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torch.compile (inductor)

Gemm Divide Sum Scaling · batch_size = 1024 · input_size = 8192 · fp32 · run 01a03f0e-f6e6…
●passedReported evidence

Not re-observed recently.

Primary measurement
2.77ms±0.00 · mean of 100

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

Compare with #1 →

max ms 2.92 · min ms 2.75 · std ms 0.0197 · mean ms 2.77

Identity
implementationtorch.compile (inductor)
projectPyTorch
revisionunknown
workloadbatch_size = 1024 · input_size = 8192 · fp32
comparison keysha256:5a6850cbeaf93698…
sourceKernelBench baseline timings
external idH100_Modal/torch-compile-inductor/level2/14_Gemm_Divide_Sum_Scaling.py
sha256:580f45a54b0811797cd970b…

Correctness

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

Workload

batch_size1024
input_size8192
input_1fp32 [1024, 8192]
definition comparatornot_asserted

Measurements

latency · max2.92 ms · n=100
latency · mean2.77 ms · n=100
latency · min2.75 ms · n=100
latency · std19.7 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": 2770000,
          "maximum": 2920000,
          "minimum": 2750000,
          "confidence95": [
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            2773861
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 19700,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 2.92,
          "min_ms": 2.75,
          "std_ms": 0.0197,
          "mean_ms": 2.77
        },
        "benchmark": "H100_Modal/baseline_time_torch_compile_inductor_default.json",
        "externalId": "H100_Modal/torch-compile-inductor/level2/14_Gemm_Divide_Sum_Scaling.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:28368d547fa1a81af3fbce9273548e930017009db2cc60ef2a4d08e97941ef25",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:64a17eb0893082e523c82b3cbc35488baace513392d9860f61cdcee6cd3acdd1"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-compile-inductor-l2-14-gemm-divide-sum-scaling",
      "title": "Gemm Divide Sum Scaling · torch.compile (inductor) · Modal"
    },
    "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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    "apiVersion": "kernelindex.dev/v1alpha1"
  },
  "environment": {
    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
        "vendor": "nvidia",
        "product": "NVIDIA H100",
        "formFactor": "80GB HBM3",
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    },
    "metadata": {
      "name": "kernelbench-h100-modal",
      "title": "KernelBench host · H100 80GB HBM3 (Modal)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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