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PyTorch eager

Softplus · dim = 393216 · batch_size = 4096 · fp32 · run 01a03f0e-f6cc…
●passedReported evidence

Not re-observed recently.

Primary measurement
4.31ms±0.04 · mean of 100

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

Compare with #1 →

max ms 6.18 · min ms 4.27 · std ms 0.193 · mean ms 4.31

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddim = 393216 · batch_size = 4096 · fp32
comparison keysha256:69e09fea70e6b16e…
sourceKernelBench baseline timings
external idH100_Modal/torch/level1/29_Softplus.py
sha256:5e34ade65c266ae1e6cb147…

Correctness

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

Workload

dim393216
batch_size4096
xfp32 [4096, 393216]
definition comparatornot_asserted

Measurements

latency · max6.18 ms · n=100
latency · mean4.31 ms · n=100
latency · min4.27 ms · n=100
latency · std193.0 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
{
  "run": {
    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
          "mean": 4310000,
          "maximum": 6180000,
          "minimum": 4270000,
          "confidence95": [
            4272172,
            4347828
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 193000,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 6.18,
          "min_ms": 4.27,
          "std_ms": 0.193,
          "mean_ms": 4.31
        },
        "benchmark": "H100_Modal/baseline_time_torch.json",
        "externalId": "H100_Modal/torch/level1/29_Softplus.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:7fac1fbe649b59ff09c406ce4e1b5766ebd3a40f8bf7b43a1622cbd351f61754",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:dee43504f05cf2b310dd3909bba58a36faa3956725f59299af3e9c80e3937b8a"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-l1-29-softplus",
      "title": "Softplus · PyTorch eager · 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"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  },
  "environment": {
    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
        "vendor": "nvidia",
        "product": "NVIDIA H100",
        "formFactor": "80GB HBM3",
        "architecture": "sm_90"
      },
      "software": {}
    },
    "metadata": {
      "name": "kernelbench-h100-modal",
      "title": "KernelBench host · H100 80GB HBM3 (Modal)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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