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torch.compile (inductor)

Matmul BatchNorm BiasAdd Divide Swish · batch_size = 1024 · in_features = 8192 · fp32 · run 01a03f0e-f6ed…
●passedReported evidence

Not re-observed recently.

Primary measurement
2.75ms±0.01 · mean of 100

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

max ms 3.17 · min ms 2.73 · std ms 0.0434 · mean ms 2.75

Identity
implementationtorch.compile (inductor)
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · in_features = 8192 · fp32
comparison keysha256:7fecba718d74bef7…
sourceKernelBench baseline timings
external idH100_Modal/torch-compile-inductor/level2/97_Matmul_BatchNorm_BiasAdd_Divide_Swish.py
sha256:36a6a8908c72c1d385ec709…

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 · max3.17 ms · n=100
latency · mean2.75 ms · n=100
latency · min2.73 ms · n=100
latency · std43.4 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,
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          "minimum": 2730000,
          "confidence95": [
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        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
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          "unit": "ns",
          "value": 43400,
          "metric": "latency",
          "statistic": "std"
        }
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      "sourceNative": {
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          "std_ms": 0.0434,
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        },
        "benchmark": "H100_Modal/baseline_time_torch_compile_inductor_default.json",
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      "implementationDigest": "sha256:7386c3ac1049b264e5205b529a01dca28c4df578df19bd8ca829df0770de7c45"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-compile-inductor-l2-97-matmul-batchnorm-biasadd-divide-swish",
      "title": "Matmul BatchNorm BiasAdd Divide Swish · torch.compile (inductor) · Modal"
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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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}
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