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PyTorch eager

conv standard 2D asymmetric input asymmetric kernel · width = 256 · height = 512 · batch_size = 8 · in_channels = 64 · fp32 · run 01a03f0e-f834…
●passedReported evidence

Not re-observed recently.

Primary measurement
5.24ms±0.03 · mean of 100

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

max ms 5.29 · min ms 4.69 · std ms 0.128 · mean ms 5.24

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 256 · height = 512 · batch_size = 8 · in_channels = 64 · fp32
comparison keysha256:0377df9400cd2d67…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level1/56_conv_standard_2D__asymmetric_input__asymmetric_kernel.py
sha256:8e22c728194c35050fa1061…

Correctness

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

Workload

width256
height512
batch_size8
in_channels64
xfp32 [8, 64, 512, 256]
definition comparatornot_asserted

Measurements

latency · max5.29 ms · n=100
latency · mean5.24 ms · n=100
latency · min4.69 ms · n=100
latency · std128.0 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
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    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
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          "maximum": 5290000,
          "minimum": 4690000,
          "confidence95": [
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            5265088
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 128000,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
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          "min_ms": 4.69,
          "std_ms": 0.128,
          "mean_ms": 5.24
        },
        "benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
        "externalId": "H100_PCIe_LambdaLabs/torch/level1/56_conv_standard_2D__asymmetric_input__asymmetric_kernel.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:c73036b6704ca726ac7465b805dd8b80469c2ed96bd21ddaab5d57bed516bc20",
      "environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
      "implementationDigest": "sha256:ddb4c8b2943e1776794cf575c70210e04da7da2eae313ef718f2cffbb48e591b"
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs-torch-l1-56-conv-standard-2d-asymmetric-input-asymmetric-kernel",
      "title": "conv standard 2D asymmetric input asymmetric kernel · PyTorch eager · Lambda Labs"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
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  "protocol": {
    "kind": "BenchmarkProtocol",
    "spec": {
      "harness": {
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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"
      }
    },
    "metadata": {
      "name": "kernelbench-timing-v1",
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  "environment": {
    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
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        "formFactor": "PCIe",
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      "title": "KernelBench host · H100 PCIe (Lambda Labs)"
    },
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  }
}
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