{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"font.size\"] = 16\n",
    "\n",
    "g_label_fontsize = 16\n",
    "\n",
    "colors = [\n",
    "    \"#5D7599\",\n",
    "    \"#233142\",\n",
    "    \"#F95959\",\n",
    "]\n",
    "\n",
    "edgecolors = [\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "    \"#FFFFFF\",\n",
    "]\n",
    "\n",
    "hatches = [\"\\\\\\\\\", \"\", \"\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 14 / 5), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"1-node\",\n",
    "    \"2-node\",\n",
    "    \"4-node\",\n",
    "    \"8-node\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MFU\n",
    "data_a = {\n",
    "    labels_name_a[0]: [40.1, 55.8, 62.4],\n",
    "    labels_name_a[1]: [39.6, 48.9, 55.2],\n",
    "    labels_name_a[2]: [38.2, 47.9, 54.0],\n",
    "    labels_name_a[3]: [38.1, 47.8, 53.2],\n",
    "}\n",
    "\n",
    "data_pa = []\n",
    "data_pb = []\n",
    "data_pc = []\n",
    "\n",
    "for v in data_a.values():\n",
    "    data_pa.append(v[0])\n",
    "    data_pb.append(v[1])\n",
    "    data_pc.append(v[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Megatron-LM\", \"XLA\", \"Lynx\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar_width = 0.2\n",
    "group_spaing = 0.15\n",
    "\n",
    "group_positions = {}\n",
    "current_pos = 0\n",
    "\n",
    "x_label_cnt = len(data_a.keys())\n",
    "x_label_group_cnt = 3\n",
    "\n",
    "for idx in range(x_label_cnt):\n",
    "    group_positions[idx] = []\n",
    "    for _ in range(x_label_group_cnt):\n",
    "        group_positions[idx].append(current_pos)\n",
    "        current_pos += bar_width\n",
    "    current_pos += group_spaing\n",
    "\n",
    "group_centers = {}\n",
    "for idx, positions in group_positions.items():\n",
    "    group_centers[idx] = sum(positions) / len(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 13.333333333333332, 'The number of node')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_set = set()\n",
    "\n",
    "x_a = []\n",
    "x_b = []\n",
    "x_c = []\n",
    "\n",
    "for idx, (x_label, y_data) in enumerate(data_a.items()):\n",
    "    positions = group_positions[idx]\n",
    "    x_a.append(positions[0])\n",
    "    x_b.append(positions[1])\n",
    "    x_c.append(positions[2])\n",
    "\n",
    "    for i, (pos, value, color, edgecolor, hatch, label) in enumerate(\n",
    "        zip(\n",
    "            positions,\n",
    "            y_data,\n",
    "            colors,\n",
    "            edgecolors,\n",
    "            hatches,\n",
    "            legend_labels,\n",
    "        )\n",
    "    ):\n",
    "        if label in label_set:\n",
    "            local_label = None\n",
    "        else:\n",
    "            local_label = label\n",
    "            label_set.add(local_label)\n",
    "\n",
    "        ax.bar(\n",
    "            pos,\n",
    "            value,\n",
    "            width=bar_width,\n",
    "            color=color,\n",
    "            label=local_label,\n",
    "            hatch=hatch,\n",
    "            edgecolor=edgecolor,\n",
    "        )\n",
    "\n",
    "ax.set_xticks(list(group_centers.values()))\n",
    "ax.set_xticklabels(list(data_a.keys()))\n",
    "\n",
    "ax.set_ylim(0, 105)\n",
    "ax.set_yticks([0, 50, 100])\n",
    "ax.set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
    "\n",
    "ax.plot(x_a, data_pa, color=colors[0], marker=\"x\", linestyle=\"--\", alpha=0.68)\n",
    "ax.plot(x_b, data_pb, color=colors[1], marker=\"v\", linestyle=\"--\", alpha=0.68)\n",
    "ax.plot(x_c, data_pc, color=colors[2], marker=\"^\", linestyle=\"--\", alpha=0.68)\n",
    "\n",
    "\n",
    "ax.tick_params(axis=\"x\", bottom=False, labelsize=g_label_fontsize, pad=1)\n",
    "ax.tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
    "ax.set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)\n",
    "ax.set_xlabel(\"The number of node\", fontsize=g_label_fontsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd1646275c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=3,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    bbox_to_anchor=(0.5, 0.97),\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YsWPlgw8+sCkGEZEjR45Ihw4d5ObNmyIiYjKZZNasWTJs2DDd75vnnntONmzYkO3nwBZTpkzJir1du3ayadMmzedZRMTPz0/Gjx8vY8aMsXjs4MGD0q9fP6fEBAAAAOD/c3dlBwAAAADAfnnljBVDhw7N+rXxG2+8oW7duqXZNykpyeJ0/GLHr3qHDBmS1e+tt96yut+xY8eUv7+/xboLFy6c67/ujY2N1T1bxMyZM5WIqAcffFDt3LlTd4xt27YpHx8fizFsuZTCW2+9pRmDrerXr28xTk6XArlr3rx5mu+B3r17qylTpigRUU888YQ6deqU7hjr16/X/fX96tWr1csvv6xERDVp0kT9/fffuuOcP39e8+wEXl5eKiYmxqr56J2xYsCAAVm/em/btq366aef1KlTp1RSUpKKiopSP/74o2ratGm2v3YPDAxUJ06csCqO7t27Z/WbNGmSVX2UuvNcap0dpHr16iojIyPbvi+++KJm3KNGjVIior7//nuz5U+dOmV2Vps6deqo6Ohoq2O1xY0bN1TlypXN4po7d67V/fft26f5eStVqpSKj4+3OR53nrHifpmZmSoyMlIznl9//VWFhISo8PBw9cMPP+iOce3aNc0znnh7e6vLly9bHcu7775r1r9t27ZW901NTVWNGjXSnMfPP/9s9Tg3b95UFSpUMOs/aNAgq/uPGzfO4TNW7Nu3L+s7rVy5curGjRtW9cvMzFStWrXSXPf8+fOtngMAAACA7FFYAQAAAAAeLK8UVgQFBSkfHx+Lg6haDh06pHkQ97nnnrNq/WvXrs3qX7hwYasPPt3Vp08fzedszJgxNo1jj5YtW1qsNyAgQPn6+qpu3bpZdUmPnj17asZ/6dIlq2IYNmyYUwortApkrC2suHXrluZ7oGTJksrf39+q95FSSo0YMULzuShXrlzWgVG9y2Dca/78+ZrjWHsQXquw4u4lFMqWLas2bdqUbf+pU6dme4mGTp065RjD3eIcEVHVqlVT6enpVsV+l9Z7U0TUwoULs+339ddfa/bz8/NTffv21e13+vRpdfTo0RwLNxzxyiuvmMVUq1YtlZmZadMYb775pub8spubnrxUWKGUUiNHjtSMJyQkRFWvXl2dPn06xzGmTZumOcZPP/1kVQzbtm2z+C7YvXu3TfPYs2eP8vLysoghIiJCxcbGWjXGoEGDzPpWrFhR99JSWlJTUzULtKwtrEhKSlJVq1a1+bvnrg0bNmiuu2bNmja/5wEAAABo41IgAAAAAACHJScny/z586Vv3745LlurVi2pX7++RfuRI0esWteIESNEKSUiIk8//bTmpQWy06hRI812Wy65YK+yZctatCUlJUnPnj3l559/lsDAwBzH6Natm2a7tc+fl5dz/hRgMpns7hsUFKR5CY74+HhZsWKFVe8jEZGXXnpJs/3MmTMyYcIEmThxou5lMO7VtWtXzVP5W/ucarlx44Y0atRIdu/eneOlLgYMGCALFizQjfWPP/6Q06dP6/bPyMiQkSNHZt3v37+/eHt72xSvvZ+LQoUKabaHhobKl19+qduvfPnyUr16dae9H+93+PBhi0vkvPTSSza/b0eNGqX5Xp05c6asX7/eoRjdTev76G775s2bpXz58jmO4cj3kVJKBg8enPV9LiJSr149iYyMzLHvverXr6/5XXDlypWsyytlZ9euXTJlyhSztkGDBuleWkqLr6+vvPjii1Yvf7+5c+fK8ePHRUQkPDxcunfvblP/hg0bar63jxw5Ihs3brQ7LgAAAAD/orACAAAAAOCwQYMGyVNPPWX18rVr17ZoO3fuXI799u3bJ1u2bMm637FjR6vXeVfJkiU128+cOSMnT560eTxbaBWBFC9eXGbNmmX1AWa9A+Dnz593KDZX0zog3717d2nZsqXVY5QqVUoqVKhg0d6sWTN58803rR7H399fatSoYdF+4cIFq8e4X+XKlWXt2rVStGhRq5bv2rWrfPHFF5qPZWZmyowZM3T7Ll26VM6cOZN135mfi+3bt8vt27d1++kdfB41apSEhYXZHIezjBo1SjIyMszannjiCZvHCQ0NlZdfflnzsc8//9yu2PIKvaK0+fPnS+HCha0aIyIiQsqVK2fRbs330fLly2XXrl1mbfa8RiIi77zzjmb7vHnzcswtb7/9tmRmZmbdDwgIkBdeeMHmGFq1amVzn7smTpyY9f/HHntMfH19beofEBCg+3lbs2aN3XEBAAAA+BeFFQAAAAAAh9l61ohSpUpZtMXHx5v9clnLV199ZXa/Zs2aNq1X5M7ZEvRs2rTJ5vFsoXUQ2s/Pz6azC5QuXVrzzAbXrl1zKDZX03ou7DkLRsWKFS3a7DkLQunSpS3a4uLibB7nrpIlS2b7XtPy5ptvSoMGDTQfW716tW6/adOmZf2/QIECUqVKFZvWK6L/uUhNTZUdO3bo9tM7ANy+fXubY3CWs2fPytKlS83aSpQoIWXKlLFrvF69emm2r169Wv766y+7xswL9IpibH3fahVWWPN9dG8xwV2NGze2ad13VapUSZo0aWLRnp6eLpMnT9btt3fvXrNiPRGRpk2b2lUUVKdOHZv7iIhs2bJFDh8+nHXfnrwmov+65XZeAwAAAPILCisAAAAAAC4XGhpq0aaUkps3b2bbb+3atWb3y5QpIyaTyaZbmzZtdMd35AwF1rD1V8havL29NZ+/5ORkh8d2JX9/f6eM46yzIhQsWNCiLSUlxSljW8vb21vef/99zccOHz4siYmJFu2pqalmB4ZTU1PFx8fH5s9FdpcxyO5zYeslR1xh9uzZFmerqFatmt3jValSRRo2bKj52MKFC+0e192c8X0kIppnt8jp++jcuXOaZ1Jw5HXSK4BZsGCBbh+t4g694qac6F0WJyf357Xhw4fb/Pk1mUy6ZwnJ7bwGAAAA5BcUVgAAAAAAXE7vl7VJSUm6fWJiYiQ6OjqXIrrj6tWruTq+sw5Ca/3SPC0tzSlju4o9Z5XQovere1v5+flZtLnjOe3cubNERERYtGdkZMi+ffss2vfu3ZvrBSDZfS7sOctIbvv1118t2rTOSGILvUvUbNiwwaFx3cmd30dar5HJZNK9JI019F6js2fPSlRUlEV7amqqLF682KK9bt26dsdgj23btuXq+Lmd1wAAAID8wvLcoQAAAAAA5DKtS1mI3Dltu57t27dbtK1fv153LHsUL17caWPlJq0DojldRiWvcdZB3dw8sO+O59TX11dat24tP/30k8VjMTExFm33fy4KFy5scRkMR5UtW1b3sbxWWHHjxg3NApTg4GCHxtU72L5z505JT0936veQp7Hn+2jdunUWbQEBAQ49j1WrVpXAwEC5ffu2xWNbtmyR8uXLm7Xt3r1bc1mtwqbcopSyuNTOkCFDpEuXLk5bR177jAIAAACeKv/u9QEAAAAA3Maewopz585ZtNWsWVOKFCnitLhyGwe4YI369etrFlbEx8dbtN3/uUhOTpZmzZrlWmx53YEDBzQP6mudkcQWeoUVKSkpEhsbKyVKlHBofHdw5/fR/v37LdocfY28vLykdu3aFoUKIqJ5mQy9s41oXWoptyQkJEhCQoJZW0RERL7+DAMAAAB5FZcCAQAAAADkGdn9ylnroPKNGzdyMxzALR588EHNdq3PwP1tiYmJ2RYoGd2JEyc027O7zJA1KleurFuIwKUWbJOWlqZ5WSdHXyORO2et0KL1Gh0+fFhzWUfPbmIL8hoAAADgOSisAAAAAAB4BK0DULGxsW6IBMhd4eHhmu1ahUdan4v8fKD/+vXrmu23bt1yaFwvLy/dA+73n3EA2dMrHEhOTpaMjAyHxtY724TWa6SXP7QuD5JbyGsAAACA56CwAgAAAADgEbQOdmmdTh7wdHoHh4OCgiza+FyYS01N1Wx3RvFDSEiIZrsrz3BgBHqvkYjjr5Mtr5FeAZIrC2X4/AIAAACeg8IKAAAAAIBH0DpgtmvXLjdEAuQub29vzfbixYtbtPG5MKd3to+oqCiHx9YreClcuLDDY+cneq+RiOOvky2vkd6lR+Li4hyKwRZan9+DBw9mW3wCAAAAwD0orAAAAAAAeIRChQpZtG3evNkNkQC5S+8X8xUrVrRo0/pcbNq0yekxeQq9Iod//vnH4ctMaBW8hIaGSqlSpRwaN78JCAiQgIAAzceOHj3q0Nh6RUk1atSwaNMr8Pjrr78cisEWWp/flJQU2blzp8tiAAAAAGAdCisAAAAAAB6hSJEiFm2nTp2SDRs2OGX8EydOSHp6ulPGAhyhVVjh5+cnNWvWtGjX+lysX79eTp8+7ZRY/v77b1FKOWUsV6hSpYpme0pKipw8edKhsRMTEy3aHnroIYfGzK/0XidHixq0XiMRkbp161q06RXh7N2716EYbKEXw3fffeeU8ZOSkpxythYAAAAAFFYAAAAAADyE3gHMGTNmOGX8/v37y0cffeSUsfIyLy/+FJDXnT9/3qKtQYMG4ufnZ9Gu9blQSsk333zjcByZmZny+OOPy8yZMx0ey1Vq166tezmIdevWOTS21kH7du3aOTRmfvXII49otufGa1SlShUpV66cRXvp0qU1x9i6datkZmY6FIe1/P39pVq1ahbtCxculPj4eIfHnz9/vrRo0cLhcQAAAABQWAEAAAAA8BD16tWTwMBAi/aff/7Z4dOmHzhwQDZv3qx5RgCj8fX1tWhz1pk6XHUw0ugOHDhg0fbkk09qLqt3gHrKlCkSHR3tUBy//fabREdHe9TnwsvLS/c5Wb58ud3jpqeny7Vr1yzaO3fubPeY+VnLli0123fv3i1Xrlyxe9xLly5ZtHXp0kVz2aZNm2q2x8TEyKpVq+yO4X45fb9qvV+TkpJk2LBhDq1XKSWTJ0/2qM8vAAAAkJdRWAEAAAAA8Ai+vr7SpEkTi/aMjAzp16+fpKSk2D32+++/L97e3vnil71axSl6p8+3VVpamlPGye/27dtndt/Pz0+eeeYZzWVLliwpFStWtGi/deuWvPjii3bHkJGRIR988IGEh4dLvXr17B7HHfr06aPZvm7dOs3LrFjj1KlTFu/vRo0aOeWgtSddasVZOnXqpHkZG6WU/Prrr3aPe+zYMYu2/v37ay7bvHlz3XGmT59udwz3S01NzfZxvTi++uor2bhxo93rXbBggRw4cEAeffRRu8cAAAAA8C8KKwAAAAAAHqNv376a7UePHpU+ffpIRkaGzWPOnz9fVq1aJZ06dZJixYo5GmKep3Uw88aNG3L79m2bxrl165ZFW3Jyst1x4Y7Lly/Lli1bzNr69u2b7XtT73Oxdu1aeffdd+2KY8yYMXL06FF54YUXxMfHx+b+7jx7yRNPPCGlSpWyaE9KSpKvv/7arjH/+usvi7YBAwbYNIZe4ZEjRWGeys/PT7fgYfLkyXaNmZmZaVFY0apVK81LbYiIlC9fXurWrav52G+//SZ//PGHXXHcLykpKdvHu3btKmFhYRbtSil59tln5eTJkzav8+rVq/LOO++Ir6+v9OrVy+b+AAAAACxRWAEAAAAAHkzvgFxOv5B1hDt/Xd2jRw8pXbq05mM//fSTvPDCCzadNWHXrl3yyiuviIjIG2+84ZQYs5MXfpn+wAMPWLQppeTgwYNW9VdKycCBA2Xv3r0Wj2kVW2Q3DizNnz/frEAoLCxMRo4cmW2f1157TfNMJCIiY8eOlffee8+m53vZsmUyfPhw8fb2ltdeey3bZfWKmWx5Lzibj4+PvP/++5qPTZo0ya5L39x/GZFq1arJc889Z9MYN2/etKk9t7n7Mzho0CAJDg62aD98+LBdl+LYunWrXL9+3azt448/zrbPW2+9pfvYq6++KrGxsTbHcb/7Y7pfwYIFs/LQ/S5evCgtW7aUf/75x+r13b59W3r06CEXLlyQnj17SkREhC3hAgAAANBBYQUAAAAAeDC9Szg469IOWrSKNuw5UGnt2Pfy9fXVPWAqIjJv3jxp0KCB7N+/P8d1zZ07V9q0aSM3b96UTp06SZs2bWyO11Zav1x21XN3V/Xq1TXbFy5cmGPf5ORkeeaZZ2Tq1KkSEhJi8fi5c+esikFEO157LiWidWDfnudU6wwLrr60SUJCgowZM8as7csvv8zxTCqFChXK9uwJY8aMkdatW8vp06ezHSczM1MmTJggTz75pKSnp8trr70mVatWzbaPXnFXdHR0tv1y24ABAyQyMtKi/dy5c/LVV1/ZNFZycrL89ttvZm1jxowRb29vm8a5dOmSZvuZM2dsGsdZ9M6k4IzvJGu+j0qWLCmffPKJ5mMffPCBzWc9uf87rFu3btK0adNs+/Ts2VPKlSun+di5c+ekTZs2EhcXZ9X67//s3nXo0KEc+w4aNEjzO1VE5MKFC1KvXj2ZMmVKjsUwJ06ckNatW8v69eslMDBQPv3005wDBwAAAGAVCisAAAAAwIPFxMRotl+8eDHX1qn161tnFXLEx8fnuMyAAQOkffv2uo8fPHhQGjRoIB06dJB58+bJsWPHJDExUW7fvi3Hjx+Xb775Rho1aiTPP/+83Lx5UwoWLCgTJkxwSvw5uXLlikWbs36tbu3Bv6JFi2qeGv+rr76yuATFvQ4cOCCNGjWSBQsWyPjx4zVPoX/8+HGLU/Hr0Xqtb9y4YVXfe2kd2Lfn/ag1jjXvRz03btyw+cDwBx98YPaZ7t+/v+5lPu73ySefSJ06dXQf37Bhg1SvXl26d+8uS5culZMnT0pSUpLcvHlTDh8+LBMnTpSaNWvK//3f/0laWpqUKlVKPvrooxzXe+HCBc32tWvXWhV3bvHy8pJvvvlG/Pz8LB778MMPbbq8wrhx4+Tq1atZ91988UXp3LmzzTFt27ZNs33Hjh02j+UMWt9HIs75TrL2++iNN96QJk2aWLTv3btXxo4da/X6Tp06ZXaZlxIlSsiMGTNy7Ofr6yuzZs0SLy/tP5EeOnRImjRpIrt27dId49atWzJgwAB57733NB9ftWqVHD16NOv+tWvXLAokSpYsmW28iYmJMnDgQKlSpYp88sknsmfPHrly5YqkpaXJpUuXZPny5dK3b1+pVauW7Ny5U0RERowYIWXKlNEdEwAAAICNFAAAAADAI509e1aJiOatUqVKLl1njx49bBpn4sSJmuP88MMPVvW/fPmyKlmypO78bbktXrzYnqfCLpGRkZoxxMfHWz1Genq6Kly4sMUYLVu2tHqMjz76SDOOAgUKqCFDhqhDhw6p5ORkFRMTo5YtW6a6du2qvL29lYio0aNHK6WUatGiheYYNWrUUPPnz1c//fSTeu2119SKFSss1p+SkqL8/Pws+lavXt3qOdzVuHFji3GKFCli8zhNmza1GCcwMFClp6fn2Ld3796az0XXrl1VYmKiVeufPn26Wd///Oc/Ki0tzaY5/P3336pgwYIOfyYKFCigtm/fnuP6UlJSVL169TTHCAwMVAcOHLAp/tzw3XffacZXvXp1FRsbm2P/zZs3q4CAgKx+Dz/8sNWv6b2ioqJUaGio7vN9+PBhe6bnkHfeeUczniVLltg0TteuXS3G8PPzUykpKVb1P3/+vCpevLjFGD4+Pmrp0qU59r9165bZ90BQUJDasmWLTXP44IMPsv1M+Pj4qKefflqtWrVKXbhwQSUlJanjx4+rMWPGqDJlyigRUQ0aNNDtHxQUpNq3b68aNGigvL291Y4dOzTjePHFF52S15544gmVmZlp03MAAAAAIHsUVgAAAACAhxo0aFC2B1bWr1/v9HUOHz5cc13h4eHq0qVLVo2RkpKiateurTlO+/btrY7lr7/+UsWKFXPo4NPnn39u71Nhs+PHjysvLy/NOL744gurx5k3b57mGL6+vur48eNWjREfH6+KFCli8/P15ZdfZo2hV1hx761+/fpq1apVFuv/9ttvdfvs3LnT6udi69atuuMsX77c6nF2796tTCaT5jjz58/Psf/o0aN146hRo4basGGDbt/MzEw1ZswYs/dG3759VWpqqtXx32vjxo0OFVf4+PioH3/8Mdt4r169qlasWKFatmyZ7VgFCxZUo0aNUocPH1YJCQl2zccZ/ve//2nGV6FChWzfbz/99JMKCQnJWv7hhx9WN27csGndly5dUjNmzFARERHZPleFCxdW06dPV1euXHF0ulZJTU1VFSpU0IylQ4cOVo8TFRWlChQooDnO999/b/U4R48e1S2uGDt2rG6B06lTp1STJk2ylg8ODlbr1q2zer13ZWRkqOeee87uz02dOnXU1atXc1yuadOm6tdff9UtekhLS1PdunWzOw4RUQ0bNrT5fQoAAAAgZxRWAAAAAICHiYqKUi+//HKOB1dCQkLUtGnTHDqgmZmZqa5du6a2b9+u3njjDd3CABFRFStWVIsWLVIxMTEqIyPDYqykpCS1e/du1alTp2zjfumll9TRo0et+rXzmTNndM8Ckd3N29tbTZ8+3e7nxRYJCQlq+fLlqkqVKtkezP7000/ViRMnNOedmZmpLly4oL7++msVFBSkO065cuXUr7/+qmJjY3P8tfKKFSuUj4+PVc+Xn5+fxdlE9AorAgICVJ8+fSwOWGdkZKhz586pKVOmmJ0B4P5b8eLF1YIFC9TFixc1D6ampqaq06dPq6+//lqFhYXpjhMYGKjGjBmjjh8/rpKTky3GSU5OVqdOnVLjxo3LtsjE399fjR07Vp0+fTrbs1ds2bJF1a9fX3ecNm3aqJkzZ6rjx4+r27dvq5iYGPXLL7+oZs2amcU8bdq0bF83axw8eFBVqlTJ5s9FQECA+u2337Idu1y5cnYf8J01a5bDc7PXokWLzIok7t68vLxUx44d1fTp09Wff/6pfv/9dzV+/Hizg/Umk0m99dZbNhW79OvXL9vvy+xuXl5eytvbW3l7e6vo6GinPg8pKSlq9+7dqkOHDtnG8Nxzz6l9+/apW7duaY4THx+vfv/992y/1wIDA9XkyZPV2bNnrTrzy9mzZ1WjRo00x6pcubIaOXKkWrZsmVqzZo2aM2eOev75582+S+rVq6dOnDhh93OTkZGhexaP7G5t2rRR169fV0opzcdNJpPq0qWL2rp1q9VxvPvuu7rFXtnd2rVrp/uaAQAAAHCMSan7LuoHAAAAAMhzatasKUopiYuLk0uXLtnU19vbW0qXLi0FCxaUBg0ayMyZM63uO2rUKBk5cqSN0d6xYcMGadGihZw5c0YqVqwoGRkZNo8xatQoGT58eLbLpKeny4QJE+Szzz6TuLi4HMds1qyZTJo0SerWrWtzPLby8fGxa96tWrWSdevWiYhI//795fvvv7dr/dHR0VK2bFndx5cvXy4vvPBCts9bzZo1ZdasWVK/fn2z9pYtW8rGjRuz7leuXFleffVV6du3r4SHh5stu3HjRmnZsqVdc7j3PfDRRx/JiBEj7Bqnb9++Wc+jI+PcfV9ryczMlF9++UUmT54smzZtsnpMLy8v6d69u3zxxRfZvl62SExMlE8//VQmTZokiYmJOS7fpUsX+fLLL6VixYrZLvfPP/9IamqqXTGVLl1awsLC7OrrDDExMfLBBx/I3LlzrZqDyWSSDh06yKeffioPPfSQTeu6cOGCxMfH2xnpv6pWrSq+vr4OjyMi8uijj2Z9r9jCZDJJZmamiIjMmTNH+vTpI/b8OXH27NnywgsvZLtMRkaGzJgxQ0aPHi0XLlywatzy5cvLhx9+KL179xZvb2+b47rfqlWrZPDgwXLs2LFslwsKCpJhw4bJkCFDxMvLS0TuPFd3FShQQJ5//nkZMmSIVKtWzeY4Nm/eLEOGDJGdO3fmuGyRIkXk008/lRdffDErFgAAAADORWEFAAAAAHiAI0eOOGWcoKAgKV++vNXLX7lyRa5cuWLXusqXLy9BQUGSlpYmx48ft2uMiIgIiYiIsGrZpKQkWbx4sSxfvlz27dsn58+fl9TUVClatKiUKlVKWrRoIf/5z3+kadOmdsViD3tft3tfJ0cO0FpzUPbKlSsyZcoUWbZsmZw4cUJSU1OlRIkS0qhRI3n66aela9eumgfqWrZsKVu3bpXOnTvLa6+9Jm3atDE7qHivxMREiYqKsmsO974HHHk/hoeHS6lSpRwe5+77OieHDx+WJUuWyPr162X79u2SkpJi9riXl5fUqVNHOnbsKL1795ZKlSrZFU9Orl+/LgsWLJBVq1bJwYMH5eLFi5KZmSnFihWT0qVLS5s2beTJJ5+UOnXq5Mr686JLly7J4sWLZd26dXLkyBG5ePGiJCUlSVBQkBQpUkTq1KkjTZs2le7du0uZMmXcHa7TREVFWVVko6VmzZoicuf9dP78ebvGsKWwJj09XVasWCF//PGH7N27V06dOiU3b94Ub29vCQ8Pl8qVK0uDBg2kc+fO0rx5c93vHntlZmbK8uXLZfHixbJjxw45e/aspKWlSaFChaRWrVry+OOPS58+faRQoUJm/Uwmk4SEhMgrr7wigwcPlpIlSzocy/bt22XRokWyZcsWOXnypCQkJEjBggWlRIkSUqtWLenatat06tRJgoODHV4XAAAAAH0UVgAAAAAAAOSijIwMiY2NlStXrkhSUpKEhoZKuXLlxN/f392hAQAAAAAAK1BYAQAAAAAAAAAAAAAAoIOL7gEAAAAAAAAAAAAAAOigsAIAAAAAAAAAAAAAAEAHhRUAAAAAAAAAAAAAAAA6KKwAAAAAAAAAAAAAAADQQWEFAAAAAAAAAAAAAACADgorAAAAAAAAAAAAAAAAdFBYAQAAAAAAAAAAAAAAoIPCCgAAAAAAAAAAAAAAAB0UVgAAAAAAAAAAAAAAAOigsAIAAAAAAAAAAAAAAEAHhRUAAAAAAAAAAAAAAAA6KKwAAAAAAAAAAAAAAADQQWEFAAAAAAAAAAAAAACADgorAAAAAAAAAAAAAAAAdFBYAQAAAAAAAAAAAAAAoIPCCgAAAAAAAAAAAAAAAB0UVgAAAAAAAAAAAAAAAOigsAIAAAAAAAAAAAAAAEAHhRUAAAAAAAAAAAAAAAA6KKwAAAAAAAAAAAAAAADQQWEFAAAAAAAAAAAAAACADgorAAAAAAAAAAAAAAAAdFBYAQAAAAAAAAAAAAAAoIPCCgAAAAAAAAAAAAAAAB0UVgAAAAAAAAAAAAAAAOigsAIAAAAAAAAAAAAAAEAHhRUAAAAAAAAAAAAAAAA6KKwAAAAAAAAAAAAAAADQQWEFAAAAAAAAAAAAAACADgorAAAAAAAAAAAAAAAAdFBYAQAAAAAAAAAAAAAAoIPCCgAAAAAAAAAAAAAAAB0UVgAAAAAAAAAAAAAAAOigsAIAAAAAAAAAAAAAAEAHhRUAAAAAAAAAAAAAAAA6KKwAAAAAAAAAAAAAAADQQWEFAAAAAAAAAAAAAACADgorAAAAAAAAAAAAAAAAdFBYAQAAAAAAAAAAAAAAoIPCCgAAAAAAAAAAAAAAAB0UVgAAAAAAAAAAAAAAAOj4f5mnRQygAqQsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"scalability.pdf\", bbox_inches=\"tight\", dpi=1000)\n",
    "fig"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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