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PyTorch eager

Matrix scalar multiplication · m = 65536 · n = 16384 · fp32 · run 01a03f0e-f6f2…
●passedReported evidence

Not re-observed recently.

Primary measurement
4.64ms±0.00 · mean of 100

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

max ms 4.7 · min ms 4.63 · std ms 0.00766 · mean ms 4.64

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadm = 65536 · n = 16384 · fp32
comparison keysha256:241421c192f6a30e…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/5_Matrix_scalar_multiplication.py
sha256:71a3a0bd964283e3c2b46df…

Correctness

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

Workload

m65536
n16384
afp32 [65536, 16384]
definition comparatornot_asserted

Measurements

latency · max4.70 ms · n=100
latency · mean4.64 ms · n=100
latency · min4.63 ms · n=100
latency · std7.66 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": 4640000,
          "maximum": 4700000,
          "minimum": 4630000,
          "confidence95": [
            4638499,
            4641501
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 7660,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 4.7,
          "min_ms": 4.63,
          "std_ms": 0.00766,
          "mean_ms": 4.64
        },
        "benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
        "externalId": "H100_PCIe_LambdaLabs/torch/level1/5_Matrix_scalar_multiplication.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:3376694ac8a9600225717ed1457ad28717757956c03ac3feef5164cec5c881d0",
      "environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
      "implementationDigest": "sha256:8b7e16fda93471e42b4d94b914c64cb1085e5581111dac2944d4716da3cba10b"
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs-torch-l1-5-matrix-scalar-multiplication",
      "title": "Matrix scalar multiplication · PyTorch eager · Lambda Labs"
    },
    "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": "PCIe",
        "architecture": "sm_90"
      },
      "software": {}
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs",
      "title": "KernelBench host · H100 PCIe (Lambda Labs)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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