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PyTorch eager

L1Norm · dim = 65535 · batch_size = 32768 · fp32 · run 01a03f0e-f6cc…
●passedReported evidence

Not re-observed recently.

Primary measurement
15.6ms±0.05 · mean of 100

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

Compare with #1 →

max ms 17.8 · min ms 15.5 · std ms 0.275 · mean ms 15.6

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloaddim = 65535 · batch_size = 32768 · fp32
comparison keysha256:3fcb8ab537984e29…
sourceKernelBench baseline timings
external idH100_Modal/torch/level1/38_L1Norm_.py
sha256:d213fc77603987260a0a319…

Correctness

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

Workload

dim65535
batch_size32768
xfp32 [32768, 65535]
definition comparatornot_asserted

Measurements

latency · max17.8 ms · n=100
latency · mean15.6 ms · n=100
latency · min15.5 ms · n=100
latency · std275.0 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
{
  "run": {
    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
          "mean": 15600000,
          "maximum": 17800000,
          "minimum": 15500000,
          "confidence95": [
            15546100,
            15653900
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 275000,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 17.8,
          "min_ms": 15.5,
          "std_ms": 0.275,
          "mean_ms": 15.6
        },
        "benchmark": "H100_Modal/baseline_time_torch.json",
        "externalId": "H100_Modal/torch/level1/38_L1Norm_.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:f9bc956ddeb65b413a05ef6067d6ce5c94721d8d742920de7d6936f809bb926a",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:e2f56e9845599c15adf38b55c68b4e9cc167057f062599dc1164745e8f3463ae"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-l1-38-l1norm",
      "title": "L1Norm · 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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