{
  "schemaVersion": 1,
  "lessonId": "kernels-04",
  "contentHash": "502f1d93126e5e86a2e224f8d494da3faadd751606503d415c9d3c37d2f1b7d0",
  "executedAt": "2026-10-20T10:50:03.077466+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Kernel errors versus represented-input oracle: [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]\nGaps versus original float32 data: [[0.0, 0.000862419605255127], [0.0, 0.025217056274414062], [0.0, 0.030788421630859375], [0.0, 0.0292205810546875]]\nExpected benchmark refusal: TPU benchmark requires real TPU inputs; interpretation timings are not accepted\nCPU correctness audit complete; no TPU latency or speedup measured\nKernel errors versus represented-input oracle: [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]\nGaps versus original float32 data: [[0.0, 0.000862419605255127], [0.0, 0.025217056274414062], [0.0, 0.030788421630859375], [0.0, 0.0292205810546875]]\nExpected benchmark refusal: TPU benchmark requires real TPU inputs; interpretation timings are not accepted\nCPU correctness audit complete; no TPU latency or speedup measured\nJoint row permutation preserves output identity\nEarly/final-only rounding: 1.0 1.0078125\nConfiguration, padded elements: (8, 128) 2 4096\nConfiguration, padded elements: (8, 128) 3 4096\nConfiguration, padded elements: (16, 256) 2 4096\nConfiguration, padded elements: (16, 256) 3 4096\nMixed and integer dtypes rejected\nModeled twofold-local speedup and zero-cost limit: 1.1764705882352942 1.4285714285714286\nPASS: kernels-04\n",
  "notebookCells": [
    {
      "sourceHash": "bd9bea5eebb0e6a296c23ddd61b0654571c7b41bd198d049e8c667536d1beb4d",
      "outputs": []
    },
    {
      "sourceHash": "616f8f9ced903f3cf1bbdc0b8c5139aae8ba73378901e96feab33dfe2000590b",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel errors versus represented-input oracle: [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]\n",
            "Gaps versus original float32 data: [[0.0, 0.000862419605255127], [0.0, 0.025217056274414062], [0.0, 0.030788421630859375], [0.0, 0.0292205810546875]]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "93f0030559298da392217a893651250a49b2fa960da847391041e3f935e6813e",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected benchmark refusal: TPU benchmark requires real TPU inputs; interpretation timings are not accepted\n",
            "CPU correctness audit complete; no TPU latency or speedup measured\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "d3caf6da98fc4af020c09e9c542401c1f2598ac91f2960cff9b5fbf39b4202f4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kernel errors versus represented-input oracle: [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]\n",
            "Gaps versus original float32 data: [[0.0, 0.000862419605255127], [0.0, 0.025217056274414062], [0.0, 0.030788421630859375], [0.0, 0.0292205810546875]]\n",
            "Expected benchmark refusal: TPU benchmark requires real TPU inputs; interpretation timings are not accepted\n",
            "CPU correctness audit complete; no TPU latency or speedup measured\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "33282c863bb473f53ccabc48d0d5d794e2d19cabbcaaf355012007bc8bc75ee6",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": "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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "1a0c4c332f298e5b21ba701eed9dc898281584ff9f98b0197092374d5aee36be",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Joint row permutation preserves output identity\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "726e85aea810f2548c50e8ac7e93233e0f1faf1c7c1872c9b7776ee151bd1eb4",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Early/final-only rounding: 1.0 1.0078125\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "eddfa2188b9fa7b5fcb2d1308225d52663c48ef53e0eeb1516bfb74d8abf6b3f",
      "outputs": []
    },
    {
      "sourceHash": "834879b1e3bef806ce9f6db1c7f8a5f64d16b896dc6028b7819cc974de76d856",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Configuration, padded elements: (8, 128) 2 4096\n",
            "Configuration, padded elements: (8, 128) 3 4096\n",
            "Configuration, padded elements: (16, 256) 2 4096\n",
            "Configuration, padded elements: (16, 256) 3 4096\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "00323d8374e8b38c6c44d0a42c0882b4534f080e90004a78e2bc2b5eb9e31b3a",
      "outputs": []
    },
    {
      "sourceHash": "a5927e4b4704362b6a7b95f709a45b9ef0a0f629c02b1d9c5b86d62ed94344cd",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mixed and integer dtypes rejected\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "b3db56a99edaaf6ef2783e9e407ec676ac452e80ca1a2249e24315c01ffa3877",
      "outputs": []
    },
    {
      "sourceHash": "2ef5f396236935fd26d23a29bb0e41b3a6057c87bc7ee62802f23c73ee088aa6",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Modeled twofold-local speedup and zero-cost limit: 1.1764705882352942 1.4285714285714286\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}