{
  "schema_version": 1,
  "repository": "https://github.com/ai-emerald/emerald-ai-demo-may-2025",
  "commit": "34403aef78c2690a8e4f0ba8f7f62d302cdba967",
  "license": "Apache-2.0",
  "retrieved_date": "2026-11-06",
  "verification": "Each raw input matches the Git blob SHA1 recorded by the pinned upstream commit; SHA256 values additionally recorded for reproducibility.",
  "inputs": [
    {
      "source_path": "data/dvfs_sweep.csv",
      "local_path": "raw/dvfs_sweep.csv",
      "source_url": "https://raw.githubusercontent.com/ai-emerald/emerald-ai-demo-may-2025/34403aef78c2690a8e4f0ba8f7f62d302cdba967/data/dvfs_sweep.csv",
      "bytes": 3949,
      "sha256": "65025daeb2c723019c27d05ed7da900f5800b5b5b0f957ccb23e7903b4028bd7",
      "git_blob_sha1": "6be26dd1e579f0fa386026614446de9b55ea053f"
    },
    {
      "source_path": "README.md",
      "local_path": "raw/README.emerald.md",
      "source_url": "https://raw.githubusercontent.com/ai-emerald/emerald-ai-demo-may-2025/34403aef78c2690a8e4f0ba8f7f62d302cdba967/README.md",
      "bytes": 11084,
      "sha256": "8bd6dce7d67c3bab2be9149102507fb1a2ca0d9eca5769937f1321598da76d86",
      "git_blob_sha1": "e80c4b9f44eb31b05a4f06415cf2265af4acfe70"
    },
    {
      "source_path": "LICENSE",
      "local_path": "LICENSE.emerald.txt",
      "source_url": "https://raw.githubusercontent.com/ai-emerald/emerald-ai-demo-may-2025/34403aef78c2690a8e4f0ba8f7f62d302cdba967/LICENSE",
      "bytes": 11357,
      "sha256": "c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4",
      "git_blob_sha1": "261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64"
    }
  ],
  "derived_outputs": [
    {
      "path": "emerald.json",
      "bytes": 21038,
      "sha256": "b50d27843046c7ec2f14708f123c96e7216d2f7a85fa680f8bf130632c23dd97",
      "transformation": "CSV parsed to numeric values; rows grouped by workload; cap normalized by 400 W; observed power normalized by observed power at 400 W for the same workload; normalized throughput retained from source; inferred GPU counts rounded from total / per-GPU power. Default curve is ft_llama_8b_dolly."
    }
  ],
  "excluded_data": [
    {
      "source_path": "data/SRP_total_power.csv",
      "reason": "Already controlled experiment; no verified uncontrolled interval, so excluded from baseline."
    }
  ],
  "limitations": [
    "Measurements are from the published 256-GPU field-demonstration dataset, not a GridPass deployment.",
    "The selected response curve is one Llama 8B Dolly fine-tuning workload; it is not a universal workload or GPU response model.",
    "cap is the commanded cap divided by 400 W. power_ratio is observed GPU draw divided by observed GPU draw at the 400 W cap; these quantities differ.",
    "Only the six published cap points are measured. Interpolation between them, dynamic response and simulated site scaling are modeled.",
    "No uncontrolled demand baseline is provided. Any varying demand in the GridPass demo must be explicitly labeled synthetic.",
    "The retrieved SRP total-power trace contains controlled-event behavior and is deliberately excluded from the demand baseline.",
    "GPU-only power measurements do not establish server, cooling or facility power response.",
    "The CSV does not state the units of its raw throughput column; source_throughput is preserved without assigning units.",
    "GPU counts per curve are inferred from measured total GPU power divided by measured power per GPU.",
    "Some inference variants share normalized throughput values; this fixture does not claim they are independent experiments.",
    "Sampling frequency, transient response, error bars, service-level performance and actuator safety are not established by this static sweep."
  ],
  "paper_url": "https://arxiv.org/html/2507.00909v1"
}