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paper_note/lynx/end_to_end.ipynb
yezhengmao1 dd4a54f2dc add
2025-03-06 13:03:05 +08:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 583,
"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",
" \"#1E1E1E\",\n",
" \"#c8beb4\",\n",
" \"#F40009\",\n",
"]\n",
"\n",
"edgecolors = colors\n",
"\n",
"hatches = [\"\", \"\", \"\"]"
]
},
{
"cell_type": "code",
"execution_count": 584,
"metadata": {},
"outputs": [
{
"data": {
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xadOmeP/992PDhg3xj3/8Iy677LLYa6+9Ut67du3a6NevX8yaNWu3zA0AALVZvuwj6bRo0SKeffbZ6NWrV4WveeKJJ2Lx4sXRp0+fmDt3bnTp0mWPzQcAACTfznacBg0aZNw3F3aqigiKyGGffvpp2np1fhgjIurUqZO2vnXr1mr1TScfzgAAALWRe/l/mzRpUrkF/qqrroqvfOUrWZwIAADyX77tJMXFxfH9738/IiKOOOKIuP7663fr9QAAgMzlyz7y7LPP7vT56667LpYsWRJjxoyJbt26lXuuc+fOMXbs2Jg9e3Y0b9485b2bNm2K008/PVasWFGjMwMAQG2XL/tIRZo2bRrPPvtsfPOb39zp62bNmhWXXHJJbNu2bQ9NBgAA5IOKdqr69etHQUFBxn1zZadKR1BEDqvoB7K6aZA7duxIW//ss8+q1TedfDgDAADURu7lP/fZZ5/FLbfcUva4U6dOcd1112VxIgAAqB3yaScpKSmJ4cOHx4YNG6JRo0bx4IMPRt26dXfb9QAAgOrJl33k9ddfT1s/8sgjY8GCBTFmzJho1KjRTnv06tUrnnjiiahXr17Kc6tXr47hw4fXxKgAAMD/L1/2kZ1p0KBBPPzww3Heeeft9HV333139O3bN1atWrWHJgMAAJKuNuxU/0lQRA4rLS1NW69uGmRJSUnaelFRUbX6ppMPZwAAgNrIvfznfvvb30ZxcXFEfJ4Cee+99+bsrAAAkE/yaSf5f//v/8X//u//RkTE2LFj46CDDtpt1wIAAKovX/aRhQsXpq0PHjw42rdvX+k+Rx11VPzkJz9J+9yMGTNixowZGc0HAACkypd9ZFdmzZoVjz/+eNStWzd69epV4euef/75OProo+Odd97Zg9MBAABJVVt2qi8SFJHDGjduvFv6btmyJW29uoko6eTDGQAAoDZyLx+xdu3auO6668oeX3/99XHcccdlcSIAAKg98mUnefnll+P666+PiIjTTjttl9+OBQAAZF++7COLFi1KW+/YsWOVe1155ZXRrFmztM/94he/qHI/AAAgvXzZR3bm1ltvjf79+8f69evjkUceieeffz6uvvrqCl//3nvvxdFHHx1z587dg1MCAABJVBt2qv8kKCKH7b333mnrFf1AVdbWrVvT1qubiJJOPpwBAABqI/fyET/4wQ9i3bp1ERHx9a9/PX784x9neSIAAKg98mEn+fTTT+O73/1ubNu2Lfbdd9+45557avwaAABAzcuHfWTbtm2xcePGtM/tu+++Ve7XqFGjuOSSS9I+N3369LLPUwAAgOrJh32kIjt27IiLL744rrrqqigpKYnx48fH4MGDo7CwMH7+85/H7373u6hXr17a965bty5OPvnkePHFF/fYvAAAQPLk805VEUEROWx3/UBu2rQpbb1p06bV6ptOPpwBAABqo9p+Lz9t2rS4//77IyJi//33jwceeCAKCgqyPBUAANQe+bCTXHHFFfHOO+9EQUFB/O53v6vw23cBAIDckg/7SEUhERERbdq0yajneeedl7ZeWloas2fPzqgnAABQXj7sI+ns2LEjzjjjjLjrrrsiIuLyyy+P7373u+Vec9ZZZ8XTTz9d4UwbNmyIk08+Of7+97/v7nEBAICEytedamcEReSwffbZJ229uj+QGzZsSFvv0KFDtfqmkw9nAACA2qg238svW7Yszj333IiIaNasWTz55JPRvHnzLE8FAAC1S9J3kscffzzuvvvuiIi48soro1+/fjXaHwAA2H2Svo9ERHz66acVPpfpZx4dOnSIbt26pX3ub3/7W0Y9AQCA8vJhH/lPpaWlMWLEiHj44YcjIuKAAw6In//852lf26dPn5gzZ060a9cu7fMbN26MQYMGxfr163fbvAAAQHLl4061K4IiclinTp3S1itKHqmsjz/+OG29ffv21eqbTj6cAQAAaqPaei+/bdu2OOOMM2Lt2rVRr169mDJlSoX/WwAAALtP0neSL37T7q233hoFBQUZ/XPOOeek7V/R6wEAgOpL+j4SEbHXXntV+FyTJk0y7nvyySenrX/00UcZ9wQAAP4tH/aR/3TTTTfFgw8+WPb4Zz/7WRQVFVX4+kMOOSTmzJkTHTt2TPv8+++/H1dddVVNjwkAAOSBinaqHTt2VCssIpf/DkZQRA7r0qVL2vrq1atj+/btGfddvXp12npFi3R15MMZAACgNqqt9/Lf//734/nnn4/CwsKYNGlS9O7dO9sjAQBArZT0nWTVqlU12g8AANhzkr6PREQ0atSowufq1auXcd+uXbumra9duzbjngAAwL/lwz7yRX/7299i9OjRZY/33XffGDp06C7fd8ABB8TMmTMr/KOr++67L1555ZUamxMAAMgP++23X4WB2cuXL8+4by7/HYygiBzWrFmzaNOmTUq9pKQkVqxYkXHflStXpq336NEj454VyYczAABAbVQb7+V/85vfxN133x0REePGjYvvfOc7WZ4IAABqr9q4kwAAALkhH/aRxo0bVxgWsXXr1oz7VvRHa5999lnGPQEAgH/Lh33ki77//e9HSUlJ2ePTTjst6tatW6n3duzYMZ5++ulo0aJF2udvvfXWGpkRAADIL507d05b//DDDzPumcu/cyYoIscdeuihaevLli3LqN+WLVtizZo1KfWWLVvGV77ylYx67ko+nAEAAGqj2nQv/9hjj8Xll18eERHXX399fO9738vqPAAAQO3aSQAAgNySD/vIAQcckLa+bt26jHu2bt06bb158+YZ9wQAAMrLh30kIuK5556Ll156qVytX79+VerRqVOneOSRR9KGS0yZMiU2b95crRkBAID8U9M7VUTEBx98kFIrLCyMI488MuOeNUVQRI475ZRT0taXLFmSUb/i4uK09V69emXUrzLy4QwAAFAb1ZZ7+blz58YZZ5wRO3bsiMsvvzxGjx6d1XkAAIDP1ZadBAAAyD35sI8ccsghaeuZniEiYu+9905br+gbfgEAgKrLh30kIuLBBx9MqVW0p+xM7969Y8yYMSn1rVu3xgsvvJDJaAAAQB6r6Z1q8+bNsWrVqpR6ly5dKvzcZE8SFJHjBg8enLb+6quvZtRv0aJFaeuDBg3KqF9l5MMZAACgNqoN9/JvvfVWnHrqqfHpp5/GiBEj4o477sjaLAAAQHlJ3klKS0tr5J8JEyZUqT8AAFAzkryP/Muxxx6btv7WW29l3LNRo0Zp65n8sRcAAJBePuwjERFz5sxJqbVp0yajXldddVV06NAhpb548eKM+gEAAPmrf//+Ub9+/ZR6pjvV4sWL0/5eVq78TbugiBy3//77R5cuXVLqr7zySkb93nzzzZRa3bp147TTTsuoX2XkwxkAAKA2yvd7+XfffTdOPvnkWL9+fXzzm9+M++67LwoKCrIyCwAAkCrfdxIAACB35cM+0r9//7T1v/3tbxn33LRpU9r68ccfn3FPAACgvHzYRyIili1bllJL98dalVGvXr04++yzU+rr1q3LqB8AAJC/GjZsGP369Uup1+ROFRHxne98J6N+NU1QRAIMHz48pfbaa69l9M1Q6RJP+vfvH82bN89otsrKhzMAAEBtlK/38u+//36ceOKJsXz58hgwYEA89NBDUadOnT0+BwAAsHP5upMAAAC5L+n7yFe/+tXo3r17Sn369OkZ9/z4449Tau3atYuvfe1rGfcEAABSJX0fiYi0s6bbKSqrd+/eKbWmTZtm3A8AAMhf6Xaq4uLijMLm0u1UBx98cHTr1i2j2WqaoIgEuOiii6JJkyblap988knMnTu3yr2ef/75lNrIkSMzHa3S8uEMAABQG+XjvfwHH3wQJ554YixdujSOP/74mDJlShQVFVWr5y9+8Ytq/WIlAACQXj7uJAAAQDLkwz5y/vnnp9Q++OCDCr/9alcWLlxYqWsAAADVkw/7SJs2bVJqH330Ucb99t1335Ra27ZtM+4HAADkr6FDh8YBBxyQUn/qqaeq3Ot///d/U2q59DtngiIS4Etf+lJcdNFFKfUpU6ZUqc8rr7wSK1euLFc7/PDDY8CAAdWarzLy4QwAAFAb5du9/Pvvvx99+vSJ9957L4488sh44oknomHDhtXqeffdd8eoUaN8CzEAAOwG+baTAAAAyZEP+8g555wTLVq0SKlPmjQpo34vvfRSucd169aNCy+8MKNeAABAxfJhHznyyCNTai+++GLG/bZs2VLucWFhYRxzzDEZ9wMAAPJXnTp14oc//GFKvao71erVq+Pll18uV2vbtm2MGDGiWvPVJEERCTFy5Mho1KhRuVpVfyAfffTRlNoNN9xQrbmqIh/OAAAAtVG+3MsXFxfHCSecEEuWLImePXvGU089FXvvvXfG/UpKSuL222+P733ve9GlS5c47LDDanBaAADgX/JlJwEAAJIn6ftIw4YN47rrrkupT5w4MTZu3Fjlfo888ki5xxdffHG0a9cu4/kAAICKJX0f+a//+q+U2mOPPZZxv7fffrvc42OPPTZatWqVcT8AAKB6tm/fnlIrKSnJwiTpnXPOObHvvvuWqz355JMpIXQ7M3Xq1JQzXXPNNVFUVFQjM9YEQREJ0bZt2xg9enS52uLFi2PWrFmVev+WLVvi3nvvLVf71re+FaeeemqlZ5g6dWp07949ioqKokOHDnHTTTdV6V/aXDgDAABQdblwL1/dfeSdd96J448/PoqLi6NXr17xzDPPRNOmTSv13tLS0ti+fXts2rQpPvzww5g3b17cdtttceihh8aVV14ZJSUlMWzYsErPAgAAVE0+7CQAAEAy5cM+cvHFF0ePHj3K1dauXRu33357pXtERLzwwgvx+uuvlz1u165d3HjjjVXqAQAAVF7S95GhQ4fGAQccUK72+OOPx6JFiyp9/S+aPHlyucdXXXVVRn0AAICasW3btpTaZ599VmP958yZE8cdd1zUr18/2rZtG1deeWWVQh7q16+f8lnIpk2b4o9//GOle/z6178u9/iII46Iiy66qNLv3xMKSktLS7M9BJWzbdu2OPbYY+Oll14qq5100knxzDPP7PK9P/vZz+Kaa64pe9y6det49dVXK53o/qc//Sm+/e1vp9QvvfTSGDduXKV6RGT3DAAAQOaSvI+8+eab0b9//1i1alWlrldVBQUF8f7778d+++23W/oDAADJ3kmqY+LEiXHOOeek1H28BwAAe04+7CPvvPNO9OzZMzZu3FhWKyoqildeeSW6dOmyy/d/9tlnccQRR8Sbb74ZEZ9/NjJ16tQYPHhwpWcAAACqLun7yKOPPhrf+ta3ytV69+4dzz77bNSpU6dSPSIi5s6dG8ccc0zZ5yMDBgyIp556qtLvBwAAal7nzp1jwYIF5WqnnHJK/OUvf6l27xdeeCFOOOGElDCKQYMGxbRp06rU67TTTospU6aUPf7a174Wf//736Nu3bo7fd+DDz5Y7gtFGzVqFC+++GJ07dq1Stff3QRFJExxcXH06NEj1q1bV1abOHFijBgxosL3zJ49OwYMGBBbt26NiM9TUGbOnBm9evWq9HXT/QsbEVFYWBjLly+P1q1b5/wZAACA6kniPjJ37tw49dRTY/369ZW+XlWdcMIJMXPmzN3WHwAA+FwSd5LqEhQBAAC5IR/2kalTp8bQoUNjx44dZbVOnTrFnDlzomXLlhW+r6SkJM4555yYNGlSWe3aa6+Nn/70p5W+NgAAkLmk7yOXXHJJ3HnnneVq//3f/x133XVXFBYW7vL9S5YsieOOOy6WL18eERH7779/vPjii7v18xkAAGDXmjZtGp988km5Wrdu3eKNN96odu9TTz01nnzyybTPzZs3L4444ohK9/r444+jR48e8d5775XVxowZE9ddd12F71mwYEEcd9xxZXtYQUFBPPLII/HNb36z0tfdU3a9VZFTOnbsGE888UQ0bty4rHbhhReWSzP5oj/+8Y8xcODAsgV/7733jilTplQ5YGHx4sVp6yUlJeX+5aiMbJ0BAAConqTtI0899VT069dvt4ZEREQMHz58t/YHAAA+l7SdBAAAyB/5sI8MGTIk7r333igoKCirvf3229GvX79YtGhR2vesWbMmhg4dWi4k4vLLLxcSAQAAe1DS95GxY8em/H7VPffcE9/4xjdiyZIlFb6vtLQ0Hn744TjiiCPKQiLatm0bf/3rX4VEAABAlv31r39NCYmIiHjrrbfizTffrHb/ivaRXT2XTtOmTeOpp56KNm3alNWuv/76+M1vfpP29TNnzoy+ffuWhUTstddeMX78+JwMiYiIKCj1lUOJNG/evBgyZEisWLGirDZkyJAYOnRotGvXLoqLi2PSpEnx3HPPlT3fqVOnmDx5cnTr1q3K1+vSpUvMnz8/pV6db8va02cAAABqRhL2kSlTpsR3vvOd2LZtW5WvVxX169ePlStXRpMmTXbrdQAAgH9Lwk5SUyZOnBjnnHNOSt3HewAAkB35sI9MnTo1zjrrrNi4cWNZrV69enHmmWfGgAEDom3btrF27dqYM2dO3H///WWB3PXq1YvbbrstLr300ipfEwAAqL4k7yOlpaXxox/9KG655ZYoKSkpqxcVFcUpp5wSX//612O//faLoqKiWLt2bbz66qsxbdq0WLBgQdlr+/btGw899JCQCAAAyJJNmzbFhx9+GE8//XT85Cc/Kfc5wxe1atUqbrrppujdu3fsu+++0bBhwypfa+DAgfGXv/wl7XPz5s2LI444oso9Fy1aFIMHD46FCxeW1fr27RvDhg2L/fffP1asWBGTJ0+OP//5z2XPt2vXLh544IHo27dvla+3pwiKSLCVK1fGD37wg3jooYd2+romTZrEFVdcEaNGjYqioqKMrvXwww/H6aefnlK/9NJLY9y4cRn1jNizZwAAAGpOru8jZ599dvzud7/L6HpVcfrpp8fkyZN3+3UAAIDycn0nqSmCIgAAIPfkwz7y9ttvx1VXXRWPPfbYLl9bUFAQgwYNiltuuSW++tWvZnxNAACg+pK+j7zxxhtx9dVXx1NPPVXp9xx88MFx9dVXx7Bhw6JOnToZXRcAAMjcpEmTYsSIEdXq8dxzz0WfPn0q/foXX3wx+vTpk/LFoYMGDYpp06ZlPMfGjRvjJz/5Sdx5552xY8eOCl9Xv379uOCCC+KGG27I+S8VFRSRBxYsWBATJkyImTNnxrvvvhubNm2KFi1aRPfu3WPgwIExfPjwGvlBnDp1aowZMybmz58fbdq0iYsuuihGjRoVhYWFiTkDAABQs/JhHwEAAJLLTgIAAGRLPuwjr7/+ejzyyCMxffr0WLp0aaxZsybq1KkTLVu2jM6dO0efPn1i6NChceCBB1b7WgAAQM1J+j6yePHiePLJJ+Ppp5+O4uLiWL16daxbty4aNWoUzZs3j/322y969+4dJ554YvTu3TsKCgqqfRYAACAzH3/8cSxbtqxaPfbff/9o1KhRld4zZ86cGDVqVLz88suxzz77xLBhw+LGG2+M+vXrV2uWiIilS5fG+PHjY/r06bFw4cLYsGFD7LPPPtG5c+c45ZRT4uyzz45WrVpV+zp7gqAIAAAAAAAAAAAAAAAAAAAAgITwNUcAAAAAAAAAAAAAAAAAAAAACSEoAgAAAAAAAAAAAAAAAAAAACAhBEUAAAAAAAAAAAAAAAAAAAAAJISgCAAAAAAAAAAAAAAAAAAAAICEEBQBAAAAAAAAAAAAAAAAAAAAkBCCIgAAAAAAAAAAAAAAAAAAAAASQlAEAAAAAAAAAAAAAAAAAAAAQEIIigAAAAAAAAAAAAAAAAAAAABICEERAAAAAAAAAAAAAAAAAAAAAAkhKAIAAAAAAAAAAAAAAAAAAAAgIQRFAAAAAAAAAAAAAAAAAAAAACSEoAgAAAAAAAAAAAAAAAAAAACAhBAUAQAAAAAAAAAAAAAAAAAAAJAQgiIAAAAAAAAAAAAAAAAAAAAAEkJQBAAAAAAAAAAAAAAAAAAAAEBCCIoAAAAAAAAAAAAAAAAAAAAASAhBEQAAAAAAAAAAAAAAAAAAAAAJISgCAAAAAAAAAAAAAAAAAAAAICEERQAAAAAAAAAAAAAAAAAAAAAkhKAIAAAAAAAAAAAAAAAAAAAAgIQQFAEAAAAAAAAAAAAAAAAAAACQEIIiAAAAAAAAAAAAAAAAAAAAABJCUAQAAAAAAAAAAAAAAAAAAABAQgiKAAAAAAAAAAAAAAAAAAAAAEgIQREAAAAAAAAAAAAAAAAAAAAACSEoAgAAAAAAAAAAAAAAAAAAACAhBEUAAAAAAAAAAAAAAAAAAAAAJISgCAAAAAAAAAAAAAAAAAAAAICEEBQBAAAAAAAAAAAAAAAAAAAAkBCCIgAAAAAAAAAAAAAAAAAAAAASQlAEAAAAAAAAAAAAAAAAAAAAQEIIigAAAAAAAAAAAAAAAAAAAABICEERAAAAAAAAAAAAAAAAAAAAAAkhKAIAAAAAAAAAAAAAAAAAAAAgIQRFAAAAAAAAAAAAAAAAAAAAACSEoAgAAAAAAAAAAAAAAAAAAACAhBAUAQAAAAAAAAAAAAAAAAAAAJAQgiIAAAAAAAAAAAAAAAAAAAAAEkJQBAAAAAAAAAAAAAAAAAAAAEBCCIoAAAAAAAAAAAAAAAAAAAAASAhBEQAAAAAAAAAAAAAAAAAAAAAJ8f8Bw+YcfeNv060AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 4200x1680 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(\n",
" figsize=(14, 14 / 2.5), ncols=1, nrows=2, constrained_layout=True, dpi=300\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 585,
"metadata": {},
"outputs": [],
"source": [
"# MFU\n",
"data_a = {\n",
" \"ModelA\\n#8GPUs\": [53, 53, 65], # 22.64\n",
" \"ModelA\\n#16GPUs\": [53, 53, 65], # 22.64\n",
" \"ModelB\\n#8GPUs\": [35, 35, 48], # 37\n",
" \"ModelB\\n#16GPUs\": [35, 35, 48], # 37\n",
" \"ModelC\\n#8GPUs\": [53, 53, 65], # 22.64\n",
" \"ModelC\\n#16GPUs\": [53, 53, 65], # 22.64\n",
" \"ModelD\\n#8GPUs\": [53, 53, 65], # 22.64\n",
" \"ModelD\\n#16GPUs\": [53, 53, 65], # 22.64\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 586,
"metadata": {},
"outputs": [],
"source": [
"# overlapping rate\n",
"data_b = {\n",
" \"ModelA\\n#8GPUs\": [54, 62, 12],\n",
" \"ModelA\\n#16GPUs\": [54, 62, 12],\n",
" \"ModelB\\n#8GPUs\": [52, 62, 12],\n",
" \"ModelB\\n#16GPUs\": [52, 62, 12],\n",
" \"ModelC\\n#8GPUs\": [52, 62, 12],\n",
" \"ModelC\\n#16GPUs\": [52, 62, 12],\n",
" \"ModelD\\n#8GPUs\": [53, 62, 12],\n",
" \"ModelD\\n#16GPUs\": [52, 62, 12],\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 587,
"metadata": {},
"outputs": [],
"source": [
"# communication baseline\n",
"data_c = {\n",
" \"ModelA\\n#8GPUs\": 70,\n",
" \"ModelA\\n#16GPUs\": 70,\n",
" \"ModelB\\n#8GPUs\": 70,\n",
" \"ModelB\\n#16GPUs\": 70,\n",
" \"ModelC\\n#8GPUs\": 70,\n",
" \"ModelC\\n#16GPUs\": 70,\n",
" \"ModelD\\n#8GPUs\": 70,\n",
" \"ModelD\\n#16GPUs\": 70,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 588,
"metadata": {},
"outputs": [],
"source": [
"legend_labels = [\"FSDP\", \"XLA + FSDP\", \"DLRover-Lynx + FSDP\"]"
]
},
{
"cell_type": "code",
"execution_count": 589,
"metadata": {},
"outputs": [],
"source": [
"bar_width = 0.2\n",
"group_spaing = 0.15\n",
"\n",
"group_positions = {}\n",
"current_pos = 0\n",
"\n",
"for x_label, y_data in data_a.items():\n",
" group_positions[x_label] = []\n",
" for i in range(len(y_data)):\n",
" group_positions[x_label].append(current_pos)\n",
" current_pos += bar_width\n",
" current_pos += group_spaing\n",
"\n",
"group_centers = {}\n",
"for x_label, positions in group_positions.items():\n",
" group_centers[x_label] = sum(positions) / len(positions)"
]
},
{
"cell_type": "code",
"execution_count": 590,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '(a)')"
]
},
"execution_count": 590,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_set = set()\n",
"for x_label, y_data in data_a.items():\n",
" positions = group_positions[x_label]\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",
" ax[0].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[0].set_xticks(list(group_centers.values()))\n",
"ax[0].set_xticklabels(list(data_a.keys()))\n",
"\n",
"ax[0].set_ylim(0, 100)\n",
"ax[0].set_yticks([0, 50, 100])\n",
"ax[0].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
"\n",
"ax[0].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
"ax[0].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
"\n",
"ax[0].set_ylabel(\"MFU (%)\", fontsize=g_label_fontsize)\n",
"ax[0].set_title(\"(a)\")"
]
},
{
"cell_type": "code",
"execution_count": 591,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, '(b)')"
]
},
"execution_count": 591,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for x_label, y_data in data_b.items():\n",
" positions = group_positions[x_label]\n",
" for i, (pos, value, diff_value, color, hatch) in enumerate(\n",
" zip(\n",
" positions,\n",
" y_data,\n",
" list(data_c.values()),\n",
" colors,\n",
" hatches,\n",
" )\n",
" ):\n",
" ax[1].bar(\n",
" pos,\n",
" value,\n",
" width=bar_width,\n",
" color=color,\n",
" hatch=hatch,\n",
" )\n",
" ax[1].bar(\n",
" pos,\n",
" diff_value - value,\n",
" bottom=value,\n",
" edgecolor=\"gray\",\n",
" color=\"white\",\n",
" linestyle=(0, (3, 1, 1, 1, 1, 1)),\n",
" width=bar_width,\n",
" )\n",
"\n",
"ax[1].set_xticks(list(group_centers.values()))\n",
"ax[1].set_xticklabels(list(data_a.keys()))\n",
"\n",
"ax[1].set_ylim(0, 100)\n",
"ax[1].set_yticks([0, 50, 100])\n",
"ax[1].set_yticklabels([\"0\", \"50\", \"100\"], rotation=90, ha=\"center\", va=\"center\")\n",
"\n",
"ax[1].tick_params(axis=\"x\", bottom=False, labelsize=11, pad=1)\n",
"ax[1].tick_params(axis=\"y\", left=True, labelsize=g_label_fontsize, pad=5)\n",
"\n",
"ax[1].set_ylabel(\"Overlapping\\nCommunication Ratio (%)\", fontsize=g_label_fontsize)\n",
"ax[1].set_title(\"(b)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 592,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fc2420fd370>"
]
},
"execution_count": 592,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig.legend(\n",
" ncol=4,\n",
" loc=\"upper center\",\n",
" frameon=False,\n",
" shadow=False,\n",
" bbox_to_anchor=(0.5, 0.95),\n",
" fontsize=g_label_fontsize,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 593,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 4200x1680 with 2 Axes>"
]
},
"execution_count": 593,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig.savefig(\"lynx_end_to_end.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
}
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