vizualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def data_preparation(df: pd.DataFrame, keep_ratio: float = 0.8) -> pd.DataFrame:\n",
    "    stats = (\n",
    "        df.groupby([\"Module\", \"Function\"])\n",
    "        .agg(\n",
    "            mean_cpu=pd.NamedAgg(column=\"CPU Time\", aggfunc=\"mean\"),\n",
    "            std_cpu=pd.NamedAgg(column=\"CPU Time\", aggfunc=\"std\"),\n",
    "            mean_mem_bound=pd.NamedAgg(column=\"Memory Bound(%)\", aggfunc=\"mean\"),\n",
    "            std_mem_bound=pd.NamedAgg(column=\"Memory Bound(%)\", aggfunc=\"std\"),\n",
    "            mean_fp=pd.NamedAgg(column=\"Vectorization:% of FP Ops(%)\", aggfunc=\"mean\"),\n",
    "            std_fp=pd.NamedAgg(column=\"Vectorization:% of FP Ops(%)\", aggfunc=\"std\"),\n",
    "            mean_l1_bound=pd.NamedAgg(column=\"Memory Bound:L1 Bound(%)\", aggfunc=\"mean\"),\n",
    "            mean_l2_bound=pd.NamedAgg(column=\"Memory Bound:L2 Bound(%)\", aggfunc=\"mean\"),\n",
    "            mean_l3_bound=pd.NamedAgg(column=\"Memory Bound:L3 Bound(%)\", aggfunc=\"mean\"),\n",
    "            mean_dram_bound=pd.NamedAgg(column=\"Memory Bound:DRAM Bound(%)\", aggfunc=\"mean\"),\n",
    "            mean_store_bound=pd.NamedAgg(column=\"Memory Bound:Store Bound(%)\", aggfunc=\"mean\"),\n",
    "        )\n",
    "        .sort_values(by=\"mean_cpu\", ascending=False)\n",
    "    )\n",
    "    stats[\"perc_cpu_time\"] = stats[\"mean_cpu\"] / stats[\"mean_cpu\"].sum() * 100\n",
    "    stats[\"cumperc_cpu_time\"] = stats[\"perc_cpu_time\"].cumsum()\n",
    "    hotspots = stats[\n",
    "        stats[\"cumperc_cpu_time\"] <= max(keep_ratio * 100, stats[\"cumperc_cpu_time\"].iloc[0])\n",
    "    ]\n",
    "\n",
    "    # Total CPU Time per module\n",
    "    module_mean_cpu = hotspots.groupby(\"Module\")[\"mean_cpu\"].sum().to_dict()\n",
    "    hotspots = hotspots.reset_index()\n",
    "    hotspots[\"module_cpu\"] = hotspots[\"Module\"].map(module_mean_cpu)\n",
    "\n",
    "    # Clean Function and Module names\n",
    "    def module_short(module: str) -> str:\n",
    "        if m := re.match(r\"^\\w+\\.so\", module):\n",
    "            return m.group(0)\n",
    "        return module\n",
    "\n",
    "    hotspots[\"func_short\"] = hotspots[\"Function\"].apply(\n",
    "        lambda x: re.sub(r\"<.*>\", \"\", x)\n",
    "    )\n",
    "    hotspots[\"module_short\"] = hotspots[\"Module\"].apply(module_short)\n",
    "\n",
    "    # CPU Time sorted by \"Module CPU Time\" -> \"CPU Time\".\n",
    "    hotspots = hotspots.sort_values(by=[\"module_cpu\", \"mean_cpu\"], ascending=False)\n",
    "    # Reorder the cumulative makespan for plotting.\n",
    "    hotspots[\"cumperc_cpu_time\"] = hotspots[\"perc_cpu_time\"].cumsum()\n",
    "    hotspots = hotspots.reset_index()\n",
    "    hotspots.index += 1\n",
    "\n",
    "    return hotspots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "from bokeh.models import (\n",
    "    ColorBar,\n",
    "    ColumnDataSource,\n",
    "    FactorRange,\n",
    "    HoverTool,\n",
    "    LabelSet,\n",
    "    LinearAxis,\n",
    "    LinearColorMapper,\n",
    "    Range1d,\n",
    "    Whisker,\n",
    ")\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.transform import transform\n",
    "\n",
    "\n",
    "def plot_hotspots(\n",
    "    data: pd.DataFrame,\n",
    "    *,\n",
    "    y_limit: float | None = None,\n",
    "    nthreads: int | None = None,\n",
    "    colorbar_color: str = \"Plasma256\",\n",
    "    colorbar_on: str,\n",
    "    colorbar_title: str,\n",
    "    pipeline: str,\n",
    "    highlight: list[str] | None = None,\n",
    "):\n",
    "    xs = [(module, str(i)) for i, module in enumerate(data[\"module_short\"], start=1)]\n",
    "    source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            x=xs,\n",
    "            module=data[\"Module\"].values,\n",
    "            func=data[\"Function\"].values,\n",
    "            module_short=data[\"module_short\"].values,\n",
    "            func_short=data[\"func_short\"].values,\n",
    "            func_id=data.index.values,\n",
    "            perc_cpu_time=data[\"perc_cpu_time\"].values,\n",
    "            cumperc_cpu_time=data[\"cumperc_cpu_time\"].values,\n",
    "            mean_cpu=data[\"mean_cpu\"].values,\n",
    "            std_cpu=data[\"std_cpu\"].values,\n",
    "            std_lower_cpu=data[\"mean_cpu\"].values - data[\"std_cpu\"].values,\n",
    "            std_upper_cpu=data[\"mean_cpu\"].values + data[\"std_cpu\"].values,\n",
    "            mean_mem_bound=data[\"mean_mem_bound\"].values,\n",
    "            std_mem_bound=data[\"std_mem_bound\"].values,\n",
    "            mean_fp=data[\"mean_fp\"].values,\n",
    "            std_fp=data[\"std_fp\"].values,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    p = figure(\n",
    "        x_range=FactorRange(*xs),\n",
    "        title=(\n",
    "            f\"Avg. CPU Time (seconds): {pipeline}\"\n",
    "            + (f\" with {str(nthreads)} threads\" if nthreads else \"\")\n",
    "        ),\n",
    "        width=1500,\n",
    "        toolbar_location=None,\n",
    "    )\n",
    "\n",
    "    # Tools\n",
    "    hover = HoverTool()\n",
    "    hover.tooltips = [\n",
    "        (\"Module\", \"@module\"),\n",
    "        # (\"Function\", \"@func\"),\n",
    "        (\"Module (short)\", \"@module_short\"),\n",
    "        (\"Function (short)\", \"@func_short\"),\n",
    "        (\"Function ID\", \"@func_id\"),\n",
    "        (\"Makespan contribution\", \"@perc_cpu_time{1.1}%\"),\n",
    "        (\"Makespan contribution (cumulative)\", \"@cumperc_cpu_time{1.1}%\"),\n",
    "        (\"CPU Time (mean ± std)\", \"@mean_cpu{1.11} ± @std_cpu{1.11}\"),\n",
    "        (\n",
    "            \"% of memory bound (mean ± std)\",\n",
    "            \"@mean_mem_bound{1.11} ± @std_mem_bound{1.11}\",\n",
    "        ),\n",
    "        (\"% of FP ops (mean ± std)\", \"@mean_fp{1.11} ± @std_fp{1.11}\"),\n",
    "    ]\n",
    "    p.tools.append(hover)\n",
    "\n",
    "    # Mean\n",
    "    color_mapper = LinearColorMapper(palette=colorbar_color, low=0, high=100)\n",
    "    color_bar = ColorBar(\n",
    "        color_mapper=color_mapper,\n",
    "        label_standoff=14,\n",
    "        location=(0, 0),\n",
    "        title=colorbar_title,\n",
    "        title_text_font_size=\"20pt\",\n",
    "    )\n",
    "    p.add_layout(color_bar, \"left\")\n",
    "    p.vbar(\n",
    "        x=\"x\",\n",
    "        top=\"mean_cpu\",\n",
    "        width=0.9,\n",
    "        source=source,\n",
    "        color=transform(colorbar_on, color_mapper),\n",
    "    )\n",
    "    # Error\n",
    "    error = Whisker(\n",
    "        base=\"x\",\n",
    "        upper=\"std_upper_cpu\",\n",
    "        lower=\"std_lower_cpu\",\n",
    "        source=source,\n",
    "        level=\"annotation\",\n",
    "        line_width=2,\n",
    "    )\n",
    "    error.upper_head.size = 20\n",
    "    error.lower_head.size = 20\n",
    "    p.add_layout(error)\n",
    "\n",
    "    # Cumulative makespan\n",
    "    p.extra_y_ranges = {\"percentage\": Range1d(start=0, end=100)}\n",
    "    p.add_layout(\n",
    "        LinearAxis(y_range_name=\"percentage\", axis_label=\"Cumulative CPU TIME (%)\"),\n",
    "        \"right\",\n",
    "    )\n",
    "    p.scatter(\n",
    "        x=\"x\",\n",
    "        y=\"cumperc_cpu_time\",\n",
    "        source=source,\n",
    "        y_range_name=\"percentage\",\n",
    "        color=\"lightgreen\",\n",
    "        size=10,\n",
    "    )\n",
    "\n",
    "    # # Bar labels\n",
    "    # bar_labels = LabelSet(\n",
    "    #     x=\"x\",\n",
    "    #     y=0,\n",
    "    #     text=\"func_short\",\n",
    "    #     x_offset=0,\n",
    "    #     y_offset=0,\n",
    "    #     angle=math.pi / 2,\n",
    "    #     source=source,\n",
    "    #     text_color=\"black\",\n",
    "    #     text_font_size=\"0.8em\",\n",
    "    #     background_fill_color=\"white\",\n",
    "    #     background_fill_alpha=0.5,\n",
    "    # )\n",
    "    # p.add_layout(bar_labels)\n",
    "\n",
    "    # Style\n",
    "    if not y_limit:\n",
    "        y_limit = 1.05 * (data[\"mean_cpu\"].values + data[\"std_cpu\"].values).max()\n",
    "    p.y_range.end = y_limit\n",
    "    p.y_range.start = 0\n",
    "    # p.y_range = Range1d(start=0, end=y_limit)\n",
    "    p.x_range.range_padding = 0.1\n",
    "    p.xaxis.major_label_orientation = math.pi / 2\n",
    "    p.xaxis.group_label_orientation = math.pi / 8\n",
    "    p.xaxis.separator_line_alpha = 0\n",
    "    p.xgrid.grid_line_color = None\n",
    "    p.yaxis.formatter.use_scientific = False\n",
    "\n",
    "    ## Font size\n",
    "    p.title.text_font_size = \"25pt\"\n",
    "    p.xaxis.group_text_font_size = \"12pt\"\n",
    "    p.yaxis.axis_label_text_font_size = \"20pt\"\n",
    "    p.yaxis.major_label_text_font_size = \"15pt\"\n",
    "\n",
    "    # Prevent overlap of x-axis labels\n",
    "    if (l := len(data)) > 120:\n",
    "        p.xaxis.major_label_text_font_size = \"6pt\"\n",
    "    elif l > 80:\n",
    "        p.xaxis.major_label_text_font_size = \"8pt\"\n",
    "    else:\n",
    "        p.xaxis.major_label_text_font_size = \"12pt\"\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from bokeh.palettes import Colorblind8\n",
    "from bokeh.transform import factor_cmap\n",
    "\n",
    "\n",
    "def plot_cpu_time(path: Path, *, nthreads: int = 1, dataset=None):\n",
    "    # Data preparation\n",
    "    data = pd.concat(\n",
    "        (\n",
    "            pd.read_csv(filename, delimiter=\"\\t\").assign(\n",
    "                filename=filename.stem,\n",
    "                pipeline=\"/\".join(filename.relative_to(path).parts[:2]),\n",
    "            )\n",
    "            for filename in path.rglob(f\"**/{dataset}/**/*.csv\" if dataset else \"*.csv\")\n",
    "        ),\n",
    "        ignore_index=True,\n",
    "    )\n",
    "\n",
    "    group = (\n",
    "        data.groupby([\"pipeline\", \"filename\"])[\"CPU Time\"]\n",
    "        .sum()\n",
    "        .divide(nthreads)\n",
    "        .groupby(\"pipeline\")\n",
    "        .agg([\"mean\", \"std\"])\n",
    "    )\n",
    "\n",
    "    source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            pipeline=group.index.values,\n",
    "            mean=group[\"mean\"].values,\n",
    "            std=group[\"std\"].values,\n",
    "            std_lower=group[\"mean\"].values - group[\"std\"].values,\n",
    "            std_upper=group[\"mean\"].values + group[\"std\"].values,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Plotting\n",
    "    cmap = factor_cmap(\n",
    "        \"pipeline\", palette=Colorblind8, factors=sorted(data[\"pipeline\"].unique())\n",
    "    )\n",
    "    p = figure(\n",
    "        x_range=group.index.values,\n",
    "        width=1500,\n",
    "        toolbar_location=None,\n",
    "        y_axis_label=\"Total CPU time (seconds)\",\n",
    "    )\n",
    "\n",
    "    p.vbar(\n",
    "        x=\"pipeline\",\n",
    "        top=\"mean\",\n",
    "        width=0.9,\n",
    "        source=source,\n",
    "        line_color=cmap,\n",
    "        fill_color=cmap,\n",
    "    )\n",
    "    # Error\n",
    "    error = Whisker(\n",
    "        base=\"pipeline\",\n",
    "        upper=\"std_upper\",\n",
    "        lower=\"std_lower\",\n",
    "        source=source,\n",
    "        level=\"annotation\",\n",
    "        line_width=2,\n",
    "    )\n",
    "    error.upper_head.size = 20\n",
    "    error.lower_head.size = 20\n",
    "    p.add_layout(error)\n",
    "\n",
    "    # Tools\n",
    "    hover = HoverTool()\n",
    "    hover.tooltips = [\n",
    "        (\"Pipeline\", \"@pipeline\"),\n",
    "        (\"Makespan (mean ± std)\", \"@mean{1.11} ± @std{1.11}\"),\n",
    "    ]\n",
    "    p.tools.append(hover)\n",
    "\n",
    "    # Appearance\n",
    "    p.left[0].formatter.use_scientific = False\n",
    "    p.y_range.end = 1.05 * (group[\"mean\"].values + group[\"std\"].values).max()\n",
    "    p.y_range.start = 0\n",
    "    p.xaxis.major_label_orientation = math.pi / 8\n",
    "    p.xgrid.grid_line_color = None\n",
    "    p.outline_line_color = None\n",
    "\n",
    "    ## Font size\n",
    "    p.xaxis.major_label_text_font_size = \"36pt\"\n",
    "    p.yaxis.axis_label_text_font_size = \"36pt\"\n",
    "    p.yaxis.major_label_text_font_size = \"36pt\"\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "\n",
    "from bokeh.palettes import Colorblind8\n",
    "from bokeh.transform import factor_cmap\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def parse_makespan(filename):\n",
    "    df = pd.read_csv(filename, delimiter=\"\\t\", on_bad_lines=\"skip\")\n",
    "    return float(df[df[\"Metric Name\"] == \"Elapsed Time\"][\"Metric Value\"].values[0])\n",
    "\n",
    "\n",
    "def plot_makespan(path: Path, *, dataset=None, pipeline_filter=None):\n",
    "    # Data preparation\n",
    "    data = defaultdict(list)\n",
    "    for filename in path.rglob(\n",
    "        f\"**/{dataset}/**/*.makespan\" if dataset else \"*.makespan\"\n",
    "    ):\n",
    "        pipeline = \"/\".join(filename.relative_to(path).parts[:2])\n",
    "        if pipeline_filter and pipeline not in pipeline_filter:\n",
    "            continue\n",
    "        data[pipeline].append(parse_makespan(filename))\n",
    "\n",
    "    pipelines = sorted(data.keys())\n",
    "    means = np.array([np.mean(data[pipeline]) for pipeline in pipelines])\n",
    "    stds = np.array([np.std(data[pipeline]) for pipeline in pipelines])\n",
    "\n",
    "    source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            pipeline=pipelines,\n",
    "            mean=means,\n",
    "            std=stds,\n",
    "            std_lower=means - stds,\n",
    "            std_upper=means + stds,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Plotting\n",
    "    cmap = factor_cmap(\"pipeline\", palette=Colorblind8, factors=pipelines)\n",
    "    p = figure(\n",
    "        x_range=pipelines,\n",
    "        width=1500,\n",
    "        toolbar_location=None,\n",
    "        y_axis_label=\"Makespan (seconds)\",\n",
    "    )\n",
    "\n",
    "    p.vbar(\n",
    "        x=\"pipeline\",\n",
    "        top=\"mean\",\n",
    "        width=0.9,\n",
    "        source=source,\n",
    "        line_color=cmap,\n",
    "        fill_color=cmap,\n",
    "    )\n",
    "    # Error\n",
    "    error = Whisker(\n",
    "        base=\"pipeline\",\n",
    "        upper=\"std_upper\",\n",
    "        lower=\"std_lower\",\n",
    "        source=source,\n",
    "        level=\"annotation\",\n",
    "        line_width=2,\n",
    "    )\n",
    "    error.upper_head.size = 20\n",
    "    error.lower_head.size = 20\n",
    "    p.add_layout(error)\n",
    "\n",
    "    # Tools\n",
    "    hover = HoverTool()\n",
    "    hover.tooltips = [\n",
    "        (\"Pipeline\", \"@pipeline\"),\n",
    "        (\"Makespan (mean ± std)\", \"@mean{1.11} ± @std{1.11}\"),\n",
    "    ]\n",
    "    p.tools.append(hover)\n",
    "\n",
    "    # Appearance\n",
    "    p.left[0].formatter.use_scientific = False\n",
    "    p.y_range.end = 1.05 * (means + stds).max()\n",
    "    p.y_range.start = 0\n",
    "    p.xaxis.major_label_orientation = math.pi / 8\n",
    "    p.xgrid.grid_line_color = None\n",
    "    p.outline_line_color = None\n",
    "\n",
    "    ## Font size\n",
    "    p.xaxis.major_label_text_font_size = \"20pt\"\n",
    "    p.yaxis.axis_label_text_font_size = \"20pt\"\n",
    "    p.yaxis.major_label_text_font_size = \"20pt\"\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import socket\n",
    "import warnings\n",
    "\n",
    "from selenium import webdriver\n",
    "\n",
    "\n",
    "class remote_driver:\n",
    "    def __enter__(self):\n",
    "        options = webdriver.ChromeOptions()\n",
    "        options.add_argument(\"--headless\")\n",
    "        options.add_argument(\"--window-size=1920,1080\")\n",
    "        options.add_argument(\"--ignore-ssl-errors=yes\")\n",
    "        options.add_argument(\"--ignore-certificate-errors\")\n",
    "\n",
    "        remote = socket.gethostbyname(socket.gethostname()) + \":4444\"\n",
    "        try:\n",
    "            self.driver = webdriver.Remote(remote, options=options)\n",
    "            self.driver.maximize_window()\n",
    "            return self.driver\n",
    "        except:\n",
    "            warnings.warn(\"Failed to initialize Selenium webdriver.\")\n",
    "            return None\n",
    "\n",
    "    def __exit__(self, exc_type, exc_value, exc_tb):\n",
    "        if hasattr(self, \"driver\"):\n",
    "            self.driver.quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Optional\n",
    "\n",
    "\n",
    "from bokeh.io import export_png\n",
    "from bokeh.plotting import output_file, save\n",
    "\n",
    "\n",
    "def show_and_export(\n",
    "    p: figure,\n",
    "    *,\n",
    "    fout: Optional[Path] = None,\n",
    "    driver: Optional[webdriver.Remote] = None,\n",
    "    hide_title: bool = False,\n",
    "):\n",
    "    if fout:\n",
    "        if hide_title:\n",
    "            p.title = \"\"\n",
    "        fout.parent.mkdir(parents=True, exist_ok=True)\n",
    "        export_png(p, filename=fout.as_posix() + \".png\", webdriver=driver)\n",
    "\n",
    "        # Save HTML file\n",
    "        output_file(filename=fout.as_posix() + \".html\")\n",
    "        save(p)\n",
    "\n",
    "    else:\n",
    "        return show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_profiling_data(path: Path, *, delimiter: str = \"\\t\") -> pd.DataFrame:\n",
    "    return pd.concat(\n",
    "        (\n",
    "            pd.read_csv(filename, delimiter=delimiter)\n",
    "            for filename in path.rglob(\"*.csv\")\n",
    "        ),\n",
    "        ignore_index=True,\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "    <style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"ccd45479-a8e2-4083-84d1-aad9e4b6597f\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    function drop(id) {\n      const view = Bokeh.index.get_by_id(id)\n      if (view != null) {\n        view.model.document.clear()\n        Bokeh.index.delete(view)\n      }\n    }\n\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n\n    // Clean up Bokeh references\n    if (id != null) {\n      drop(id)\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim()\n            drop(id)\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ccd45479-a8e2-4083-84d1-aad9e4b6597f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ccd45479-a8e2-4083-84d1-aad9e4b6597f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import output_notebook\n",
    "\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "figures_dir = Path(\"paper\", \"figures\")\n",
    "figures_dir.mkdir(parents=True, exist_ok=True, mode=0o755)\n",
    "\n",
    "tables_dir = Path(\"paper\", \"tables\")\n",
    "tables_dir.mkdir(parents=True, exist_ok=True, mode=0o755)\n",
    "\n",
    "profiling_dir = Path(\n",
    "    \"/\", \"mnt\", \"lustre\", \"mathdugre\", \"mri-bottleneck\", \"vtune_output\"\n",
    ")\n",
    "dataset = \"ds004513\"\n",
    "VERBOSE = False"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single-threaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2466675/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n"
     ]
    }
   ],
   "source": [
    "with remote_driver() as driver:\n",
    "    show_and_export(\n",
    "        plot_makespan(profiling_dir / \"1-threads\", dataset=dataset),\n",
    "        fout=figures_dir / \"makespan-1thread\",\n",
    "        driver=driver,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2466675/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n",
      "WARNING:bokeh.core.validation.check:W-1008 (PALETTE_LENGTH_FACTORS_MISMATCH): Palette length does not match number of factors: ['fsl/mcflirt'] will be assigned to `nan_color` gray\n",
      "WARNING:bokeh.core.validation.check:W-1008 (PALETTE_LENGTH_FACTORS_MISMATCH): Palette length does not match number of factors: ['fsl/mcflirt'] will be assigned to `nan_color` gray\n"
     ]
    }
   ],
   "source": [
    "with remote_driver() as driver:\n",
    "    show_and_export(\n",
    "        plot_cpu_time(profiling_dir / \"1-threads\", dataset=dataset),\n",
    "        fout=figures_dir / \"cpu-time-1thread\",\n",
    "        driver=driver,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2466675/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n"
     ]
    }
   ],
   "source": [
    "dataset = \"ds004513\"\n",
    "experiments: dict[str, tuple[tuple[str, float | None]]] = {\n",
    "    \"ants\": (\n",
    "        (\"brainExtraction\", 700),\n",
    "        (\"brainExtraction-fp\", 700),\n",
    "        (\"registrationSyN\", 2200),\n",
    "        (\"registrationSyN-fp\", 2200),\n",
    "    ),\n",
    "    \"fsl\": (\n",
    "        (\"fast\", None),\n",
    "        (\"mcflirt\", None),\n",
    "        (\"flirt\", None),\n",
    "    ),\n",
    "    \"freesurfer\": ((\"reconall\", None),),\n",
    "    # \"fmriprep\": ((\"full\", None),),\n",
    "}\n",
    "\n",
    "with remote_driver() as driver:\n",
    "    for toolkit, pipelines in experiments.items():\n",
    "        for pipeline, y_limit in pipelines:\n",
    "            profiling_data = read_profiling_data(\n",
    "                profiling_dir / \"1-threads\" / toolkit / pipeline / dataset\n",
    "            )\n",
    "            data = data_preparation(profiling_data, keep_ratio=0.8)\n",
    "            p = plot_hotspots(\n",
    "                data,\n",
    "                pipeline=f\"{toolkit}.{pipeline}\",\n",
    "                y_limit=y_limit,\n",
    "                nthreads=1,\n",
    "                colorbar_on=\"mean_mem_bound\",\n",
    "                colorbar_title=\"% of memory bound\",\n",
    "                colorbar_color=\"Plasma256\",\n",
    "            )\n",
    "\n",
    "            _filename = f\"hotspots-1thread-{toolkit}-{pipeline}\"\n",
    "            show_and_export(\n",
    "                p,\n",
    "                fout=figures_dir / f\"{_filename}\",\n",
    "                driver=driver,\n",
    "            )\n",
    "\n",
    "            out_data = data[[\"module_short\", \"func_short\", \"mean_cpu\", \"std_cpu\"]]\n",
    "            out_data.to_csv(tables_dir / f\"{_filename}.csv\")\n",
    "            if VERBOSE:\n",
    "                print(out_data.head(10).to_string())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-threaded (32 threads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2466675/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n"
     ]
    }
   ],
   "source": [
    "with remote_driver() as driver:\n",
    "    show_and_export(\n",
    "        plot_makespan(profiling_dir / \"32-threads\", dataset=dataset),\n",
    "        fout=figures_dir / \"makespan-32threads\",\n",
    "        driver=driver,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2466675/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n",
      "WARNING:bokeh.core.validation.check:W-1008 (PALETTE_LENGTH_FACTORS_MISMATCH): Palette length does not match number of factors: ['fsl/mcflirt'] will be assigned to `nan_color` gray\n",
      "WARNING:bokeh.core.validation.check:W-1008 (PALETTE_LENGTH_FACTORS_MISMATCH): Palette length does not match number of factors: ['fsl/mcflirt'] will be assigned to `nan_color` gray\n"
     ]
    }
   ],
   "source": [
    "with remote_driver() as driver:\n",
    "    show_and_export(\n",
    "        plot_cpu_time(profiling_dir / \"32-threads\", nthreads=32, dataset=dataset),\n",
    "        fout=figures_dir / \"cpu-time-32threads\",\n",
    "        driver=driver,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2466675/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n"
     ]
    }
   ],
   "source": [
    "experiments: dict[str, tuple[tuple[str, float | None]]] = {\n",
    "    \"ants\": (\n",
    "        (\"brainExtraction\", 1100),\n",
    "        (\"brainExtraction-fp\", 1100),\n",
    "        (\"registrationSyN\", 3000),\n",
    "        (\"registrationSyN-fp\", 3000),\n",
    "    ),\n",
    "    \"fsl\": (\n",
    "        (\"fast\", None),\n",
    "        (\"mcflirt\", None),\n",
    "        (\"flirt\", None),\n",
    "    ),\n",
    "    \"freesurfer\": ((\"reconall\", None),),\n",
    "    # \"fmriprep\": ((\"full\", None),),\n",
    "}\n",
    "\n",
    "with remote_driver() as driver:\n",
    "    for toolkit, pipelines in experiments.items():\n",
    "        for pipeline, y_limit in pipelines:\n",
    "            profiling_data = read_profiling_data(\n",
    "                profiling_dir / \"32-threads\" / toolkit / pipeline\n",
    "            )\n",
    "            data = data_preparation(profiling_data, keep_ratio=0.8)\n",
    "            p = plot_hotspots(\n",
    "                data,\n",
    "                pipeline=f\"{toolkit}.{pipeline}\",\n",
    "                y_limit=y_limit,\n",
    "                nthreads=32,\n",
    "                colorbar_on=\"mean_mem_bound\",\n",
    "                colorbar_title=\"% of memory bound\",\n",
    "                colorbar_color=\"Plasma256\",\n",
    "            )\n",
    "\n",
    "            _filename = f\"hotspots-32threads-{toolkit}-{pipeline}\"\n",
    "            show_and_export(\n",
    "                p,\n",
    "                fout=figures_dir / f\"{_filename}\",\n",
    "                driver=driver,\n",
    "            )\n",
    "            out_data = data[[\"module_short\", \"func_short\", \"mean_cpu\", \"std_cpu\"]]\n",
    "            out_data.to_csv(tables_dir / f\"{_filename}.csv\")\n",
    "            if VERBOSE:\n",
    "                print(out_data.head(10).to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FreeSurfer threading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2748803/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 4, 8, 16, 32)\n",
      "[1.0, 1.3237079309534956, 1.3554696405257673, 1.4890580446935193, 1.5647610165847086, 1.5782481813782403]\n",
      "[100.0, 66.18539654767478, 33.88674101314418, 18.61322555866899, 9.77975635365443, 4.932025566807001]\n"
     ]
    }
   ],
   "source": [
    "nthreads = (1, 2, 4, 8, 16, 32)\n",
    "freesurfer_threading = [\n",
    "    list(\n",
    "        profiling_dir.glob(\n",
    "            f\"{i}-threads/freesurfer/reconall/ds004513/sub-s036/*.makespan\"\n",
    "        )\n",
    "    )[0]\n",
    "    for i in nthreads\n",
    "]\n",
    "\n",
    "# Data preparation\n",
    "pipelines = [\n",
    "    \"/\".join(filename.relative_to(profiling_dir).parts[:2])\n",
    "    for filename in freesurfer_threading\n",
    "]\n",
    "makespans = [parse_makespan(filename) for filename in freesurfer_threading]\n",
    "speedups = [makespans[0] / makespan for makespan in makespans]\n",
    "efficiencies = [speedup / nthread * 100 for nthread, speedup in zip(nthreads, speedups)]\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        nthread=nthreads,\n",
    "        pipeline=pipelines,\n",
    "        makespan=makespans,\n",
    "        speedup=[np.nan] + speedups[1:],\n",
    "        efficiency=[np.nan] + efficiencies[1:],\n",
    "    )\n",
    ")\n",
    "\n",
    "# Plotting\n",
    "p = figure(\n",
    "    width=1200,\n",
    "    toolbar_location=None,\n",
    "    x_axis_label=\"# of threads\",\n",
    "    y_axis_label=\"Makespan (seconds)\",\n",
    ")\n",
    "\n",
    "p.line(\n",
    "    x=\"nthread\",\n",
    "    y=\"makespan\",\n",
    "    source=source,\n",
    "    width=3,\n",
    ")\n",
    "p.scatter(\n",
    "    x=\"nthread\",\n",
    "    y=\"makespan\",\n",
    "    source=source,\n",
    "    size=15,\n",
    "    legend_label=\"Makespan\",\n",
    ")\n",
    "\n",
    "# Tools\n",
    "hover = HoverTool()\n",
    "hover.tooltips = [\n",
    "    (\"Pipeline\", \"@pipeline\"),\n",
    "    (\"Makespan\", \"@makespan{1.11}\"),\n",
    "    (\"Speedup\", \"@speedup{1.11}\"),\n",
    "    (\"Parallel Efficiency\", \"@efficiency{1.11}\"),\n",
    "]\n",
    "p.tools.append(hover)\n",
    "\n",
    "# Cumulative makespan\n",
    "p.extra_y_ranges = {\"percentage\": Range1d(start=0, end=100)}\n",
    "p.add_layout(\n",
    "    LinearAxis(y_range_name=\"percentage\", axis_label=\"Parallel efficiency (%)\"),\n",
    "    \"right\",\n",
    ")\n",
    "p.line(\n",
    "    x=\"nthread\",\n",
    "    y=\"efficiency\",\n",
    "    source=source,\n",
    "    y_range_name=\"percentage\",\n",
    "    color=\"red\",\n",
    "    width=3,\n",
    ")\n",
    "p.scatter(\n",
    "    x=\"nthread\",\n",
    "    y=\"efficiency\",\n",
    "    source=source,\n",
    "    y_range_name=\"percentage\",\n",
    "    color=\"red\",\n",
    "    size=15,\n",
    "    marker=\"square\",\n",
    "    legend_label=\"Parallel efficiency\",\n",
    ")\n",
    "\n",
    "# Appearance\n",
    "p.left[0].formatter.use_scientific = False\n",
    "p.y_range.end = 1.05 * max(makespans)\n",
    "p.y_range.start = 0\n",
    "p.xgrid.grid_line_color = None\n",
    "p.outline_line_color = None\n",
    "p.xaxis.ticker = nthreads\n",
    "\n",
    "## Font size\n",
    "p.xaxis.axis_label_text_font_size = \"28pt\"\n",
    "p.xaxis.major_label_text_font_size = \"28pt\"\n",
    "p.yaxis.axis_label_text_font_size = \"28pt\"\n",
    "p.yaxis.major_label_text_font_size = \"28pt\"\n",
    "p.legend.label_text_font_size = \"28pt\"\n",
    "\n",
    "with remote_driver() as driver:\n",
    "    show_and_export(\n",
    "        p,\n",
    "        fout=figures_dir / \"makespan-freesurfer\",\n",
    "        driver=driver,\n",
    "    )\n",
    "print(nthreads)\n",
    "print(speedups)\n",
    "print(efficiencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Long-tail distribution (Global)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "profiling_data = read_profiling_data(profiling_dir / \"1-threads\")\n",
    "data = (\n",
    "    profiling_data.groupby([\"Module\", \"Function\"])\n",
    "    .agg(\n",
    "        mean_cpu=pd.NamedAgg(column=\"CPU Time\", aggfunc=\"mean\"),\n",
    "        std_cpu=pd.NamedAgg(column=\"CPU Time\", aggfunc=\"std\"),\n",
    "        mean_mem_bound=pd.NamedAgg(column=\"Memory Bound(%)\", aggfunc=\"mean\"),\n",
    "        std_mem_bound=pd.NamedAgg(column=\"Memory Bound(%)\", aggfunc=\"std\"),\n",
    "        mean_fp=pd.NamedAgg(column=\"Vectorization:% of FP Ops(%)\", aggfunc=\"mean\"),\n",
    "        std_fp=pd.NamedAgg(column=\"Vectorization:% of FP Ops(%)\", aggfunc=\"std\"),\n",
    "    )\n",
    "    .sort_values(by=\"mean_cpu\", ascending=False)\n",
    ").reset_index()\n",
    "data.index += 1\n",
    "data[\"perc_cpu_time\"] = data[\"mean_cpu\"] / data[\"mean_cpu\"].sum() * 100\n",
    "data[\"cumperc_cpu_time\"] = data[\"perc_cpu_time\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig, (ax, ax2) = plt.subplots(1, 2, sharey=True, facecolor=\"w\", figsize=(10, 5))\n",
    "fig.tight_layout()\n",
    "\n",
    "xlabels = data.index / len(data) * 100\n",
    "\n",
    "ax.plot(xlabels, data[\"mean_cpu\"], label=\"test\")\n",
    "ax2.plot(xlabels, data[\"mean_cpu\"])\n",
    "ax.set_xlim(-.1, 2.5)\n",
    "ax2.set_xlim(2.5, None)\n",
    "\n",
    "# hide the spines between ax and ax2\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax2.spines[\"left\"].set_visible(False)\n",
    "ax.yaxis.tick_left()\n",
    "ax2.yaxis.set_visible(False)\n",
    "\n",
    "d = 0.015  # how big to make the diagonal lines in axes coordinates\n",
    "# arguments to pass plot, just so we don't keep repeating them\n",
    "kwargs = dict(transform=ax.transAxes, color=\"k\", clip_on=False)\n",
    "ax.plot((1 - d, 1 + d), (-d, +d), **kwargs)\n",
    "ax.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)\n",
    "\n",
    "kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes\n",
    "ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs)\n",
    "ax2.plot((-d, +d), (-d, +d), **kwargs)\n",
    "\n",
    "fig.text(0.5, 0, \"% Functions\", ha=\"center\")\n",
    "fig.text(-0.01, 0.5, \"CPU Time (seconds)\", va=\"center\", rotation=\"vertical\")\n",
    "\n",
    "# Secondary y-axis\n",
    "ax11 = ax.twinx()\n",
    "ax22 = ax2.twinx()\n",
    "ax11.yaxis.set_visible(False)\n",
    "ax11.spines[\"right\"].set_visible(False)\n",
    "ax22.spines[\"left\"].set_visible(False)\n",
    "ax22.set_ylabel(\"Cumulative CPU Time (%)\")\n",
    "\n",
    "ax11.plot(xlabels, data[\"cumperc_cpu_time\"], c=\"orange\")\n",
    "ax22.plot(xlabels, data[\"cumperc_cpu_time\"], c=\"orange\")\n",
    "fig.legend()\n",
    "\n",
    "# fig.savefig(figures_dir / \"global_longtail.png\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "xlabels = data.index / len(data) * 100\n",
    "ax.plot(xlabels, data[\"mean_cpu\"], label=\"CPU Time (seconds)\")\n",
    "ax2.plot(\n",
    "    xlabels, data[\"cumperc_cpu_time\"], c=\"tab:orange\", label=\"Cumulative CPU Time (%)\"\n",
    ")\n",
    "ax.set_xlim(-0.1, 1.5)\n",
    "\n",
    "# Style\n",
    "ax.set_ylabel(\"CPU Time (seconds)\")\n",
    "ax2.set_ylabel(\"Cumulative CPU Time (%)\")\n",
    "ax.set_xlabel(\"Percentage of functions\")\n",
    "fig.legend(loc=\"upper right\", bbox_to_anchor=(0, 0, 1, 1), bbox_transform=ax.transAxes)\n",
    "\n",
    "# Show and Save the plot\n",
    "plt.tight_layout()\n",
    "plt.savefig(figures_dir / \"global-longtail.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.special import zeta\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "xlabels = data.index / len(data) * 100\n",
    "ax.loglog(xlabels, data[\"mean_cpu\"], label=\"CPU Time (seconds)\")\n",
    "\n",
    "x = np.linspace(*ax.get_xlim())\n",
    "s = 1.6\n",
    "ax.plot(\n",
    "    x,\n",
    "    x**-s / zeta(s),\n",
    "    label=f\"Zipf's law alpha={s}\",\n",
    "    linestyle=\"--\",\n",
    "    color=\"black\",\n",
    "    linewidth=2,\n",
    ")\n",
    "\n",
    "ax.set_ylabel(\"CPU Time (seconds)\")\n",
    "ax.set_xlabel(\"Percentage of functions\")\n",
    "fig.legend(loc=\"upper right\", bbox_to_anchor=(0, 0, 1, 1), bbox_transform=ax.transAxes)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figures_dir / \"global-zipf_law.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Values less than or equal to 0 in data. Throwing out 0 or negative values\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating best minimal value for power law fit\n",
      "Alpha: 1.462853667418846, Xmin: 0.05387635\n"
     ]
    }
   ],
   "source": [
    "import powerlaw\n",
    "import numpy as np\n",
    "\n",
    "# Assuming `data` contains the frequencies of words or other elements\n",
    "# that you expect to follow Zipf's law\n",
    "# data = np.random.zipf(a=1.5, size=1000) # This is just a placeholder for your actual data\n",
    "\n",
    "# Fit the data to a power-law distribution\n",
    "fit = powerlaw.Fit(data[\"mean_cpu\"])\n",
    "\n",
    "# Print the alpha parameter and the xmin value of the fit\n",
    "print(f\"Alpha: {fit.alpha}, Xmin: {fit.xmin}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loglikelihood Ratio: -17.482081785854312, p-value: 8.66881435379545e-05\n",
      "Loglikelihood Ratio: 6967.95190641087, p-value: 8.909262855567788e-115\n",
      "Loglikelihood Ratio: -48.49607193015156, p-value: 0.0\n",
      "Loglikelihood Ratio: -17.58742181701401, p-value: 0.013658366408679038\n",
      "Loglikelihood Ratio: 2443063.9454744537, p-value: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Assuming nested distributions\n"
     ]
    }
   ],
   "source": [
    "# Assess the goodness-of-fit\n",
    "R, p = fit.distribution_compare('power_law', 'lognormal')\n",
    "print(f\"Loglikelihood Ratio: {R}, p-value: {p}\")\n",
    "\n",
    "R, p = fit.distribution_compare('power_law', 'exponential')\n",
    "print(f\"Loglikelihood Ratio: {R}, p-value: {p}\")\n",
    "\n",
    "R, p = fit.distribution_compare('power_law', 'truncated_power_law')\n",
    "print(f\"Loglikelihood Ratio: {R}, p-value: {p}\")\n",
    "\n",
    "R, p = fit.distribution_compare('power_law', 'stretched_exponential')\n",
    "print(f\"Loglikelihood Ratio: {R}, p-value: {p}\")\n",
    "\n",
    "R, p = fit.distribution_compare('power_law', 'lognormal_positive')\n",
    "print(f\"Loglikelihood Ratio: {R}, p-value: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the empirical PDF\n",
    "figPDF = plt.figure(figsize=(8, 6))\n",
    "ax = figPDF.add_subplot(111)\n",
    "fit.plot_pdf(color='b', linewidth=2, label='Empirical Data')\n",
    "\n",
    "# Plot the theoretical PDF of the fitted power-law\n",
    "fit.power_law.plot_pdf(color='r', linestyle='--', ax=ax, label='Fitted Power-law')\n",
    "\n",
    "# Add Zipf's law theoretical plot (manually)\n",
    "x = np.arange(1, max(data[\"mean_cpu\"])+1)\n",
    "y = (x**(-fit.power_law.alpha)) / np.sum(x**(-fit.power_law.alpha))\n",
    "plt.plot(x, y, color='g', linestyle=':', label='Theoretical Zipf')\n",
    "\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Probability Density Function (PDF)')\n",
    "plt.legend()\n",
    "plt.savefig(figures_dir / \"zipf_law.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QQ Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Generate QQ plot\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "sm.qqplot(data[\"mean_cpu\"], line=\"s\", ax=ax, markersize=1)\n",
    "\n",
    "# Set plot title and labels\n",
    "ax.set_title(\"QQ Plot\")\n",
    "ax.set_xlabel(\"Theoretical Quantiles\")\n",
    "ax.set_ylabel(\"Sample Quantiles\")\n",
    "\n",
    "# Show and Save the plot\n",
    "fig.savefig(figures_dir / \"qq_plot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "    <style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"b378896f-5aea-41f2-9780-27705aef99c3\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    function drop(id) {\n      const view = Bokeh.index.get_by_id(id)\n      if (view != null) {\n        view.model.document.clear()\n        Bokeh.index.delete(view)\n      }\n    }\n\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n\n    // Clean up Bokeh references\n    if (id != null) {\n      drop(id)\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim()\n            drop(id)\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"b378896f-5aea-41f2-9780-27705aef99c3\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"b378896f-5aea-41f2-9780-27705aef99c3\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": ""
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     "output_type": "display_data"
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     "data": {
      "text/html": [
       "\n",
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     "metadata": {},
     "output_type": "display_data"
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Quantiles\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2776\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p2768\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p2769\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p2770\"},\"axis_label\":\"Theoretical Quantiles\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2771\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2772\",\"attributes\":{\"axis\":{\"id\":\"p2768\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2777\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p2773\"}}}]}}]}};\n  const render_items = [{\"docid\":\"df65f953-fe77-4bd4-b7f8-939a09740197\",\"roots\":{\"p2756\":\"f9b89b80-74f3-4656-866a-c56391e7f061\"},\"root_ids\":[\"p2756\"]}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p2756"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "\n",
    "n_quantiles = len(data)\n",
    "# n_quantiles = 100\n",
    "# Calculate quantiles\n",
    "data_quantiles = np.quantile(data[\"mean_cpu\"], np.linspace(0, 1, n_quantiles))\n",
    "theoretical_quantiles = stats.norm.ppf(\n",
    "    np.linspace(0, 1, n_quantiles)\n",
    ")  # For a normal distribution\n",
    "\n",
    "# Create a Bokeh plot\n",
    "output_notebook()  # For inline display in a Jupyter Notebook\n",
    "p = figure(\n",
    "    title=\"QQ Plot\", x_axis_label=\"Theoretical Quantiles\", y_axis_label=\"Data Quantiles\"\n",
    ")\n",
    "\n",
    "# Scatter plot for the QQ plot\n",
    "p.circle(theoretical_quantiles, data_quantiles, size=5)\n",
    "\n",
    "# Reference line (y=x)\n",
    "theoretical_quantiles = theoretical_quantiles[~np.isinf(theoretical_quantiles)]\n",
    "min_quantile = min(theoretical_quantiles)\n",
    "max_quantile = max(theoretical_quantiles)\n",
    "p.line([min_quantile, max_quantile], [min_quantile, max_quantile], color=\"red\")\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from scipy.stats import goodness_of_fit, genpareto\n",
    "\n",
    "\n",
    "# res = goodness_of_fit(genpareto, data[\"mean_cpu\"], statistic=\"ks\")\n",
    "# res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from scipy.stats import goodness_of_fit, lomax\n",
    "\n",
    "# res = goodness_of_fit(lomax, data[\"mean_cpu\"], statistic=\"ks\")\n",
    "# res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANTS Makespan: Single vs. Double Precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pipeline_filter = [\n",
    "#     \"ants/brainExtraction\",\n",
    "#     \"ants/brainExtraction-fp\",\n",
    "#     \"ants/registrationSyN\",\n",
    "#     \"ants/registrationSyN-fp\",\n",
    "# ]\n",
    "\n",
    "# with remote_driver() as driver:\n",
    "#     show_and_export(\n",
    "#         plot_makespan(\n",
    "#             profiling_dir / \"1-threads\",\n",
    "#             dataset=dataset,\n",
    "#             pipeline_filter=pipeline_filter,\n",
    "#         ),\n",
    "#         fout=figures_dir / \"makespan-ants\",\n",
    "#         driver=driver,\n",
    "#     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "path = profiling_dir / \"1-threads\"\n",
    "pipeline_filter = [\n",
    "    \"ants/brainExtraction\",\n",
    "    \"ants/brainExtraction-fp\",\n",
    "    \"ants/registrationSyN\",\n",
    "    \"ants/registrationSyN-fp\",\n",
    "]\n",
    "\n",
    "data = defaultdict(list)\n",
    "for filename in path.rglob(f\"**/{dataset}/**/*.makespan\" if dataset else \"*.makespan\"):\n",
    "    pipeline = \"/\".join(filename.relative_to(path).parts[:2])\n",
    "    if pipeline_filter and pipeline not in pipeline_filter:\n",
    "        continue\n",
    "    data[pipeline].append(parse_makespan(filename))\n",
    "\n",
    "pipelines = sorted(data.keys())\n",
    "means = np.array([np.mean(data[pipeline]) for pipeline in pipelines])\n",
    "stds = np.array([np.std(data[pipeline]) for pipeline in pipelines])\n",
    "\n",
    "plt.bar(\n",
    "    pipelines,\n",
    "    means,\n",
    "    yerr=stds,\n",
    "    label=[\"Double\", \"Single\", \"_Double\", \"_Single\"],\n",
    "    color=[\"tab:blue\", \"tab:orange\", \"tab:blue\", \"tab:orange\"],\n",
    ")\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.legend(title=\"Precision\")\n",
    "\n",
    "plt.savefig(figures_dir / \"makespan-ants.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value for ttest single vs. double (brainExtraction):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.270069975581398e-11"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"p-value for ttest single vs. double (brainExtraction):\")\n",
    "stats.ttest_ind(data[pipeline_filter[0]], data[pipeline_filter[1]]).pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value for ttest single vs. double (registrationSyN):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.5872343066780315e-43"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"p-value for ttest single vs. double (registrationSyN):\")\n",
    "stats.ttest_ind(data[pipeline_filter[2]], data[pipeline_filter[3]]).pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpolation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = \"ds004513\"\n",
    "experiments: dict[str, tuple[tuple[str, float | None]]] = {\n",
    "    \"ants\": (\n",
    "        (\"brainExtraction\", 700),\n",
    "        (\"brainExtraction-fp\", 700),\n",
    "        (\"registrationSyN\", 2200),\n",
    "        (\"registrationSyN-fp\", 2200),\n",
    "    ),\n",
    "    \"fsl\": (\n",
    "        (\"fast\", None),\n",
    "        (\"mcflirt\", None),\n",
    "        (\"flirt\", None),\n",
    "    ),\n",
    "    \"freesurfer\": ((\"reconall\", None),),\n",
    "    # \"fmriprep\": ((\"full\", None),),\n",
    "}\n",
    "\n",
    "interpolation_func = [\n",
    "    \"interpolation\",\n",
    "    \"interpolate\",\n",
    "    \"convolution\",\n",
    "    \"convolve\",\n",
    "]\n",
    "\n",
    "with open(tables_dir / \"interpolation.csv\", \"w\") as f:\n",
    "    f.write(\"pipeline,# of functions with interpolation,% CPU Time with interpolation\\n\")\n",
    "    for toolkit, pipelines in experiments.items():\n",
    "        for pipeline, y_limit in pipelines:\n",
    "            profiling_data = read_profiling_data(\n",
    "                profiling_dir / \"1-threads\" / toolkit / pipeline / dataset\n",
    "            )\n",
    "            data = data_preparation(profiling_data, keep_ratio=1)\n",
    "            interpolation_df = data[\"func_short\"].apply(\n",
    "                lambda x: any([f.lower() in x.lower() for f in interpolation_func])\n",
    "            )\n",
    "\n",
    "            f.write(f\"{toolkit}/{pipeline},{interpolation_df.sum()},{data[interpolation_df]['perc_cpu_time'].sum():.2f}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Memory boundness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataset = \"ds004513\"\n",
    "experiments: dict[str, tuple[tuple[str, float | None]]] = {\n",
    "    \"ants\": (\n",
    "        (\"brainExtraction\", 700),\n",
    "        (\"brainExtraction-fp\", 700),\n",
    "        (\"registrationSyN\", 2200),\n",
    "        (\"registrationSyN-fp\", 2200),\n",
    "    ),\n",
    "    \"fsl\": (\n",
    "        (\"fast\", None),\n",
    "        (\"mcflirt\", None),\n",
    "        (\"flirt\", None),\n",
    "    ),\n",
    "    \"freesurfer\": ((\"reconall\", None),),\n",
    "    # \"fmriprep\": ((\"full\", None),),\n",
    "}\n",
    "\n",
    "k = 10\n",
    "topk = dict()\n",
    "mem_bound_impact = dict()\n",
    "for toolkit, pipelines in experiments.items():\n",
    "    for pipeline, y_limit in pipelines:\n",
    "        profiling_data = read_profiling_data(\n",
    "            profiling_dir / \"1-threads\" / toolkit / pipeline / dataset\n",
    "        )\n",
    "        data = data_preparation(profiling_data, keep_ratio=1)\n",
    "        topk[f\"{toolkit}/{pipeline}\"] = data.sort_values(\n",
    "            \"mean_cpu\", ascending=False\n",
    "        ).head(k)[\"mean_mem_bound\"]\n",
    "        mem_bound_impact[f\"{toolkit}/{pipeline}\"] = {\n",
    "            \"memory\": (data[\"mean_mem_bound\"] / 100 * data[\"perc_cpu_time\"]).sum(),\n",
    "            \"l1\": (data[\"mean_l1_bound\"] / 100 * data[\"perc_cpu_time\"]).sum(),\n",
    "            \"l2\": (data[\"mean_l2_bound\"] / 100 * data[\"perc_cpu_time\"]).sum(),\n",
    "            \"l3\": (data[\"mean_l3_bound\"] / 100 * data[\"perc_cpu_time\"]).sum(),\n",
    "            \"dram\": (data[\"mean_dram_bound\"] / 100 * data[\"perc_cpu_time\"]).sum(),\n",
    "            \"store\": (data[\"mean_store_bound\"] / 100 * data[\"perc_cpu_time\"]).sum(),\n",
    "        }\n",
    "\n",
    "plt.violinplot(topk.values(), showmeans=False, showmedians=True)\n",
    "plt.xticks(range(1, len(topk) + 1), topk.keys(), rotation=90)\n",
    "plt.ylabel(\"% of memory bound\")\n",
    "plt.title(f\"Top {k} functions with the highest CPU time\")\n",
    "plt.savefig(figures_dir / \"topk-violinplot.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[19.78375  5.57625  0.885    1.17125  4.98625  0.5675   6.5975 ]\n"
     ]
    }
   ],
   "source": [
    "with open(tables_dir / \"memory_bound.csv\", \"w\") as f:\n",
    "    f.write(\n",
    "        \"pipeline, Memory Bound (%), L1 Bound(%), L2 Bound (%), L3 Bound (%), DRAM Bound (%), Store Bound (%), Disk Bound (%)\\n\"\n",
    "    )\n",
    "    for pipeline, v in mem_bound_impact.items():\n",
    "        disk_fetch = v[\"memory\"] - (\n",
    "            v[\"l1\"] + v[\"l2\"] + v[\"l3\"] + v[\"dram\"] + v[\"store\"]\n",
    "        )\n",
    "        f.write(\n",
    "            f\"{pipeline},{v['memory']:.2f},{v['l1']:.2f},{v['l2']:.2f},{v['l3']:.2f},{v['dram']:.2f},{v['store']:.2f},{disk_fetch:.2f}\\n\"\n",
    "        )\n",
    "\n",
    "df = pd.read_csv(tables_dir / \"memory_bound.csv\", index_col=0)\n",
    "print(df.mean().values)\n",
    "with open(tables_dir / \"memory_bound.csv\", \"a\") as f:\n",
    "    values_str = \",\".join([f\"{v:.2f}\" for v in df.mean().values])\n",
    "    f.write(f\"Average across pipelines,{values_str}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplified hotspot plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "from bokeh.models import (\n",
    "    ColumnDataSource,\n",
    "    FactorRange,\n",
    "    Whisker,\n",
    ")\n",
    "from bokeh.plotting import figure\n",
    "\n",
    "\n",
    "def plot_simple_hotspots(\n",
    "    data: pd.DataFrame,\n",
    "    *,\n",
    "    y_limit: float | None = None,\n",
    "    nthreads: int | None = None,\n",
    "    pipeline: str,\n",
    "):\n",
    "    \n",
    "    xs = [(module, str(i)) for i, module in enumerate(data[\"module_short\"], start=1)]\n",
    "    source = ColumnDataSource(\n",
    "        data=dict(\n",
    "            x=xs,\n",
    "            module_short=data[\"module_short\"].values,\n",
    "            func_id=data.index.values,\n",
    "            mean_cpu=data[\"mean_cpu\"].values,\n",
    "            std_cpu=data[\"std_cpu\"].values,\n",
    "            std_lower_cpu=data[\"mean_cpu\"].values - data[\"std_cpu\"].values,\n",
    "            std_upper_cpu=data[\"mean_cpu\"].values + data[\"std_cpu\"].values,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    p = figure(\n",
    "        x_range=FactorRange(*xs),\n",
    "        width=1920,\n",
    "        height=1080,\n",
    "        toolbar_location=None,\n",
    "        y_axis_label=\"CPU Time (seconds)\",\n",
    "    )\n",
    "\n",
    "    # Mean\n",
    "    p.vbar(\n",
    "        x=\"x\",\n",
    "        top=\"mean_cpu\",\n",
    "        width=0.9,\n",
    "        source=source,\n",
    "    )\n",
    "    # Error\n",
    "    error = Whisker(\n",
    "        base=\"x\",\n",
    "        upper=\"std_upper_cpu\",\n",
    "        lower=\"std_lower_cpu\",\n",
    "        source=source,\n",
    "        level=\"annotation\",\n",
    "        line_width=2,\n",
    "    )\n",
    "    error.upper_head.size = 20\n",
    "    error.lower_head.size = 20\n",
    "    p.add_layout(error)\n",
    "\n",
    "    # Style\n",
    "    if not y_limit:\n",
    "        y_limit = 1.05 * (data[\"mean_cpu\"].values + data[\"std_cpu\"].values).max()\n",
    "    p.y_range.end = y_limit\n",
    "    p.y_range.start = 0\n",
    "    p.x_range.range_padding = .01\n",
    "    p.xaxis.separator_line_alpha = 0\n",
    "    p.xgrid.grid_line_color = None\n",
    "    p.yaxis.formatter.use_scientific = False\n",
    "\n",
    "    if data[\"module_short\"].nunique() > 1:\n",
    "        p.xaxis.group_label_orientation = math.pi / 2\n",
    "        p.x_range.group_padding = 2\n",
    "        p.xaxis.major_label_standoff = -1000\n",
    "    else:\n",
    "        p.xaxis.major_label_standoff = -1000\n",
    "\n",
    "    ## Font size\n",
    "    p.xaxis.group_text_font_size = \"32pt\"\n",
    "    p.yaxis.axis_label_text_font_size = \"36pt\"\n",
    "    p.yaxis.major_label_text_font_size = \"32pt\"\n",
    "    p.xaxis.major_label_text_color = None\n",
    "\n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FreeSurfer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4159875/703094914.py:21: UserWarning: Failed to initialize Selenium webdriver.\n",
      "  warnings.warn(\"Failed to initialize Selenium webdriver.\")\n"
     ]
    }
   ],
   "source": [
    "toolkit = \"freesurfer\"\n",
    "pipeline = \"reconall\"\n",
    "\n",
    "with remote_driver() as driver:\n",
    "    for nthread in [1, 32]:\n",
    "        profiling_data = read_profiling_data(\n",
    "                profiling_dir / f\"{nthread}-threads\" / toolkit / pipeline\n",
    "            )\n",
    "        data = data_preparation(profiling_data, keep_ratio=0.8)\n",
    "\n",
    "        p = plot_simple_hotspots(\n",
    "            data,\n",
    "            pipeline=f\"{toolkit}.{pipeline}\",\n",
    "            y_limit=None,\n",
    "            nthreads=nthread,\n",
    "        )\n",
    "        _filename = f\"hotspots-{nthread}threads-{toolkit}-{pipeline}-simple\"\n",
    "        show_and_export(\n",
    "            p,\n",
    "            fout=figures_dir / f\"{_filename}\",\n",
    "            driver=driver,\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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