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PyTorch eager

Matmul Add Swish Tanh GELU Hardtanh · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f6d8…
●passedReported evidence

Not re-observed recently.

Primary measurement
2.85ms±0.00 · mean of 100

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

Compare with #1 →

max ms 2.87 · min ms 2.84 · std ms 0.00396 · mean ms 2.85

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:04f4ad93d60c55d6…
sourceKernelBench baseline timings
external idH100_Modal/torch/level2/95_Matmul_Add_Swish_Tanh_GELU_Hardtanh.py
sha256:780ab1cf25289b3fe2a5a17…

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 · max2.87 ms · n=100
latency · mean2.85 ms · n=100
latency · min2.84 ms · n=100
latency · std3.96 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": {
          "mean": 2850000,
          "maximum": 2870000,
          "minimum": 2840000,
          "confidence95": [
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            2850776
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 3960,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
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        "metrics": {
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          "min_ms": 2.84,
          "std_ms": 0.00396,
          "mean_ms": 2.85
        },
        "benchmark": "H100_Modal/baseline_time_torch.json",
        "externalId": "H100_Modal/torch/level2/95_Matmul_Add_Swish_Tanh_GELU_Hardtanh.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:6f03379a8be43082811e162bac056b6d3636cad20392bdb817812a534ae61767",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:09d5dbf4e7ac74199394a50a873c92f3973f16c96c4bbfe6cf08410b346f1dcc"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-l2-95-matmul-add-swish-tanh-gelu-hardtanh",
      "title": "Matmul Add Swish Tanh GELU Hardtanh · PyTorch eager · Modal"
    },
    "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"
      }
    },
    "metadata": {
      "name": "kernelbench-timing-v1",
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    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
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      "title": "KernelBench host · H100 80GB HBM3 (Modal)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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