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PyTorch eager

Matmul Swish Scaling · batch_size = 128 · in_features = 32768 · fp32 · run 01a03f0e-f83b…
●passedReported evidence

Not re-observed recently.

Primary measurement
10.9ms±0.09 · mean of 100

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

Compare with #1 →

max ms 11.7 · min ms 9.47 · std ms 0.454 · mean ms 10.9

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 128 · in_features = 32768 · fp32
comparison keysha256:ec5519ceaab9330f…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level2/59_Matmul_Swish_Scaling.py
sha256:f767ae96acca5120116271f…

Correctness

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

Workload

batch_size128
in_features32768
input_1fp32 [128, 32768]
definition comparatornot_asserted

Measurements

latency · max11.7 ms · n=100
latency · mean10.9 ms · n=100
latency · min9.47 ms · n=100
latency · std454.0 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": 10900000,
          "maximum": 11700000,
          "minimum": 9470000,
          "confidence95": [
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            10988984
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 454000,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
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          "min_ms": 9.47,
          "std_ms": 0.454,
          "mean_ms": 10.9
        },
        "benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
        "externalId": "H100_PCIe_LambdaLabs/torch/level2/59_Matmul_Swish_Scaling.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:52c4ff934925c6e4bed1c8939ddc53d63a4ad4bcfed6abfd13223e8e6ab816d0",
      "environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
      "implementationDigest": "sha256:e4a057ba702d715b4273319d0fdfed844bf0d9c7f68ca602079d69f17031b77e"
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs-torch-l2-59-matmul-swish-scaling",
      "title": "Matmul Swish Scaling · 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"
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  "environment": {
    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
        "vendor": "nvidia",
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        "formFactor": "PCIe",
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    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs",
      "title": "KernelBench host · H100 PCIe (Lambda Labs)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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