paper_note/lynx/scalability.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(7, 14 / 6), ncols=1, nrows=1, constrained_layout=True, dpi=300\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_name_a = [\n",
    "    \"1-node\",\n",
    "    \"2-node\",\n",
    "    \"4-node\",\n",
    "    \"8-node\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"Megatron-LM\", \"XLA\", \"Lynx\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 13.333333333333332, 'The number of node')"
      ]
     },
     "execution_count": 25,
     "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, 110)\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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6599ff2a50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(\n",
    "    ncol=3,\n",
    "    loc=\"upper center\",\n",
    "    frameon=False,\n",
    "    shadow=False,\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pvr6+Do3t6empkiVLauDAgWrYsKF1ub+/vzZt2qQBAwaobNmy1uUhISEaN26cqlevrg4dOujHH39M8wKbw4cPq169evryyy8VHx9vdzvDMDRhwgS9//77adb73nvv6eTJk1q+fHmKeu3p0qWLTpw4kWrcypUr6/r16/r888/tPm/Lli1M4QoADiBYkQ/ExcXp5ZdfVtu2bXXkyJFUYQp7spJgtBWyWL58uWrWrGmdg3r69OmqXbt2hm5dunRx9o8HcEheO5nvLqEEdzkOd2Pviu1du3bp999/z9LYp0+f1ooVKyRJRYsWVb9+/bI0Xno++eQTPf300woLC1PDhg11+PBhLV++XH379lXr1q3VsWNHjR49WsHBwXrllVdSPX/dunVq1qyZIiIiHN5nQkKCOnXqlGIe6SeeeELbtm1Ty5Yt5eFh+fPMZDKpadOm2rx5c4p2yOlJTEy0hjVsGT58uD7++GPrtAU+Pj764YcftHTpUvXs2VOtWrVSly5d9Mknnyg4ONjmFYIff/yx1qxZY3cfCxYsSBXGrFSpUqrtPvzwQ7vhzT59+igoKEilS5dW79699euvv6Y71cLJkyf1+OOPp9vR49SpU3bXXb9+Xc2aNbNeAenj46NJkybpyJEj+uCDD9ShQwe1a9dOAwYM0MqVK3XkyBE1atQoxRhXr15Vx44dNXXq1DTrmDx5sipVqqSuXbtqw4YN6QZRbt68qdatW2vevHlpbpdcUt1wHnd6DQQAOKZIkSKaM2eO3fWvvfaazpw5o169elnDnQ8//LDGjBmTUyXmWcuWLbOGSlu3bq2aNVNPx9mhQwdVrlzZ5vNnzJiRneUBAFxo/Pjxat68uc11lStXlr+/f6bHTn4BwjvvvJMiQB8aGqoePXqoYsWKevvtt9P8DiHJ77//rqZNm+rff/91uIaxY8dq//79dtd7eXmpSpUqeuqpp7Rjx440gyaSNGrUKFWtWtVmOL9YsWJ69dVXVatWrRTLJ06cqNWrV6t58+YqWrSow7UDQH5FsMLNXbt2Ta1atdLUqVOtJyNuD0Q40pkio7fkkgcsbt68qRdeeEHDhw93ylWsQE7Kayfz3SWU4C7H4U6eeuopmyfJJcuH3qz44osvrOn/wYMHq0CBAlkaLy1Tp07Vu+++K0lq166d/vjjD9WoUcPmtr6+vvriiy9sHt+BAwf0wgsvOLzfQYMGaf369dbHFSpU0MKFC1N0PkjOZDJp1qxZql27tt3akt8CAgLUpEkTm9t+//33qa5QePPNN9WxY0eb2xcsWFDLly9XmTJlUq175513bD7HmWrUqKFvv/1WV69eVURERIowyu3i4+P19NNPKzo6WsuXL1doaKg++eQT+fj4pNq2WbNmNscIDw/Xww8/rH379kmy/OyXLl2ql156yW7XgGrVqumPP/7Qww8/nGK5YRj6v//7vxRXYN7OZDKpR48eWrBggcaOHatChQrZ3TY8PFzt27fX1q1bVbRoUb3xxhvavn27bt26paioKO3du1e9evWy+dwZM2bo6tWrdsdGxrjLayAAIGPat2+fatqPJLdu3VKDBg30888/S5LKly+vZcuW0XXIAdOmTbPe/7//+z+b23h4eOjFF1+0ue6HH36w244dAJC3eXp6at68eQoICEi17t9//1VCQkKmxg0PD7cGIHx9fTV48OAU6wsXLizDMDRmzBgtWrRIPXv2THO833//XY899phu3rypevXqadasWTp+/Lh1WtKVK1fa/L7JbDY7HMI0mUyaOHGinnvuObvbpBfquH79uk6ePGl9PGbMmHTDGgCAlLzS3wR5VUhIiB5++OEUXSokpQg0FC5cWDVr1tQ999yjqlWrqnTp0ipVqpR1bvekm6enpxITE2U2mxUTE6PY2FhFRETo1q1bCgsL09WrV3XlyhWdO3dOp0+fVnBwcKrWUcn3/+WXX+qBBx7IuR8G4CQ9O1pOVq7/45CLK3FMUp1JdSfhOJAVnp6eevXVV+12cAgKCrIbAkhLaGio9Up8X19fu1+sOsP+/fs1fPhwSZarHJYvX+5Q68gRI0Zo3bp12rx5c4rl3333nZYtW6YePXqk+fy1a9em6jYwZMgQFSxYMM3neXt7a/LkyalO3ptMJq1fv14tW7ZMt3bJMlfp7bp165bmcwoVKqSBAwdq9OjRKZYHBQXpxIkTqlatmkP7zgwfH58U05oMGzZMP/zwg3799ddU2yZd6bF+/XrrXKxvv/229STI3r17JUktW7bU2LFjbe5vwIABOnjwoPVx//791bVrV4fqXL16te67775UV7IMHjxYderUUePGjVM97/bf8cDAQJu/9/Hx8eratat27Nih/v37a8KECSpSpEiKberVq6evv/5ahQoV0vTp01Osi42N1aJFi6y/88gad3gNBABkzsSJE7VhwwadOXMm1bqkEKO3t7e+++47lSxZMqfLy3N27txp7bRWpUoVu2FfSerXr58+/PBDxcbGpliekJCgr776KtV88gAA91CtWjWNGDEi1XcS4eHh+vXXX9W+ffsMj7lmzRrr+0nXrl1VvHjxFOs9PDz07bffWh/36tVL//77r3U61eQOHjyo3r17y9fXV7NmzUp1wUOxYsXUtWtXtWzZUvfdd5/OnTuXYv2PP/6okJAQlSpVyqHaZ86cqQMHDujAgQOp1o0ePVpdunSRl5ft035jxoxRdHS0JKlnz57pTvMKAEiNjhVuKi4uTp07d9Y///wjSdaOEX5+fnryySc1ffp0HTlyRKGhofrrr780Z84cvfPOO+rXr58ef/xxNW7cWPfff7/uueceVatWTZUrV1a1atV05513qk6dOqpfv75atmypTp066bnnntPw4cM1fvx4LV26VNu3b9e1a9d0/vx5/fjjj3rjjTdUu3ZtazeLpFr27t2rd955R0FBQQ7fvv/+e9f+YJHvrNyQup1+XuuU4C4dH9zlONxFv3797LYIzOwV2zNnzrROqdG7d2+bXRKcISYmRs8884zi4uIkWaa1SKtTQHImk0kffvihzXUff/xxmt2YDMOweWI7rS+Qk2vVqpXuuuuuVGM6evJ13759Cg4OTrXckWNPHm5ILunvjJxkb17RTz75RB988IE1VJHk/vvv1549e3TkyBEFBwdr8+bNqUIJkqUNdfIvTnx8fDL0BX2hQoVszleamJioF154IdVJAFvszcE+depUbdmyRYsWLdLs2bNt1p9kzJgxNtuh2gqjIPPy8msgACDzChYsqHnz5qU5NeqHH35ot3sYUkrejWzo0KHWKfFsKVGihLp3725z3ezZszN91TIAIPcbPny4ChcunGp5ZqeDSj69l71uVMmZTCa739307t1b5cuXT7OLpGQJWNjq/JmYmJjq4p20+Pv7a8mSJfLz80u17tChQ3Y/j+7Zs8c6XWmtWrXSnOIMAGAfwQo39e6772rnzp3WEEPlypU1c+ZMXb58Wd99951efPHFVCdnnK1cuXLq2LGjxo0bp4MHD+rff//VsGHD5OfnZ63ryy+/lL+/v2rVquXQrXr16tlaM3C737YfcYuT+e4SSnCX43AHgYGBqVolJlm2bFmqBH564uLirF+smkymbL2yfvHixTp27JgkqWjRonrqqacy9PyGDRva/DI9KChIW7Zssfu8PXv26OjRoymWeXt725xH2h5bXyYHBQXpzz//TPe5SfN93y6tEwNJ7NV4+fLldJ/rbMWKFbO5vHr16nr77bftPu/uu++2+3dEdHS03nzzzRTLOnbsqHLlymWoti5duqht27aplh85ckSffPJJus+vWLGizRMK0dHRWrVqlXr37p3uGMWKFbPZwSQoKCjd58Jxefk1EACQNQ8//LCGDh1qd/3GjRuZ+tQBISEh+u677yRJAQEB6tevX7rPGTJkiM3lFy9e1A8//ODU+gAAuUehQoVsfv766aefUn3Pkp7jx4/r999/lyTdeeedatWqlUPPszcdZKVKlbR161ZVqVIl3THsdcTM6Of1e+65x24nzjFjxqS6qCYmJkbPP/+8EhMTFRAQoOXLl9u8IAMAkD6CFW4oODhYX375pUwmkzw8PDRy5EgdO3ZMAwcOTLfVeHaqXr26Jk+erAMHDlinAYmJidGrr77qspoAR7jLyXyOA872f//3fzanz4iPj9cXX3yRobEWL16sS5cuSbKc0L777rudUqMtkyZNst5v06aNvL29M/R8f39/ux0D0uoK8NNPP6VaVrhwYbstGm2pX7++zeWOXN1grzOFI22qCxUqpMDAwFTLb968me5znc3eh//PPvssw/+WSebOnauzZ8+mWNa5c+dMjWVruhXJckVmZGRkms/18PBQgQIFUi1/+eWXHe5sIkmNGjVKtezSpUsym80Oj4H05dXXQABA1n366ad2A5tbtmzRtGnTcriivOerr76ydpDr3bt3mh25kiR1V7Xl9qnQAADu5eWXX071+ctsNuvTTz/N0DizZ8+2BiAd6VaRxN53KkuXLk01lYg9pUqVUuXKlVMtP3/+vMN1JHnppZf00EMPpVoeExOTKoj49ttvWzuOTpo0Sffcc0+G9wcAsCBY4YYmTZqkxMREeXh4aOnSpfrggw8ydNImu1WvXl2bN29WgwYNZBiGfvrpJ5e0Egcywl1O5nMccKYyZcrYvYJ+9uzZCgsLc3isiRMnWu+//vrrWS3Nrm3btunQoUPWx7Vr187UOLZOfkvSH3/8Yfc5J0+eTLXM1knZtNir9/Dhw+k+t3HjxqmmLmjcuLECAgIc2retY076Mjwn2QtPZGZeVckynUryNtRJGjdunKnxWrVqpfLly6daHhoaqoULF6b7fFvBEXu/b/bY+qImMTExQ/9PIn158TUQAOAcAQEBGjZsmN31b731lk6dOpWDFeUtCQkJmjlzpvVxWj/L29nrFvLbb79Zu9IBANxPmTJl1KdPn1TLFy9e7HDHwPj4eC1YsECSZfpPW+PZY+8iD2d8Xr9+/XqGxpAsF2YsWLDA5nc6v/76q5YtWybJ8v6YdIFRt27d1L9//wzvCwDwH4IVbmjlypUymUwaOHCgunXr5upybCpQoIC+/fZb61xgixYtcnFFQPrc5WQ+xwFnGj58uM2pJCIiIhy+amzdunXWYECjRo3UrFkzp9aY3KZNm1I8/uCDD2QymTJ8s3c1wYULF+zuOyQkJNWy8PDwDNVv70q+0NDQdJ9buHBhLV26VGXLlpUk3XfffZo3b57D+7Y1f2d8fLzDz3cWT09Pp463bds2/fvvvymWeXl5ZXr6MQ8PDz377LM213377bfpPj+zXTeSs3e1TExMTJbHRkp57TUQAOAcJ06c0MiRI+2uj4yMVL9+/ZgSxI7Vq1db/25u1apVhsLOzzzzjN2/ielaAQDubcSIEammz4yPj08RVE/Ljz/+aP1upkuXLg518EzijM/qku3P65n9rF6tWjWNGzfO5rrXXntNZ86c0fPPPy/DMHTHHXdo9uzZmdoPAOA/BCvczPnz53XlyhVJGWtl5QqVK1dW3759ZRhGmq3TgdzEXU7mcxxwlrvvvluPP/64zXWTJ0926MPhZ599Zr0/YsQIp9Vmy19//ZWt41+7ds3uOh8fn1TLwsPDlZCQ4PD49lpPOjo3Zrt27XTx4kVFRERo//79qlmzZrrPiYiI0IIFC3Tjxo1U61xxssDWSeyssDUfd9myZVN9WZMRLVu2tLl8586d6f4/4YzgiL3fB1cEYdxdXnsNBABkXVRUlLp27WrtTGQrfCpZpmqbMWNGDlaWd0ydOtV6/6WXXsrQcwMCAtS3b1+b6xYtWqSoqKgs1QYAyL2qV6+uJ598MtXy2bNnp/l9TJKvvvrKen/gwIEZ2rezLvKw9Xk9K5/Vhw0bphYtWqRafunSJdWrV0/nz5+Xp6enFi9e7NC0WwCAtBGscDOXL1+23s8Lc2Ulte2mRSbyEnc5mc9xwFnsta2/cuVKulMf7Nu3T7/99pskS9K+a9euTq8viWEY2rFjR4plr7/+urZu3eq029q1a+3uv0qVKjZrcmQajyTR0dE2l9uaeiItjrSq3LFjh/r166eyZcuqb9++Ge6ukV2cHaxI+v1LrmDBglka097c37Gxsdq9e3eWxnaEvS98uGo2e+SV10AAgHMMHDhQBw8elCT17NlTixcvtrvtm2++qdOnT+dQZXnDoUOHrNPnVapUyW5AMS2DBw+2+TdhWFiYlixZkuUaAQC511tvvZVqWVRUlCZPnpzm806fPq2NGzdKsgQ0Hn744WypLz22Pq9n5bO6yWTSvHnzbH7Pk3SBzHvvvaeHHnoo0/sAAPyHYIWbST5Xu72TL7lJ0hxgXFGAvMZdTuZzHHCGhx56SI0bN7a5buLEiTKbzXafm/xK7ddeey1LXQLSc+vWLd26dSvFslKlSumhhx5y2q1p06Z2929v3a5duxw+BntXYDRv3tzhMdISGRmpWbNm6d5771WTJk00b948xcTE6Mknn1TRokWdso/cJD4+3mawJfnfU5lRpkwZlSlTxuY6e9PIJHF2cMRRv//+e6amxUl+y8j8tO4kr7wGAgCybsqUKdYgRe3atTV79mx17dpV3bt3t7l9RESE+vfvT7gxmSlTpljvDxkyJFNXAN95551q06aNzXV0CQEA91avXj21bt061fJp06YpIiLC7vPmzp1rfT/u379/hj97u+qzuiOqVq2qTz/91Oa6gIAAuxcDAAAyjm/u3EyFChWsb/JJVwDkZgcOHJAklShRwsWVABnnLifzOQ44g70PacHBwVq9erXNdWfPntV3330nyfI+YK+lr7OEhoamWnbz5s1s3WdyHTt2tNkJYenSpQ6PkXR1ZHKBgYF67LHHslRbSEiI3nnnHVWsWFEvvviiDh48qPLly2vMmDE6d+6cVqxYYXcakrzszJkziouLS7XcGeHUu+66y+ZyR9qTIu/JC6+BAICs+fPPPzV8+HBJlunZVq5cab1YZOrUqXbnad+0aZNmzZqVY3XmZmFhYSk6fLz55puZDnRu2LDB5j727dunnTt35tQhAQBc4M0330y17MaNG3bfbxMTEzVv3jxJkre3t1t+9hoyZIjNLhxRUVEaNWqUCyoCAPdEsMLNFC1aVLVr15ZhGBo9enSaV8i5WlxcnGbNmiWTyWS3ZTaQ27nLyXyOA1nVpUsXVa9e3ea6CRMm2Fz+5ZdfKiEhQZLlA6CteSadyVaw4urVq9m6z+QKFCig1157LdXy33//XYcOHXJojB9++CHVsnfffdehqT1suX79uoYPH67KlStr7NixCg0N1b333qvFixfr9OnTeu+99+x2XnAHSXOj3y6tq1wcVbhwYZvLb++aAveQF14DAQCZd/nyZT311FPWOdDnzZunGjVqWNeXLFlSU6dOtfv8119/XWfOnMn2OnO7efPm5UjH0unTp2f7PgAArtO6dWs98MADqZZ//vnnNi+e+Pnnn3Xx4kVJUufOnVWqVKlsrzGnmUwmzZ071xr6TG7ixImppsYFAGQOwQo31KtXL0mWlP7zzz+fa8MVAwcOVHBwsCSpQ4cOLq4GyDx3OZnPcSArPDw8rFfw3W7nzp3asmVLimU3b97UnDlzJEl+fn4aNmxYttdo60vc/fv3Z/t+k3vrrbdUp07K30PDMDRgwIB0369PnDihZcuWpVjWoEEDm2ENR8ydO1d33nmnPv/8c0VHR6t06dL6+uuvtX//fvXs2VNeXl6ZGjcvsfWFi+Sc8IO9Dh+2upYg78sLr4EAgMyJj4/XU089pUuXLkmSRowYoSeffDLVdt27d7e5XLKENvv165etdeZ2ZrPZGngwmUz6/vvvtXXr1izdhg4danNfy5cv1/Xr13Py8AAAOcxW14qLFy9q4cKFqZbPnj3ben/gwIHZWpcrHT9+3GYHTrPZrD59+igmJsYFVQGAeyFY4YYGDhyo4sWLS5KWLFmipk2b6vjx4y6u6j+nT59W27Zt9fXXX0uyXNGZFAYB8ip3OZnPcSArnn/+ebstkMePH5/i8axZsxQeHi5J6tOnj93nOZOtE90HDhywe3I9O/j5+WnNmjWqXLlyiuU7d+7UCy+8YL16/XZXr15Vt27dUnxArlGjhtauXSsfH58M1RAWFqaOHTuqf//+1i4e7du3V1BQkHr16pWr5w11tqJFi9pcfuvWLd24cSNLY9vrWJH0NxrcT25/DQQAZM6IESO0bds2SVLz5s01duxYu9tOnz7d7nt9fp8SZN26dTpx4oQkqV27durcubMeeuihLN3efvtteXp6ptpXTEyMteU7AMA9Pfnkkza7Bk6YMCHFhSsXLlzQunXrJElVq1ZV69atc6zGnBQSEqLevXvLMAyb743Hjh3Tu+++64LKAMC9EKxwQ4ULF9aECRNkGIYky8maWrVqqU+fPjl+ZW5yf/31l/r166eaNWtq06ZNMgxDJpNJH3/8sQIDA11WF+As7nIyn+NAZvn7+9u96nrdunUKCgqSZLnqb/LkyZIsV3lntuNCRhUrVizVstjY2Byfg7lSpUravn17qrkvFy5cqAYNGujbb79VSEiI4uLidPr0aU2ZMkV169bV33//bd22U6dO2r59u0qUKJGhfV++fFlNmjTRzz//bF325JNPas2aNRkeyx2kFXL4559/sjS2rS8yJOmee+7J0rjZpWXLljIMI0u3BQsWuPowXCq3vwYCADJuyZIl1tfssmXL6ttvv02zq1epUqU0ZcoUu+tff/11nT171ul15gXJp0pxVqem8uXLq1OnTjbXzZo1y/q9GADA/Xh4eGjEiBGplgcHB2vFihXWx3PnzlViYqIkqX///m55MYlhGHr++ed1+fJlFStWTFu2bLHZLfPLL7/Un3/+6YIKAcB9EKxwU3369FGfPn2s4YX4+Hh9/fXXql+/vqpWraqXXnpJy5Yt07Fjx7Ltg+a5c+f0/fffa/DgwSpXrpyaNWumBQsWKDY21lpXp06dNHjw4GzZP+AK7nIyn+NAZg0dOtTmfI6GYWjChAmSpKVLl+rChQuSLHNb3nnnnTlSm72T6HPnznXK+NHR0Tp16pRD25YpU0abNm3S6NGjUyz/+++/1aNHD5UuXVq+vr6qUqWKXnrpJV2+fFmS9MADD2jZsmX64YcfbAZF0quvXbt2Onr0qHVZ5cqVtWjRonwx7YctJUuWtNu14vDhw1kaOzIyMtUyX19f1axZM0vjImtGjRqlH374IdvGz82vgQCAjDl06JAGDBggSfLy8tK3336rMmXKpPu8Z555Rp07d7a5Ljw8XP3793dqna4ydOhQbd+e+rOWLcHBwfrll18kSdWqVdOjjz7qtDqGDBlic/mJEyes+wQA5C1ms1nPPPNMul24n3/+eZvvzePGjbOOk9TByMvLS3379nV+sbnA559/rvXr10uS5syZo6ZNm1o/fyZnNpvVt29fm1PlAgAckz+/Rc8nvvrqK507d06bNm2SyWSyBihOnz6tadOmadq0aZIsX/JXqFAh1a1MmTIKCAhQQECA/P39rf+VLG0Vk24RERG6ePGiLly4oPPnz+vUqVPat29fivksk4c3kmpp1qyZFi9enIM/ESBnrP/jkCTLyfvkkh4nrc/tOA5kRvHixdW3b1/re0xyS5cu1UcffaTPPvvMuuz111/Psdr8/Px09913pwgWSJY5mL/44gu7J9gdtXTpUo0cOdLhqxA3b96sL774QmXLltXy5ct17tw5/fHHHzp16pSuXr0qs9msYsWKqXTp0mrUqJFatmype++9N9P1jR49WgcPHkyx7K233rJ5Eji/MJlMatq0qX766adU63777TcNGjQo02PbCla0aNFCfn5+mR4TWXP16lWNHTtWmzZtyrZ95ObXQACA48LCwvTEE09YTzx8+umnatasmcPPnzlzpv744w/rtGvJbdy4UbNnz7aGNvKiQ4cOacaMGXr77bcd2n7atGnW74UGDx4sDw/nXef1yCOP6M4771RwcHCqddOnT1f79u2dti8AQM74+eef9fPPP2v+/Plpbufn56eXX3451fvR/v379csvv8gwDJ05c0aSpfOnIwHJvGbPnj3W4x84cKCeeOIJSdKgQYO0cuVKbdy4McX2wcHBeuedd/Tll1/mdKkA4BYIVrgxLy8v/fzzz3rhhRe0ZMmSFG2ukgcdYmJidPz4cetcl85wexeM2/fdtWtXffPNN5xcgNtyl5P5HAcy47XXXtPMmTOtrRaTxMfH66mnntKhQ5afd9OmTdWkSRNbQ2SbZs2apQpWREdH6/3330/RnjijDMPQlClTVLt2bYe2X716tXr06CEvLy9t3bpVtWrVkmS5wjE7REVFaebMmamWd+jQIVv2l5e0bNnSZrBiw4YNSkhIyHQ3j6QuI8nZa1WNnDF58mQVL15cDz74YLbuJze/BgIA0mcYhnr37m39jqRbt24ZnrapTJkymjRpkp577jmb60eMGKF27drpjjvuyHK9rjB+/Hg1atRIFSpUSHfbyMhI63RhAQEBeuGFF5xai8lk0osvvqjhw4enWvfzzz/r7NmzefbnDAD51fjx4/XYY485dO5g8ODBGjt2rG7dupVi+dixY1N0+hw4cKDT63S18PBwPfPMM4qPj1fNmjX1xRdfpFg/Z84c1alTJ9XPZvLkyXryySczFBoFAFgwFYib8/Hx0TfffKMJEybIz8/PGngwmUypblmdVzv57faxJcuXE35+fvryyy+1YsUKQhVwe+4yDQXHgYyqWrWqunbtanPdzp07rfdtzYWZ3Zo3b25z+fTp07Vly5ZMj/vtt9/q77//1iOPPJLutmvWrFH37t0VFxenDz/80BqqyE6///67wsLCUi0vW7ZslsbN6nRit594doVnn31W3t7eqZaHhYVp8+bNmR739gCPv7+/evbsmenxkDXXr1/XpEmT9MQTT2T7nLq5+TUQAJC+MWPGWEOXd911l7WFeEb17t1bHTt2tLnu1q1beuGFF7JtatbsdOTIES1ZskRPPvmkQ9svWrRIN2/elCT17Nkzy13ibOnTp4/N75jMZrNmzZrl9P0BALLPhg0btG3bNoffZwoXLmyz2+SWLVus00BWrlxZbdu2dWqducHgwYN1/Phx+fr6asmSJak6kt5xxx2aOHFiqucZhqG+ffva7LQJAEgbwYp8Yvjw4Tp48KDatm1rDT/czlbYIrO35JL21bVrV/3zzz966aWXcuSYgdzAXU7mcxzIqPTa29eoUcMlV+936dJFRYoUSbXcMAz17Nkz3fk7bbl27ZpGjBghb29v9e7dO81tjx49qh49eighIUGS5aR+Tjh9+rTN5bbCFhmRkWCEp6dnqmXh4eFZ2n9yZrM5U88rU6aM3ZPgkyZNytSYN27cSNWxokePHtlyIiEvcsVJpLffflvh4eHq1q1bjuwvt74GAgDS9v3332vkyJGSpAIFCmjVqlUqWLBgpsebNWuWzb89JWnTpk1ZbsPtivfUV155RWaz2aETXoZhpDjGoUOHZktNxYoVs/v33FdffaWYmJhs2S8AuLucfp+Jj4/Xa6+9poCAgAx12HzllVfk4+OTannS9wT9+/fP9oB9TluwYIF1mvVx48bpvvvus7ld//791a5du1TLT5w4oTfffDM7SwQAt0SwIh+pVq2a1q9fr7/++kuPPfZYig4T9qTXmSK953l5eem5557ToUOHtGLFClWqVCk7Dg3I1dzlZD7Hkb/ExcXZvO+oBg0aqEWLFnbXDx8+PFNzK9urxdEaAwMDbV7JIEkXL15Uy5Yt9e+//zpcT1RUlLp3764LFy7o6aefVqlSpdLc/o033rDO1S1J586dc3hfWZF0leDtduzYkaVxM/K74e/vn2qZvZ/15cuXNWDAAJvTadgLc0RERDhcy+3eeustm8GPtWvX6siRIxke7+eff07x2MfHR++//75Dz82LV65mVGxsrN118fHxTt/fr7/+qjlz5qhkyZIOtzp119dAAIB9u3fvVq9evazvxTNnztQ999yTpTHLlStn80rRJG+//bb+/vvvTI+f0++p8+bN04YNG1SvXj1VqVIl3e1//PFH69979erVs3vSxxnsBZyvXbumr7/+Otv2CwDuLKffZ8aMGaPDhw+rffv2qbovpKVcuXJ2p9/y8vJyyjRUuemz+rFjxzRs2DBJUvv27fXyyy+nuf2cOXNUqFChVMunT5+epU6dAJAfEazIhxo3bqw1a9bo1KlT+uSTT3TvvffaDUw42pki+XM9PDzUvHlzTZgwQSdPntSCBQuy/GUEkNe5y8l8jiP/SH7yP/n9jLB3xXapUqXsfuDNSF2OLLflpZdesvmBUpIuXLigevXqaerUqel+aA4ODtbDDz+szZs3KyAgQB9//HGa21+4cEFr1qxJsaxbt26aNm2a/vnnH4WFhSkuLi7TnRfSknxe0eQmT56c7nNjY2P1+uuv2wyB2GobuXv3bpvjlClTJtWyLVu26Nq1aymWmc1mvfTSS9q+fbvN59j7YsdeVw5H3HfffTY7ahmGoXfffTfD4y1fvjzF42HDhjl08kGSoqOjUy1L6nCSVbnl5HtanUqy2kXldseOHVPPnj1lGIa6dOliM0Bjizu/BgIAUjt8+LAee+wx6982PXr0UK9evZwy9gsvvKCHHnrI5rrY2Fh179491dznjsrJ99Q///zTehLH0W4VSd0/JGV716hHHnlEgYGBNteNGzcuW04AAoC7y8n3mZUrV1q/V3F0GpDkXn/9dZvh9Y4dO2Z5GlTJ9md1yTmf1zPyWT06OlpPPfWUIiMjVbx4cc2fPz/dbhwVKlTQp59+mmq5YRjq06dPpv8OAYD8iGBFPlapUiW99dZb2r9/vy5duqSVK1dq+PDhat26tapWrSpvb2+HOlZUqVJFjz/+uN566y0tXbpUISEh+v333zV8+HCVL1/exUcJ5B7ucjKf48gfLl26ZPN+RnTo0MFmsG7YsGE250DOaF3JXbx40eExypUrl+Zcy5GRkfq///s/1ahRQx999JH27NmjkJAQxcfH6/Lly/r555/Vt29f1alTRzt37pQkffjhh7rjjjvS3G9wcHCqZRcuXNCwYcNUq1YtFS1aVL6+vvL09LQGGD08POTl5SVfX18FBgaqWLFiqlChgmrWrKkWLVpoyJAhWrZsmd0P+Enq1q1rc/nGjRs1fvx4u89bt26d7r//fn322Wc2Ax9nzpyx3jcMQ0OGDLG2orzd3XffnWpZVFSU2rVrp127dikyMlK7du1Sx44d9d1339nt8HDhwgWbyzdt2mT3OBwxevRoVa1aNdXy1atX69tvv3V4nG3btlnnZZekOnXqpBu6SZKYmKgbN26kWu6sKVNsje0K58+ft7suIx1j0rNr1y61atVKV69elZSxEzru/BoIAEjp9vcLyXIi3pnSmhI1ODhYvXr1ylS4NqfeU9euXatHH33U+jenI++pixcvTtGNw164xFm8vb1Vv359m+tOnjyZ5t//AADbcup9Zt68eXrmmWdkNpvl6+urxx9/PMNj1KhRQ126dEm1fODAgU6oUAoJCbG53Bmf1zPyWX3IkCE6dOiQJMvFMrYuSLFl0KBBatCgQarlZ8+ezbapugDAHRGsgCSpdOnSeuKJJzRhwgRt2LBBwcHBio6O1qVLl3Ts2DHt379ff/75p7Zt26a///5bJ06cUEhIiGJiYnT8+HH98MMP+uSTT/T0008zfziQBnc5mc9xuLf4+HgdPnzY+vjgwYOZGsdkMmnEiBEplgUEBGjIkCFOqSu5f/75J0NXofXo0UP9+/dPc5vjx4/r/fffV4MGDVS6dGn5+PiobNmy6tixoxYsWGDtnNC5c2e7V6YnV6tWrQy1spQsYYXExETFxcUpMjJSoaGhunDhgo4ePao//vhDM2bM0DPPPKNy5cpp0qRJdrtsNGjQwO40JW+++aaef/55HTx4ULGxsTp79qzmz5+vpk2bqkOHDjpy5Iieeuopm2HJ3377TX///bfOnj2rHj16aMaMGWrVqpXN/dibH3Xfvn1q1KiRAgMD1ahRI61bt06tW7dW9+7dU21769YtLVy40OY4n376aZpf+qQnMDBQq1evVoECBVKt69+/vzVEk5bLly+rb9++1selSpXSihUrHD6JfvToUZtTnSQPsDjC3tQvx44dy9A42WX79tSvu0mWLFmSblAoPVevXtVbb72lhx56yBpEKFq0qN3fzdvlh9dAAIDl76wpU6aoRYsWKUIVhQoVcvo0pul1rlqzZo1eeeWVDI+b1nvq/Pnzs9wJ7cyZMxowYIA6duxoPXFUu3Zt1ahRI83nXb16Va+99lqKZV5eXlmqxRH2utJJ0rvvvpulvxUBID9K631m7ty5WR7/n3/+UdeuXdWvXz/r55k2bdqoYMGCmRrvrbfeSvG4UqVKateuXZbrlGT3s5gzPq+fPn3aoa4VkydP1oIFCyRZupL37NnT4f2aTCaNGjXK5rpvvvnGKf+eAJAfZP+nGuRZJpNJpUuXVunSpV1dCuAyBQtk7orStPy5N1i+Pl56sm3KlHDPjk3k6+Ol37Yfcfo+s0N+OI5Cgf4uqsq1Zs6cmWKKh1WrVmns2LEqUqRIhsd69tln9e6771pPbr7wwgsqXrx4puqaNWuWzaknJEuXicWLF6tPnz4OjzdjxgzduHFDq1atylQ9ktSwYUMtWrQo3baLklSyZEnNmTNHzz//vNNPgIaFhemVV17R9u3btXjx4lTTHXh5eWn48OF68803bT5/0aJFWrRoUarlfn5++vzzzzV48GA1aNAgVbeI6Oho3X///dbHo0aNUufOnW3uo0OHDmrVqlW683fWqFFDixcvtv5MDcNQSEiI/vzzT40ePdpux4orV66oXr16evvtt/XYY4+pfPnyNkMSaalbt67Wrl2rxx57TBEREdblEREReuSRRzR9+nS7Uzjs379fPXv21PHjxyVZpj7ZuHFjuiceklu2bJnN5Rs3btTp06dVuXLldMcwDEMzZsy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wwx5vvvmmUU516tRRWVlZVsV4//33NZ+fueemJz8VViil1OjRozXz8fPzUzVr1lTnz5/PMcbs2bM1YyxbtsyiHP766y+TfcGBAweseh4HDx5ULi4uJjkEBgaqmJgYi2K89957Rn2Dg4N1Ly2lJS0tTbNAy9LCiuTkZFW9enWr9z33bNu2TXPdtWvXtvo9DwAAAEAblwIBAAAAANgtJSVFli5dKv37989x2Tp16kj9+vVN2sPCwixa16hRo0QpJSIivXr10ry0gDmNGzfWbLfmkgu2qlChgklbcnKyvPjii/Lzzz9LoUKFcozRvXt3zXZLt5+Li2P+FWAwGGzu6+Pjo3kJjri4OFm/fr1F7yMRkYEDB2q2R0VFyZQpU2Tq1Km6l8G4X7du3TSn8rd0m2q5ffu2NG7cWA4cOJDjpS4GDRoky5cv1831jz/+kPPnz+v2z8zMlNGjR2ffHzBggLi6ulqVr62fi6JFi2q2+/v7y6RJk3T7VapUSWrWrOmw9+ODjh8/bnKJnIEDB1r9vh0zZozme3X+/PkSGhpqV47OprU/ute+c+dOqVSpUo4x7NkfKaVkyJAh2ftzEZF69epJgwYNcux7v/r162vuC65fv559eSVz9u/fLzNmzDBqe++993QvLaXF3d1dXn/9dYuXf9DixYslPDxcREQCAgKkR48eVvVv1KiR5ns7LCxMtm/fbnNeAAAAAP5FYQUAAAAAwG7vvfeevPDCCxYv/+ijj5q0Xbx4Mcd+hw8fll27dmXf79Spk8XrvCcoKEizPSoqSs6ePWt1PGtoFYGUKlVKFixYYPEJZr0T4JcuXbIrt7ymdUK+R48e0qpVK4tjlClTRipXrmzS3qxZM3n//fctjuPl5SW1atUyab98+bLFMR5UtWpV2bJli5QoUcKi5bt16yZff/215mNZWVkyd+5c3b5r1qyRqKio7PuO/Fzs2bNHkpKSdPvpnXweM2aMFClSxOo8HGXMmDGSmZlp1Pbss89aHcff31/eeOMNzce++uorm3LLL/SK0pYuXSrFihWzKEZgYKBUrFjRpN2S/dG6detk//79Rm22vEYiIkOHDtVsX7JkSY5jy4cffihZWVnZ9729vaVPnz5W59C6dWur+9wzderU7L+ffvppcXd3t6q/t7e37udt8+bNNucFAAAA4F8UVgAAAAAA7GbtrBFlypQxaYuLizP65bKWWbNmGd2vXbu2VesVuTtbgp4dO3ZYHc8aWiehPT09rZpdoGzZspozG9y8edOu3PKa1rawZRaM4OBgkzZbZkEoW7asSVtsbKzVce4JCgoy+17T8v7770vDhg01H9u0aZNuv9mzZ2f/7eHhIdWqVbNqvSL6n4u0tDTZu3evbj+9E8Dt27e3OgdHuXDhgqxZs8aorXTp0lK+fHmb4r366qua7Zs2bZITJ07YFDM/0CuKsfZ9q1VYYcn+6P5ignueeOIJq9Z9T5UqVaRJkyYm7RkZGTJ9+nTdfocOHTIq1hMRadq0qU1FQY899pjVfUREdu3aJcePH8++b8u4JqL/uuX2uAYAAAA8LCisAAAAAADkOX9/f5M2pZQkJCSY7bdlyxaj++XLlxeDwWDVrW3btrrx7ZmhwBLW/gpZi6urq+b2S0lJsTt2XvLy8nJIHEfNilC4cGGTttTUVIfEtpSrq6sMGzZM87Hjx49LYmKiSXtaWprRieG0tDRxc3Oz+nNh7jIG5j4X1l5yJC8sXLjQZLaKGjVq2ByvWrVq0qhRI83HVqxYYXNcZ3PE/khENGe3yGl/dPHiRc2ZFOx5nfQKYJYvX67bR6u4Q6+4KSd6l8XJyYPj2meffWb159dgMOjOEpLb4xoAAADwsKCwAgAAAACQ5/R+WZucnKzbJzo6WiIjI3Mpo7tu3LiRq/EddRJa65fm6enpDomdV2yZVUKL3q/ureXp6WnS5oxt2qVLFwkMDDRpz8zMlMOHD5u0Hzp0KNcLQMx9LmyZZSS3/frrryZtWjOSWEPvEjXbtm2zK64zOXN/pPUaGQwG3UvSWELvNbpw4YJERESYtKelpcmqVatM2uvWrWtzDrb466+/cjV+bo9rAAAAwMPCdO5QAAAAAABymdalLETuTtuuZ8+ePSZtoaGhurFsUapUKYfFyk1aJ0RzuoxKfuOok7q5eWLfGdvU3d1dnnrqKVm2bJnJY9HR0SZtD34uihUrZnIZDHtVqFBB97H8Vlhx+/ZtzQIUX19fu+LqnWzft2+fZGRkOHQ/VNDYsj/aunWrSZu3t7dd27F69epSqFAhSUpKMnls165dUqlSJaO2AwcOaC6rVdiUW5RSJpfaCQkJka5duzpsHfntMwoAAAAUVA/vUR8AAAAAwGlsKay4ePGiSVvt2rWlePHiDssrt3GCC5aoX7++ZmFFXFycSduDn4uUlBRp1qxZruWW3x05ckTzpL7WjCTW0CusSE1NlZiYGCldurRd8Z3Bmfujv//+26TN3tfIxcVFHn30UZNCBRHRvEyG3mwjWpdayi3x8fESHx9v1BYYGPhQf4YBAACA/IpLgQAAAAAA8g1zv3LWOql8+/bt3EwHcIpHHnlEs13rM/BgW2JiotkCpf+6M2fOaLabu8yQJapWrapbiMClFqyTnp6ueVkne18jkbuzVmjReo2OHz+uuay9s5tYg3ENAAAAKDgorAAAAAAAFAhaJ6BiYmKckAmQuwICAjTbtQqPtD4XD/OJ/lu3bmm237lzx664Li4uuifcH5xxAObpFQ6kpKRIZmamXbH1ZpvQeo30xg+ty4PkFsY1AAAAoOCgsAIAAAAAUCBonezSmk4eKOj0Tg77+PiYtPG5MJaWlqbZ7ojiBz8/P832vJzh4L9A7zUSsf91suY10itAystCGT6/AAAAQMFBYQUAAAAAoEDQOmG2f/9+J2QC5C5XV1fN9lKlSpm08bkwpjfbR0REhN2x9QpeihUrZnfsh4neayRi/+tkzWukd+mR2NhYu3Kwhtbn9+jRo2aLTwAAAAA4B4UVAAAAAIACoWjRoiZtO3fudEImQO7S+8V8cHCwSZvW52LHjh0Oz6mg0CtyOH36tN2XmdAqePH395cyZcrYFfdh4+3tLd7e3pqPnTx50q7YekVJtWrVMmnTK/A4ceKEXTlYQ+vzm5qaKvv27cuzHAAAAABYhsIKAAAAAECBULx4cZO2c+fOybZt2xwS/8yZM5KRkeGQWIA9tAorPD09pXbt2ibtWp+L0NBQOX/+vENy+eeff0Qp5ZBYeaFatWqa7ampqXL27Fm7YicmJpq0Pf7443bFfFjpvU72FjVovUYiInXr1jVp0yvCOXTokF05WEMvhx9++MEh8ZOTkx0yWwsAAAAACisAAAAAAAWE3gnMuXPnOiT+gAED5PPPP3dIrPzMxYV/BeR3ly5dMmlr2LCheHp6mrRrfS6UUvLdd9/ZnUdWVpZ07NhR5s+fb3esvPLoo4/qXg5i69atdsXWOmnfrl07u2I+rJo3b67ZnhuvUbVq1aRixYom7WXLltWMsXv3bsnKyrIrD0t5eXlJjRo1TNpXrFghcXFxdsdfunSptGzZ0u44AAAAACisAAAAAAAUEPXq1ZNChQqZtP/88892T5t+5MgR2blzp+aMAP817u7uJm2Omqkjr05G/tcdOXLEpO3555/XXFbvBPWMGTMkMjLSrjx+++03iYyMLFCfCxcXF91tsm7dOpvjZmRkyM2bN03au3TpYnPMh1mrVq002w8cOCDXr1+3Oe7Vq1dN2rp27aq5bNOmTTXbo6OjZePGjTbn8KCc9q9a79fk5GQZOXKkXetVSsn06dML1OcXAAAAyM8orAAAAAAAFAju7u7SpEkTk/bMzEx57bXXJDU11ebYw4YNE1dX14fil71axSl60+dbKz093SFxHnaHDx82uu/p6SkvvfSS5rJBQUESHBxs0n7nzh15/fXXbc4hMzNTRowYIQEBAVKvXj2b4zhDv379NNu3bt2qeZkVS5w7d87k/d24cWOHnLQuSJdacZTOnTtrXsZGKSW//vqrzXFPnTpl0jZgwADNZVu0aKEbZ86cOTbn8KC0tDSzj+vlMWvWLNm+fbvN612+fLkcOXJE2rRpY3MMAAAAAP+isAIAAAAAUGD0799fs/3kyZPSr18/yczMtDrm0qVLZePGjdK5c2cpWbKkvSnme1onM2/fvi1JSUlWxblz545JW0pKis154a5r167Jrl27jNr69+9v9r2p97nYsmWLfPTRRzblMX78eDl58qT06dNH3NzcrO7vzNlLnn32WSlTpoxJe3Jysnz77bc2xTxx4oRJ26BBg6yKoVd4ZE9RWEHl6empW/Awffp0m2JmZWWZFFa0bt1a81IbIiKVKlWSunXraj7222+/yR9//GFTHg9KTk42+3i3bt2kSJEiJu1KKXn55Zfl7NmzVq/zxo0bMnToUHF3d5dXX33V6v4AAAAATFFYAQAAAAAFmN4JuZx+IWsPZ/66umfPnlK2bFnNx5YtWyZ9+vSxataE/fv3y5tvvikiIu+++65DcjQnP/wyvVy5ciZtSik5evSoRf2VUjJ48GA5dOiQyWNaxRbm4sDU0qVLjQqEihQpIqNHjzbb5+2339aciUREZMKECfLxxx9btb3Xrl0rn332mbi6usrbb79tdlm9YiZr3guO5ubmJsOGDdN8bNq0aTZd+ubBy4jUqFFDevfubVWMhIQEq9pzm7M/g++99574+vqatB8/ftymS3Hs3r1bbt26ZdT2xRdfmO3zwQcf6D721ltvSUxMjNV5POjBnB5UuHDh7HHoQVeuXJFWrVrJ6dOnLV5fUlKS9OzZUy5fviwvvviiBAYGWpMuAAAAAB0UVgAAAABAAaZ3CQdHXdpBi1bRhi0nKi2NfT93d3fdE6YiIkuWLJGGDRvK33//neO6Fi9eLG3btpWEhATp3LmztG3b1up8raX1y+W82nb31KxZU7N9xYoVOfZNSUmRl156SWbOnCl+fn4mj1+8eNGiHES087XlUiJaJ/Zt2aZaMyzk9aVN4uPjZfz48UZtkyZNynEmlaJFi5qdPWH8+PHy1FNPyfnz583GycrKkilTpsjzzz8vGRkZ8vbbb0v16tXN9tEr7oqMjDTbL7cNGjRIGjRoYNJ+8eJFmTVrllWxUlJS5LfffjNqGz9+vLi6uloV5+rVq5rtUVFRVsVxFL2ZFByxT7JkfxQUFCT/+9//NB8bMWKE1bOePLgP6969uzRt2tRsnxdffFEqVqyo+djFixelbdu2Ehsba9H6H/zs3nPs2LEc+7733nua+1QRkcuXL0u9evVkxowZORbDnDlzRp566ikJDQ2VQoUKydixY3NOHAAAAIBFKKwAAAAAgAIsOjpas/3KlSu5tk6tX986qpAjLi4ux2UGDRok7du313386NGj0rBhQ+nQoYMsWbJETp06JYmJiZKUlCTh4eHy3XffSePGjeWVV16RhIQEKVy4sEyZMsUh+efk+vXrJm2O+rW6pSf/SpQooTk1/qxZs0wuQXG/I0eOSOPGjWX58uUyefJkzSn0w8PDTabi16P1Wt++fduivvfTOrFvy/tRK44l70c9t2/ftvrE8IgRI4w+0wMGDNC9zMeD/ve//8ljjz2m+/i2bdukZs2a0qNHD1mzZo2cPXtWkpOTJSEhQY4fPy5Tp06V2rVry//93/9Jenq6lClTRj7//PMc13v58mXN9i1btliUd25xcXGR7777Tjw9PU0e+/TTT626vMLEiRPlxo0b2fdff/116dKli9U5/fXXX5rte/futTqWI2jtj0Qcs0+ydH/07rvvSpMmTUzaDx06JBMmTLB4fefOnTO6zEvp0qVl7ty5OfZzd3eXBQsWiIuL9r9Ijx07Jk2aNJH9+/frxrhz544MGjRIPv74Y83HN27cKCdPnsy+f/PmTZMCiaCgILP5JiYmyuDBg6VatWryv//9Tw4ePCjXr1+X9PR0uXr1qqxbt0769+8vderUkX379omIyKhRo6R8+fK6MQEAAABYSQEAAAAACqQLFy4oEdG8ValSJU/X2bNnT6viTJ06VTPOjz/+aFH/a9euqaCgIN3nb81t1apVtmwKmzRo0EAzh7i4OItjZGRkqGLFipnEaNWqlcUxPv/8c808PDw8VEhIiDp27JhKSUlR0dHRau3atapbt27K1dVViYgaN26cUkqpli1basaoVauWWrp0qVq2bJl6++231fr1603Wn5qaqjw9PU361qxZ0+LncM8TTzxhEqd48eJWx2natKlJnEKFCqmMjIwc+/bt21dzW3Tr1k0lJiZatP45c+YY9X3uuedUenq6Vc/hn3/+UYULF7b7M+Hh4aH27NmT4/pSU1NVvXr1NGMUKlRIHTlyxKr8c8MPP/ygmV/NmjVVTExMjv137typvL29s/s9+eSTFr+m94uIiFD+/v662/v48eO2PD27DB06VDOf1atXWxWnW7duJjE8PT1VamqqRf0vXbqkSpUqZRLDzc1NrVmzJsf+d+7cMdoP+Pj4qF27dln1HEaMGGH2M+Hm5qZ69eqlNm7cqC5fvqySk5NVeHi4Gj9+vCpfvrwSEdWwYUPd/j4+Pqp9+/aqYcOGytXVVe3du1czj9dff90h49qzzz6rsrKyrNoGAAAAAMyjsAIAAAAACqj33nvP7ImV0NBQh6/zs88+01xXQECAunr1qkUxUlNT1aOPPqoZp3379hbncuLECVWyZEm7Tj599dVXtm4Kq4WHhysXFxfNPL7++muL4yxZskQzhru7uwoPD7coRlxcnCpevLjV22vSpEnZMfQKK+6/1a9fX23cuNFk/d9//71un3379lm8LXbv3q0bZ926dRbHOXDggDIYDJpxli5dmmP/cePG6eZRq1YttW3bNt2+WVlZavz48Ubvjf79+6u0tDSL87/f9u3b7SqucHNzUz/99JPZfG/cuKHWr1+vWrVqZTZW4cKF1ZgxY9Tx48dVfHy8Tc/HEb755hvN/CpXrmz2/bZs2TLl5+eXvfyTTz6pbt++bdW6r169qubOnasCAwPNbqtixYqpOXPmqOvXr9v7dC2SlpamKleurJlLhw4dLI4TERGhPDw8NOPMmzfP4jgnT57ULa6YMGGCboHTuXPnVJMmTbKX9/X1VVu3brV4vfdkZmaq3r172/y5eeyxx9SNGzdyXK5p06bq119/1S16SE9PV927d7c5DxFRjRo1svp9CgAAACBnFFYAAAAAQAETERGh3njjjRxPrvj5+anZs2fbdUIzKytL3bx5U+3Zs0e9++67uoUBIqKCg4PVypUrVXR0tMrMzDSJlZycrA4cOKA6d+5sNu+BAweqkydPWvRr56ioKN1ZIMzdXF1d1Zw5c2zeLtaIj49X69atU9WqVTN7Mnvs2LHqzJkzms87KytLXb58WX377bfKx8dHN07FihXVr7/+qmJiYnL8tfL69euVm5ubRdvL09PTZDYRvcIKb29v1a9fP5MT1pmZmerixYtqxowZRjMAPHgrVaqUWr58ubpy5YrmydS0tDR1/vx59e2336oiRYroxilUqJAaP368Cg8PVykpKSZxUlJS1Llz59TEiRPNFpl4eXmpCRMmqPPnz5udvWLXrl2qfv36unHatm2r5s+fr8LDw1VSUpKKjo5Wv/zyi2rWrJlRzrNnzzb7ulni6NGjqkqVKlZ/Lry9vdVvv/1mNnbFihVtPuG7YMECu5+brVauXGlUJHHv5uLiojp16qTmzJmj/vzzT/X777+ryZMnG52sNxgM6oMPPrCq2OW1114zu780d3NxcVGurq7K1dVVRUZGOnQ7pKamqgMHDqgOHTqYzaF3797q8OHD6s6dO5px4uLi1O+//252v1aoUCE1ffp0deHCBYtmfrlw4YJq3LixZqyqVauq0aNHq7Vr16rNmzerRYsWqVdeecVoX1KvXj115swZm7dNZmam7iwe5m5t27ZVt27dUkopzccNBoPq2rWr2r17t8V5fPTRR7rFXuZu7dq1033NAAAAANjHoNQDF/UDAAAAAOQ7tWvXFqWUxMbGytWrV63q6+rqKmXLlpXChQtLw4YNZf78+Rb3HTNmjIwePdrKbO/atm2btGzZUqKioiQ4OFgyMzOtjjFmzBj57LPPzC6TkZEhU6ZMkS+//FJiY2NzjNmsWTOZNm2a1K1b1+p8rOXm5mbT827durVs3bpVREQGDBgg8+bNs2n9kZGRUqFCBd3H161bJ3369DG73WrXri0LFiyQ+vXrG7W3atVKtm/fnn2/atWq8tZbb0n//v0lICDAaNnt27dLq1atbHoO978HPv/8cxk1apRNcfr375+9He2Jc+99rSUrK0t++eUXmT59uuzYscPimC4uLtKjRw/5+uuvzb5e1khMTJSxY8fKtGnTJDExMcflu3btKpMmTZLg4GCzy50+fVrS0tJsyqls2bJSpEgRm/o6QnR0tIwYMUIWL15s0XMwGAzSoUMHGTt2rDz++ONWrevy5csSFxdnY6b/ql69uri7u9sdR0SkTZs22fsVaxgMBsnKyhIRkUWLFkm/fv3Eln8nLly4UPr06WN2mczMTJk7d66MGzdOLl++bFHcSpUqyaeffip9+/YVV1dXq/N60MaNG2XIkCFy6tQps8v5+PjIyJEjJSQkRFxcXETk7ra6x8PDQ1555RUJCQmRGjVqWJ3Hzp07JSQkRPbt25fjssWLF5exY8fK66+/np0LAAAAAMeisAIAAAAACoCwsDCHxPHx8ZFKlSpZvPz169fl+vXrNq2rUqVK4uPjI+np6RIeHm5TjMDAQAkMDLRo2eTkZFm1apWsW7dODh8+LJcuXZK0tDQpUaKElClTRlq2bCnPPfecNG3a1KZcbGHr63b/62TPCVpLTspev35dZsyYIWvXrpUzZ85IWlqalC5dWho3biy9evWSbt26aZ6oa9WqlezevVu6dOkib7/9trRt29bopOL9EhMTJSIiwqbncP97wJ73Y0BAgJQpU8buOPfe1zk5fvy4rF69WkJDQ2XPnj2Smppq9LiLi4s89thj0qlTJ+nbt69UqVLFpnxycuvWLVm+fLls3LhRjh49KleuXJGsrCwpWbKklC1bVtq2bSvPP/+8PPbYY7my/vzo6tWrsmrVKtm6dauEhYXJlStXJDk5WXx8fKR48eLy2GOPSdOmTaVHjx5Svnx5Z6frMBERERYV2WipXbu2iNx9P126dMmmGNYU1mRkZMj69evljz/+kEOHDsm5c+ckISFBXF1dJSAgQKpWrSoNGzaULl26SIsWLXT3PbbKysqSdevWyapVq2Tv3r1y4cIFSU9Pl6JFi0qdOnWkY8eO0q9fPylatKhRP4PBIH5+fvLmm2/KkCFDJCgoyO5c9uzZIytXrpRdu3bJ2bNnJT4+XgoXLiylS5eWOnXqSLdu3aRz587i6+tr97oAAAAA6KOwAgAAAAAAIBdlZmZKTEyMXL9+XZKTk8Xf318qVqwoXl5ezk4NAAAAAABYgMIKAAAAAAAAAAAAAAAAHVx0DwAAAAAAAAAAAAAAQAeFFQAAAAAAAAAAAAAAADoorAAAAAAAAAAAAAAAANBBYQUAAAAAAAAAAAAAAIAOCisAAAAAAAAAAAAAAAB0UFgBAAAAAAAAAAAAAACgg8IKAAAAAAAAAAAAAAAAHRRWAAAAAAAAAAAAAAAA6KCwAgAAAAAAAAAAAAAAQAeFFQAAAAAAAAAAAAAAADoorAAAAAAAAAAAAAAAANBBYQUAAAAAAAAAAAAAAIAOCisAAAAAAAAAAAAAAAB0UFgBAAAAAAAAAAAAAACgg8IKAAAAAAAAAAAAAAAAHRRWAAAAAAAAAAAAAAAA6KCwAgAAAAAAAAAAAAAAQAeFFQAAAAAAAAAAAAAAADoorAAAAAAAAAAAAAAAANBBYQUAAAAAAAAAAAAAAIAOCisAAAAAAAAAAAAAAAB0UFgBAAAAAAAAAAAAAACgg8IKAAAAAAAAAAAAAAAAHRRWAAAAAAAAAAAAAAAA6KCwAgAAAAAAAAAAAAAAQAeFFQAAAAAAAAAAAAAAADoorAAAAAAAAAAAAAAAANBBYQUAAAAAAAAAAAAAAIAOCisAAAAAAAAAAAAAAAB0UFgBAAAAAAAAAAAAAACgg8IKAAAAAAAAAAAAAAAAHRRWAAAAAAAAAAAAAAAA6KCwAgAAAAAAAAAAAAAAQAeFFQAAAAAAAAAAAAAAADoorAAAAAAAAAAAAAAAANBBYQUAAAAAAAAAAAAAAIAOCisAAAAAAAAAAAAAAAB0UFgBAAAAAAAAAAAAAACgg8IKAAAAAAAAAAAAAAAAHRRWAAAAAAAAAAAAAAAA6KCwAgAAAAAAAAAAAAAAQMf/A6rwpZGElBmNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2100x700 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}