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PyTorch eager

Matmul with small K dimension · k = 64 · m = 32768 · n = 32768 · fp32 · run 01a03f0e-f6ca…
●passedReported evidence

Not re-observed recently.

Primary measurement
4.06ms±0.02 · mean of 100

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

max ms 5.08 · min ms 4.04 · std ms 0.103 · mean ms 4.06

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadk = 64 · m = 32768 · n = 32768 · fp32
comparison keysha256:1ef3d7a4c51224ad…
sourceKernelBench baseline timings
external idH100_Modal/torch/level1/7_Matmul_with_small_K_dimension_.py
sha256:28d5eb5903ce69697727d3d…

Correctness

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

Workload

k64
m32768
n32768
afp32 [32768, 64]
bfp32 [64, 32768]
definition comparatornot_asserted

Measurements

latency · max5.08 ms · n=100
latency · mean4.06 ms · n=100
latency · min4.04 ms · n=100
latency · std103.0 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": 4060000,
          "maximum": 5080000,
          "minimum": 4040000,
          "confidence95": [
            4040000,
            4080188
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 103000,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 5.08,
          "min_ms": 4.04,
          "std_ms": 0.103,
          "mean_ms": 4.06
        },
        "benchmark": "H100_Modal/baseline_time_torch.json",
        "externalId": "H100_Modal/torch/level1/7_Matmul_with_small_K_dimension_.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:af3f0f21cc16442bf87cce793aa9f2e4e5971433199198d0f6b0ffa030934d52",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:500c608cde525703c3d44d60fd3f48cbf2b1f0b31c725c198bb32789a3e8b8b9"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-l1-7-matmul-with-small-k-dimension",
      "title": "Matmul with small K dimension · 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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