · n=100
latency · std15.5 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

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Canonical manifest

Show manifest
{
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    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
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          "maximum": 1710000,
          "minimum": 1630000,
          "confidence95": [
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            1643038
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 15500,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
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        "metrics": {
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          "min_ms": 1.63,
          "std_ms": 0.0155,
          "mean_ms": 1.64
        },
        "benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
        "externalId": "H100_PCIe_LambdaLabs/torch/level2/79_Conv3d_Multiply_InstanceNorm_Clamp_Multiply_Max.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:5d1d32b462bae303fe67c0a276f618f55d68e2f67e0652930eecb88f29531f47",
      "environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
      "implementationDigest": "sha256:b539ae11e2375d3d107684c8f1f5e3b9c9e15bc3a5266c7820026501f2788c34"
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    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs-torch-l2-79-conv3d-multiply-instancenorm-clamp-multiply-max",
      "title": "Conv3d Multiply InstanceNorm Clamp Multiply Max · PyTorch eager · Lambda Labs"
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    "apiVersion": "kernelindex.dev/v1alpha1"
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  "protocol": {
    "kind": "BenchmarkProtocol",
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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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  }
}
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