{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# PCCX v002 Throughput Record\n",
        "\n",
        "Source scope: existing experiment and thread-scaling CSV rows in the research repository."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "source": [
        "experiment_tok_s = [0.66, 0.67, 0.79, 1.08, 1.12]\n",
        "thread_scaling = [\n",
        "    {\"threads\": 1, \"output_tokens\": 32, \"decode_mean_ms\": 594.3, \"decode_tok_s\": 1.68, \"ffn_ms\": 380.1},\n",
        "    {\"threads\": 2, \"output_tokens\": 32, \"decode_mean_ms\": 641.2, \"decode_tok_s\": 1.56, \"ffn_ms\": 425.1},\n",
        "    {\"threads\": 4, \"output_tokens\": 32, \"decode_mean_ms\": 759.2, \"decode_tok_s\": 1.32, \"ffn_ms\": 507.5},\n",
        "    {\"threads\": 6, \"output_tokens\": 32, \"decode_mean_ms\": 783.7, \"decode_tok_s\": 1.28, \"ffn_ms\": 540.4},\n",
        "]\n",
        "\n",
        "best_thread_row = max(thread_scaling, key=lambda row: row[\"decode_tok_s\"])\n",
        "summary = {\n",
        "    \"experiment_tok_s_range\": (min(experiment_tok_s), max(experiment_tok_s)),\n",
        "    \"best_thread_row\": best_thread_row,\n",
        "    \"thread_sweep_tok_s\": [(row[\"threads\"], row[\"decode_tok_s\"]) for row in thread_scaling],\n",
        "}\n",
        "summary"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Boundary\n",
        "\n",
        "Measured throughput rows and target rows are separate records. This notebook reports only the values listed in the CSV bundle."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.x"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}