paper_note/lynx/scalability_compile_time.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\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",
    "    \"#455D7A\",\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 = [\n",
    "    \"FSDP\",\n",
    "    \"XLA\",\n",
    "    \"DLRover-Lynx\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = [2, 4, 6, 8, 10, 12, 14, 16]\n",
    "data_a = [53, 53, 53, 53, 53, 53, 53, 53]\n",
    "data_b = [53, 53, 53, 53, 53, 53, 53, 53]\n",
    "data_c = [65, 65, 65, 65, 65, 65, 65, 65]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_labels = [\"FSDP\", \"XLA\", \"DLRover-Lynx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(13.333333333333346, 0.5, 'Time (s)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.plot(x_data, data_a, marker=\"o\", color=colors[0], label=legend_labels[0])\n",
    "ax.plot(x_data, data_b, marker=\"x\", color=colors[1], label=legend_labels[1])\n",
    "ax.plot(x_data, data_c, marker=\"o\", color=colors[2], label=legend_labels[2])\n",
    "\n",
    "ax.set_ylim(0, 100)\n",
    "\n",
    "ax.grid(True)\n",
    "\n",
    "ax.set_ylabel(\"Time (s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd1de31f410>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.legend(\n",
    "    ncol=3,\n",
    "    loc=\"upper center\",\n",
    "    frameon=True,\n",
    "    shadow=False,\n",
    "    fontsize=g_label_fontsize,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ee8wq9s0331g9oQcURM4YxwMGDLDar91eRqvOSMyZAX/lqq0uIyIizPftkJAQu7btAvBfuTGWFy1apO3bt1vFJ02apIcfftjp10fW0VgBIN+oXr26hg8f7pBc7u7uhl38ZcqUcUh+APb75JNPzMsEv/POO2rSpImLKwKQmbCwMO3cudMqHhISotKlS7ugIgBZ1ahRI8Otej788ENFRUVlOU9KSopWr15tEbO1nDgA+3l4eDg859WrV3X48GGreIUKFey+nlHT1o0bN3Tw4EG78gH5iTPG8fDhw1W9enWH5KpQoYJhnDkzwJIzxnJmzp8/b54ff+yxxzRjxoxcrwHIb5w9luPi4gxXYa9QoYIGDx7s1Gsj+2isAAAbSpQoYRWrV6+eCyoB8IeoqCi9/fbbkn5/yvXdd991cUUAMpOWlqYJEyZYxT09PQ23BgGQt/j4+Bhuk5eamqpOnTopOjo6S3lmzZql69evm48DAwPVoUMHh9UJwHn27NljGLf142pWVKtWzTC+a9cuu3MCyB1G82UlS5ZU2bJlXVANgD9kZGSof//+unnzpry8vLRixQpWkgEeANOnT1dCQoJVfMSIEfLy8nJBRbgfGisAwAajfaMDAgJcUAkA6fcxGRQUpJSUFBUrVkwREREu6f4HkD2LFy/WhQsXrOJdu3bN8R7PAHLH22+/bXjPvXjxolq3bq3Tp0/f9/0HDx7U+++/bz5+5plntHDhQofXCcA5nNFYYWuvd1asAPI+5suAvGnq1KnmlSI//PBDPfXUU64tCECm4uLi9Omnn1rFvb29FRwc7IKKkBkaKwDAhkuXLlkc+/j4qE2bNi6qBsDYsWN15MgRSdKnn35q8yk3AHnL9OnTDeMDBw7M3UIA2K1WrVoaO3as4bmTJ0+qcePG2rFjh+H56Ohovfjii0pJSZEkNW3aVFu3bpWPj4/T6gXgWEbbgEg5a6ywtVf0b7/9ZndOALnjr/NlktSlSxcXVALgDwcPHtT48eMlSW3atNGoUaNcXBGArJg7d66SkpKs4p07d1apUqVcUBEyQ2MFABi4fv26rl27ZhELDAxkAhhwkW3btmnWrFmSpD59+qh///4urghAVuzcudPwSfbixYvr+eefNx/Hx8dr+fLlGjx4sJ588kmVKVNG3t7eKleunBo1aqR3331XUVFRuVk6gL947733bDYZx8XFqV27dlq8eLFF/Pr162rXrp0uXrwo6fd7+JYtW1S8eHGn1wvAcc6ePWsYz0ljRenSpQ3jp06dsjsngNzx66+/Whz7+fmpV69eLqoGQHJysoKCgpSWlqaSJUsqPDxcbm5uri4LQCZMJpOWLFlieK5Pnz7m/87IyNAPP/ygd955Ry1atFCVKlXk4+Mjf39/1a1bV/369dP69euVlpaWW6UXaJ6uLgAA8qI/lk37g5ubm0aOHOmiaoCCLSEhQQMGDJDJZFKlSpU0b948V5cEIItsfUHs3LmzvLy89Ntvv2nWrFlavny5kpOTrV535coVXblyRfv379ekSZMUFBSkGTNm2Fw+HIDzeHp66uuvv1ZAQIAOHTpkdT4tLU2DBw/W8ePHNWXKFMXExKhdu3Y6duyYvL29NX36dA0fPjz3CweQIxkZGebmqL8qU6aM3Xm9vb0N40lJSbp9+7Z8fX3tzg3AeTIyMvTjjz9axIYOHcqYBVxo1KhROn78uCRpwYIFbLkJPCB27typM2fOWMW9vb3VqVMnJSYmauHChfr0008NX5ecnKybN2/qt99+U0REhGrVqqXPPvuMVdedjBUrAMDAN998Y3EcGBioevXquaYYoIB79dVXFR0dLXd3d0VEROihhx5ydUkAsiAjI0Pr1q0zPNe0aVONGDFCTzzxhBYuXKjk5GT5+vqqUqVKKlasmM2cy5cvV926dbV//35nlQ3gPvz8/LRlyxY9/fTTNl8zbdo0de3aVc2bN1dUVJTq16+vn3/+maYK4AEVExOju3fvGp7LyYqOthorJFmtHgkg79i+fbtu3LhhPvbz89OYMWNcVxBQwG3cuFFz586VJA0ZMkTdu3d3cUUAsioyMtIw3qJFC61fv161a9fWG2+8oTNnzsjDw0PlypW7b2PziRMn1LZtW7333nvOKhmisQIArCQlJWnt2rXmY09PT73//vsurAgouMLDw7V69WpJ0pgxY9S8eXMXVwQgq3755RfFx8cbnnvzzTc1Z84clShRQuPHj9fhw4eVlJSk8+fP69atW/r11181YsQIFSpUyOq9cXFxatOmjXbs2OHsvwIAA6VKldL333+vxo0b23zNt99+q9OnTysgIEB79+5V3bp1c7FCAI5069Ytm+ec1Vhxv2sCcK2IiAiL4zfeeEMlS5Z0UTVAwRYbG6tBgwZJkmrWrKnZs2e7tiAA2bJt2zbD+O7duxUYGKirV6+qV69e2rhxoxITExUdHa0rV64oPj5eX3zxhSpXrmz4/g8++ECvv/66M0sv0GisAIC/CA8Pt5jICQ0N1aOPPurCioCC6dy5c+YPgQ0aNNDEiRNdXBGA7Pj+++/ve378+PE6e9eODXgAABd5SURBVPasJkyYoCeeeMLiXJ06dfTJJ59o586dhhO1SUlJ6tWrl65cueLQmgFkjb+/v77//vtMn4jbsWOHXnvtNfZ6BR5gd+7csXmuSJEiduf18PCweS41NdXuvACcJy4uTitXrjQfP/roo3rrrbdcWBFQsA0ZMkTXrl1ToUKFtHz5crbkAR4gMTExioqKMjx3584dderUSUePHtXq1avVsWNHi8/dxYsX1yuvvKIjR46oY8eOhjk+/fRTLVy40Cm1F3Q0VgDAn9y9e1fTpk0zH9eqVUvjx493YUVAwZSRkaF+/frp1q1b8vX11fLly+Xp6enqsgBkw6FDhwzjDRs21LFjxzRhwoRMJ34aN26sb7/9Vl5eXlbnYmNj1a9fP0eUCsAORYoU0VdffaXBgwff93Xz589Xq1atFBMTk0uVAXCk+zVW5GTFivT0dJvnbG09AsC1ZsyYoZSUFPPxggULctRgBcB+8+fPN2+9OXHiRNWvX9/FFQHIjsOHDxvGCxUqpLVr1+rbb79VnTp17pvDz89PkZGRatiwoeH5ESNG6MSJEzmuFZZorACAP/niiy907tw5Sb8/QbNw4cL7LlEKwDk++ugj/fjjj5KkTz75RDVq1HBxRQCy6/jx44bxrl27qlKlSlnO06hRI40bN87w3LZt22wunQjA+Xbs2KENGzbI09PzvluD7N69W02aNNHJkydzsToAjmAymWyey8kPqhkZGTbP8R0cyHuuXr2qOXPmmI+HDh2qVq1aubAioOA6deqURo0aJUkKCAjQ6NGjXVwRgOyyNWdWuXJlde3aNct5vL29tWzZMsPPz8nJyZo0aZLdNcIYjRUA8B9xcXEWq1NMnDhRzz33nAsrAgqm//u//zNv+9GjR49Mn4QFkDedOnXKMF6lSpVs5xo1apRKlChheG7q1KnZzgcg56ZPn6527dopISFBa9as0e7du+87qXvmzBk1adJEe/fuzcUqAeRU0aJFnZL3z0+9/1VOVsIA4BxvvfWWkpKSJEn16tXT7NmzXVsQUEDdu3dPQUFBun37tvz9/bVs2TK5u/MzH/CgceScWc2aNTVw4EDDc6tWrdL58+eznRO28S8uAPzHP/7xD8XHx0uSOnTooLFjx7q4IqDguXPnjoKCgpSWlqby5ctrwYIFri4JgB3S0tKUmJhoeK58+fLZzufr66vXXnvN8NzWrVvN928Azpeenq6QkBCFhoYqIyNDixcvVteuXeXu7q4pU6Zo6dKlhtv3SFJ8fLyef/55/fTTT7lcNQB7FStWzOa5+zVHZCY1NdXmObYWAPKWbdu2KSIiQtLv/yZ89dVXjFPARSZMmKADBw5IkubNm6eKFSu6uCIA9oiLizOM2zNnJklvvvmmYZPVvXv3FBkZaVdOGKOxAgAkrVu3TsuWLZMkVa1aVREREXJzc3NxVUDB88Ybb+jkyZNyc3PT0qVLbT6hDiBvs9VUIUllypSxK6et1WtMJpN27txpV04A2ZOenq4+ffpo3rx5kqSRI0cqKCjI4jX9+/fX5s2b5e/vb5jj1q1bev7553X06FFnlwvAAZzVWPHHk+9GbP37ASD3JSYmavDgwTKZTHJzc9OSJUtUs2ZNV5cFFEi7d+/WlClTJP3+mbt3794urgiAvWzNm9k7Z1a9enUFBAQYntu+fbtdOWGMxgoABd6lS5c0aNAgSVKJEiX03XffqWTJki6uCih4NmzYoPnz50v6fdn/Nm3auLgiAPa6c+eOzXP23mMrV66sJ554wvDcvn377MoJIOtMJpMGDBigr776SpJUrVo188TuXwUEBGjXrl0qV66c4fnExER16dJFCQkJTqsXgGM89NBDNh86yEljxa1btwzjvr6+fB8H8pBXXnnFvIT41KlT9dJLL7m4IqBgSkxMVL9+/ZSenq5q1arp008/dXVJAHLA1rxZTj4Hd+nSxTDOnJlj0VgBoEBLS0tTnz59FBcXJy8vL0VGRqpWrVquLgsokP78NPr06dPl5uZm15/g4GDD/LZeD8DxChUqZPOcn5+f3Xmff/55w/jVq1ftzgkgayZPnqzly5ebjz/88EN5e3vbfP3jjz+uXbt22dwj9vz58woNDXV0mQAczMvLS1WrVjU8d79VJzJz48YNw3ilSpXszgnAsebOnav//d//lSTzNmAAXGPSpEk6e/asJOnMmTPy8/Oze97MSHBwsOFrw8LCcvFvCRQctubNnDFnFhsbq4yMDLvzwhKNFQAKtNdff127d++Wu7u7wsPD1aJFC1eXBBRYMTExri4BgIP4+vraPOfl5WV33nr16hnGbe1NCcAx9u3bp/fee898XL58efXs2TPT91WrVk0//PCDzR9KFy1apJ9//tlhdQJwjrp16xrGL1++bHfO2NhYw7itZiwAuWvXrl0aOXKkpN+fgJ0zZ46LKwIKtmvXrrm6BAAOZGveLCdzZjVr1jR8f3p6us2mZmQfjRUACqzPPvvMvO3AnDlz2JcOAAAHKVq0qM0viampqXbntfXDzt27d+3OCSBzr7/+usUTLj169JCnp2eW3lulShVt3rxZpUqVMjw/ffp0h9QIwHls3X+jo6PtzmnrB6Jnn33W7pwAHOPs2bPq2bOn0tLS1KJFC61evVoeHh6uLgsAgHzjkUceMYznZM7M09PT5mrszJs5Do0VAAqk9evXmzvvJ06cqGHDhrm4IgAA8pdq1aoZxuPj4+3OaeuLJ3uxA86zfft2HThwwCLWpk2bbOWoVauW1qxZY9iMERkZqeTk5BzVCMC5nnzyScP4pUuX7M558eJFw3jjxo3tzgkg5+Lj49WxY0fFxMToqaee0rp161S4cGFXlwUAQL7y6KOPGsZzMmcmMW+WG2isAFDg7N27V3369FF6erpGjhxpsawxAABwjMcff9ww/se+sPYoVqyYYdzWk/AAcm758uVWMVvj+35atGihCRMmWMVTU1O1Z88ee0oDkEvat29v+LR6Tu7p586ds4q5ubnRWAG4UHJysrp27aoTJ06oZs2a2rRpkx566CFXlwUAQL7jjDkzyXjezM/PT4UKFcpRXvwXjRUACpSoqCh16tRJd+7c0YABAzRr1ixXlwTgP0wmk0P+LFmyJFv5AThHs2bNDONRUVF257S1vYg9P/ICyJpdu3ZZxcqUKWNXrtDQUFWuXNkqfvr0abvyAcgdJUqUMLyvHzx40O6cp06dsoo1adKEp+kAF7l375569uyp3bt3q2LFitqyZYsefvhhV5cF4D/CwsIcNm9mZMmSJYavHThwYO7+RYEConHjxnJ3t/6JPidzZpLxvBlzZo5FYwWAAuPf//63nn/+eSUkJKh79+5atGiR3NzcXF0WAAD5Urt27Qzj+/btsztnUlKSYbx58+Z25wRwf0ZL/du7JLiXl5fh5GxOlzsF4HxdunSxisXFxen8+fPZzhUdHW047nv37m1XbQByJiMjQ/369dPGjRtVunRpbdmyRZUqVXJ1WQAA5Ft+fn5q1KiRVfzQoUO6e/eu3XmN5s2YM3MsGisAFAjnz59X69atdfnyZbVv314rV640XMoUAAA4Rs2aNfXUU09Zxbdu3Wp3zhs3bljFypUrp9q1a9udE8D9GT3VZjQWs6pFixZWMX9/f7vzAcgdffr0MVxC+Oeff852LqOVLjw8PNSzZ0+7agNgP5PJpMGDB2vVqlXy9/fXpk2bVKtWLVeXBQBAvmfUVJySkqIff/zR7pxG39Vbt25tdz5Yo7ECQL538eJFtW7dWhcuXFDz5s0VGRkpb2/vHOWcOnVqjn4YAgCgIBgyZIhV7OLFizpy5Ihd+Y4fP56lawBwHKNtP65evWp3vvLly1vFypYta3c+ALmjQoUKCgwMtIpv2rQp27mMJov/53/+R+XKlbOrNgD2MZlMevXVVxUWFqaiRYtq48aNevrpp3OU84cfftDkyZMdVCEAAPlX3759VaRIEav4hg0b7M7513mzKlWqqG3btnbngzUaKwDka+fPn1dAQIDOnDmjhg0b6ttvv5WPj0+Ocs6fP19jxoxh71cAADIRHBysUqVKWcXDw8PtynfgwAGLY09PT/3tb3+zKxeArGnYsKFV7KeffrI7X0pKisWxu7u7mjZtanc+ALnnrbfestpOc+3atcrIyMhWno0bN1rFxowZk6PaAGSPyWTS0KFD9cUXX6hIkSJav369mjRpkqOcly5dUu/evXXt2jUHVQkAQP5VunRpBQcHW8VXrFihe/fuZTvfpUuXrB6CCAkJkbs7rQCOxP9NAPnWuXPn1LJlS509e1b169fXpk2bVKxYMbvzZWRkaObMmRo2bJjq1q2b4y5+AADyOx8fH40fP94qHhYWpsTExGznW7NmjcVxSEgIT7cCTtatWzer2Pr16+3Od+LECYvjZs2a6eGHH7Y7HwBLRpOw2W18sKVu3brq3r27RezatWvas2dPlnOcOnVKUVFRFrFevXrpiSeecEiNQH7gzHH8R67Bgwdr4cKFKlKkiNatW6eWLVvmKOcvv/yiZs2aKTY2Vv369XNMocADztljGUDucOZYHjdunNVvVteuXdOXX36Z7Vx/nTMrX768QkJCclQfrNFYASBfOnnypJo3b65z586pcePG2rJlS5b3bjaZTLp3756SkpIUHR2t/fv3a8aMGXryySc1atQoZWRk6OWXX3buXwAAgHwiJCREzz77rEUsLi5OM2fOzFaePXv26NChQ+bjcuXKadKkSY4oEcB99OzZU9WqVbOIbdiwQadOnbIr3+rVqy2OQ0ND7a4NgLW0tDSr2N27dx2Wf8aMGVZLFi9atCjL758zZ47FsZ+fn2bPnu2I0oB8w5nj+N69e+rXr5+WLFkiX19frV+/PltLhGdkZCg5OVnXr1/XsWPHtGrVKvXu3Vv169fXhQsXVLt2bdWvX98htQIPOmffkwHkDmeO5XLlyumDDz6wik+YMMHwurakp6dr/vz5FrHZs2fn6EFjGKOxAkC+c+TIETVv3lyXLl2SJO3du1fFixeXm5tblv64u7urUKFCKlasmCpUqKBGjRrpzTffND9V4+bmpqCgIFf+FQEAeGB4eHhoxYoVVl/mPvroI/36669ZynH37l2LLns3NzfNnTtXfn5+Dq0VgDVPT09NmzbNIpaRkaEhQ4YoPT09W7n27t2rr7/+2nzcvn17denSxSF1Avid0YpQt27dclj+KlWq6KOPPrKIrVixQmfOnMn0vadPn9bChQstYrNmzWL1KeAvnDWOU1NT1bNnT61YsUKSdPv2bbVt2zbL82Vubm7y8PCQj4+PSpcurTp16igwMFCrV682P7nLg0jAfzn7ngwgdzh7LI8YMUIdOnSwiJ08eVITJ07Mco5p06bp2LFj5uMePXqoZ8+eDqsR/0VjBYB8Ze/evWrZsqViYmKcdo2AgABVrFjRafkBAMhvatasqfDwcHl4eJhjqampeumllxQbG3vf92ZkZOiVV17RkSNHzLF33nlHXbt2dVq9ACz16NFDw4YNs4jt3LlTISEhWV4C9ezZs3rppZdkMpkkSVWrVlV4eLjDawUKusuXL1vFoqOjHXqNkSNHqkePHubju3fvaujQoff99yApKUl9+/ZVcnKyRZ5BgwY5tDYgP3DGOE5KSlKXLl20du3aHOW5Hx5EAizlxj0ZgPM5eyy7ubkpIiJCNWrUsIhPmTJFkZGRmb5/48aNFtvw1qlTR2FhYQ6rD5ZorACQb2zatElt2rRRQkKCU6/DXpEAAGTfiy++qIULF8rNzc0cO3HihNq0aWNzS4Hr16+rZ8+eFj++jhw5Uu+//77T6wVg6ZNPPrH6HLxgwQK98MILOnv2rM33mUwmffXVV2rQoIF5Qqps2bL617/+pUceecSpNQMFzb/+9S/dvHnTKh4VFWXRoOgIERERat68ufl427ZtGjhwoFJTU61ee+7cObVp00YHDhwwx1599VXNmDHDoTUB+YEzxnFcXJzatGmjLVu25LS8+3ruuedUpUoVp14DeFDk5j0ZgPPk1lguWbKktm3bpsqVK5tj6enpCgwM1NKlSw3fYzKZNHfuXPXo0cO8NUmtWrW0bds2tgBxIjfTH4+LAMADLDIyUr17987WvlP2KFy4sK5du8bS40AeFhYWpuDgYKs4H3mAvOGbb75R//79LZZS9PLyUmBgoNq3b6+yZcsqLi5Ou3bt0rJly8wNk15eXpoxY4aGDx/uqtKBAs9kMuntt9/WtGnTLJ5M9/b2VseOHdWhQwdVrFhR3t7eiouL08GDB7Vu3TqLJUlbtWqllStX0lQBOEhSUpKio6O1efNmjRs3znCpYkl6+OGHNXnyZLVo0ULly5eXj4+PQ64dFBSkdevWmWM1atTQ3/72Nz355JO6c+eONm3apMWLFyslJUXS79+pp0yZopEjR+b4+kB+4cxxHBsbq4CAAIt7sbPMnz9fQ4cOdfp1gLzKlffk7Pjzww5/WLJkiQYOHJirdQB5lSvH8uXLl9WjRw/t27fPIt6oUSMFBgbqscceU3p6uo4dO6Zly5bp0KFD5td07txZERER8vf3z3EdsI3GCgD5wsCBA2127jlSr169tHr1aqdfB4D9aKwA8r4TJ04oNDRU69evz/S1bm5u6tKli6ZNm6aaNWvmQnUAMnP48GGNHj1amzZtyvJ7HnvsMY0ePVovv/yyxbZAAOwTHh6uAQMG5CjH9u3bFRAQkKMcJpNJn3/+ucaNG2f4NN+fde7cWbNnz1b16tVzdE0gv8iNcbx9+3a1atUqR9fICm9vb125ckXFixd3+rWAvCav3JOzisYKwFheGcupqamaMmWKZs6cqVu3bmX6+urVq2vixInq27dvjq6LrKGxAgAAAIBLHDp0SGvWrNHWrVt14cIFXb9+XR4eHipdurTq1KmjgIAA9ezZkx9ggDzq9OnT+u6777R582adO3dOsbGxio+Pl6+vr0qWLKmKFSuqRYsWat26tVq0aGE4iQvAPjdu3NClS5dylKNq1ary9fV1SD23bt1SRESENm7cqF9++UXXr1+Xj4+PqlatqrZt26p///56/PHHHXItIL/Ia+MYgH0Yy0D+kNfGcnx8vL788kt99913Onr0qGJiYpSamio/Pz89+uijatCggbp27aq2bdvK3d3dIddE5misAAAAAAAAAAAAAAAAsIEWFgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG2isAAAAAAAAAAAAAAAAsIHGCgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG2isAAAAAAAAAAAAAAAAsIHGCgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG2isAAAAAAAAAAAAAAAAsIHGCgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG2isAAAAAAAAAAAAAAAAsIHGCgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG2isAAAAAAAAAAAAAAAAsIHGCgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG2isAAAAAAAAAAAAAAAAsIHGCgAAAAAAAAAAAAAAABtorAAAAAAAAAAAAAAAALCBxgoAAAAAAAAAAAAAAAAbaKwAAAAAAAAAAAAAAACwgcYKAAAAAAAAAAAAAAAAG/4fozog6P1BbnwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2100x840 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.savefig(\"scalability_compile_time.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
}