Files
TomoATT/test/old_tests/solver_performance/analysis_iter.ipynb
2025-12-17 10:53:43 +08:00

99 lines
13 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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pK1mVoqkHqosXkRAK8PVAdfEiEkIBvh6oLl5EQugia8xG3bYA1FJYREIpwMco07Ygs7I107YAKC/IZwf0TNmkAr5Iw1KKJkaRtC0Ikymb3PU64IfKJlUbL9JQFOBjFEnbgjBqJywiKMDHKl97gpLbFuRS2aSIoAAfq4q1LVDZpIigAB+rC2Z1ceuFx9PV0Y4BXR3t3Hrh8aNrWwAqmxQRQFU0sbtgVtfoA3oulU2KCArw9UvthEUanlI0jUQthUUaimbwCRLJqtZ81FJYpOFoBp8QkWzGPRLVxos0HAX4hKjYqtYM1caLNJyCAd7M7jezN8wsdOcIM/uIme0ys7XpL9XilaFiq1ozVBsv0nCKmcEvAs4qcMzP3f2E9Jc+85ehYqtaM1QbL9JwCgZ4d38G2FGFsTS0iq1qzZgxF869K9jGDwu+n3uXLrCK1LGoqmhOMbN1wBbgv7j7xrCDzOxK4EqAY445JqK3rg8V2Yw7l2rjRRpKUZtum1kP8CN3nx7y2L8BUu6+28w+Btzp7lMKvaY23U6A9Uu02lWkxlR10213f8vdd6d/fgxoMbPO0b6uVJh6xovUvVEHeDN7l5lZ+ueT06+5fbSvKxWmuniRulcwB29m3wc+AnSaWR/wj0ALgLvfA3wC+KyZ7QcGgEu8mLyPjKiiq1pBdfEiDaBggHf3Sws8fjdwd2Qjkmj3as1nXHc6PRNyv4jUBa1kTaCKr2oF1cWLNAAF+ASq+KpWUF28SANQN8kEmtjRTn9IMI9sVWuG6uJF6ppm8AlU8VWt+ahfvEhd0Qw+gaqyqjWX+sWL1J2iVrJWglayJszC6XmqaibBdaGNREUkBlVdySp1QnXxInVHKZoaUPFFT6C6eJE6pBl8wlV8K78M1cWL1B0F+ISryqInUF28SB1SiibhqrLoKSOsLl4thUVqlmbwCVfxrfxGopbCIjVNAT7hYlv0BGopLFLjlKJJuFgWPWWodFKkpinA14ALZnVVJ6DnUumkSE1TikbyU+mkSE3TDL7GVGXRU0amWkZVNCI1SQG+hlRlp6dcaiksUrOUoqkhVVv0NBK1FBapGZrB15CqLnoKo5bCIjVFM/gaEuuiJ1BdvEiNUYCvIbEuegLVxYvUGKVoakisi55AdfEiNUYBvsbEtugJghLJ7Bw8qC5eJMEU4GtYVWviQXXxIjVGAb5GxVITD8Pr4jNlkwr4Iomji6w1KjE18WonLJJYBQO8md1vZm+Y2Ut5Hjczu8vMNpvZejM7MfphSq7Ya+JBZZMiCVfMDH4RcNYIj58NTEl/XQl8a/TDkkJir4kHlU2KJFzBAO/uzwA7RjjkfGCxB34FdJjZu6MaoISLvSYe8pdHqmxSJBGiyMF3AdnF0X3p+4YxsyvNbJWZrdq2bVsEb924LpjVxa0XHk9XRzsGdHW0c+uFx1e3hFLthEUSrapVNO5+L3AvQG9vr1fzvetRrDXxoLJJkYSLIsD3A5Oybnen75MqqnpNfIbKJkUSK4oUzQrgU+lqmg8Au9x9awSvK0XK1MT37xzAOVQTv3xNlc+zKpsUSZRiyiS/D/wSmGpmfWb2D2Z2lZldlT7kMeA1YDPwL8DVFRuthEpETTyobFIkYQqmaNz90gKPO/C5yEYkJUtETTyobFIkYbSStQ4koiYeVDYpkjAK8HUgETXxoLJJkYRRs7E6EHuf+AyVTYokigUp9Orr7e31VatWxfLe9S62ksl81i9R0BeJiJmtdvfeYo7VDL7OxNZGOB9t1C0SG+Xg60xiSiYzVDopEhsF+DqTmJLJDJVOisRGAb7OJKZkMkOlkyKxUYCvM4kpmcxQ6aRIbBTg60xuG+GO9hbaWpq47qG1zF6wsvr9aWbMhXPvgnGTAAu+z7wsyMHf3BE0JlOvGpGKUJlkHcutqIFgNl/1vvHZcqtqIJjRn3uXqmpEilBKmaRm8HUscRU1oKoakSpSgK9jiauoAVXViFSRAnwdS1xFDaiqRqSKFODrWOIqakBVNSJVpABfxxJXUQPhVTXn3hU8tnC6KmtEIqQqmgaRyIqaDFXWiBRNVTQyTCIrajJUWSNSEQrwDSKRFTUZqqwRqQgF+AaRyIqaDFXWiFSEAnyDCKuoaWky9uzbz+T5P47voiuoskakQhTgG0RYRQ0Gf94ziHNoY5DEVNaoX43IqKmKpkHNXrCS/pD8e1dHO7+YPyeGEWVRVY1IXqqikYISfdFVVTUikVCAb1D5Lq46xJuPB1XViEREAb5BhV10zYg1Hw+qqhGJSFEB3szOMrNXzGyzmc0PeXyemW0zs7Xpr89EP1SJUvZF1zCxLoIKq6ppaoF9b+uiq0gJCgZ4M2sGvgmcDUwDLjWzaSGHPuTuJ6S/7ot4nFIBF8zq4hfz52B5Hu/fOZCMXaDajwYzGNgBOOx6PbgIqyAvMqJiZvAnA5vd/TV33wc8CJxf2WFJNY202Cm2dM2MuXDdS3DzTmg9HA7sG/q4LrqKFFRMgO8CXs+63Ze+L9e/N7P1ZrbUzCaFvZCZXWlmq8xs1bZt28oYrlTCSPl4SEDPGl10FSlLVBdZHwV63H0G8ATw3bCD3P1ed+91994JEyZE9NYyWoXy8RBz+aQuuoqUpZgA3w9kz8i70/cd5O7b3f2d9M37gPdFMzyplkw+Pl+Qj7V8Ml8rgylnqIe8yAiKCfAvAFPMbLKZtQKXACuyDzCzd2fdPA94ObohSjUlsnwyXyuDdd8LLrjqwqtIqDGFDnD3/WZ2DfA40Azc7+4bzewWYJW7rwC+YGbnAfuBHcC8Co5ZKiiz+cftj78S2sogk4+v+iYhM+YObVOwcHr+1a5qZyACqBeNjGDy/B+T719HV0c7N5w5Nb7doG7ugNDRWVB5I1Kn1ItGIpHI8smMfBdYrUk5eZE0BXjJq5jyyWvj2sA77MIrgB9AOXmRgAK85FVM+STENJvPvfBqISciLYaSBqccvBQlX//4XLHl5pWTlwahHLxErlC6JiO23Lxy8iLDKMBLUYpN10BMuXnl5EWGUYCXomVWu95x8QnJm80rJy8yjHLwUpbla/rzLoYKU/XcvHLyUqeUg5eKS/RsHvLn5NuPUv8aaRgK8DIqic3N590Varf610jDUIpGIrN8TT83LtvAwOCBgscaQQKloqmb9UuCnPuuvmBGv+/t9K5QOcZNCjYXEakBpaRoFOAlUqXm5qFKwR5GyMunRzGuO5j5q1mZJJhy8BKbUnPzcCjkVjxPP+IGIUrZSP1RgJeKKCU3n62iefp8tfLZVEopdUQpGqm4UnLz2VqajCPaxrBzzyATo0rfZOfl86ZrQCkbSSrl4CVxsnPzmZx7qSLP1S+cnq6oGUFLe7CASkFeEkIBXhItymDf0d6CGeXN8tcvCXLuuTtD5Wo/GloPP1SNo1m9xEgBXmpGOVU3Iyl5ll90yiaLZvUSIwV4qTnl5ulHUvIsv5iUzcEXbwZPaUYvVacALzUpM5vfsnOAce0tvL1vP4MHov/3mS/w3zHtt5y04R8Lp2xyaUYvVaQAL3Uhilx9KQw4t+lZ5rcu4V1sZ0tqPEc0vUMHfyniyZrRS3UowEvdqXawzziv6VkWtNzHWNtX/JNa2mHmZfDbn+rCrEROAV7qWm4qxwz+vGewYoH/vKZn+fKYJUy07aQwxliq4HNSDF1FuMdb+UnTHM5uW8fYgT8p6EvZFOClIVVjll/WjD4t5dBkh27vo5nd3k6Hvc2fGM/X983l6cNOK7/sUxqCArw0vErO8suZ0RfjHW/mbdrpYDdbvJPb9s/l0dSpwy4IjyvxZ50o6osCvEgeUQf+sBl97ky9XHu8lYcPfIjTm9Yy0d5ki3fyZOqEIbdv2z+XFalTC75W9vqA0/52Ak/9ZtuQP4NyThyj+VknnfIpwIuUaDSBP3tGv8XH82TqBC5qfiaSoJ/7PHewrNths34gPZ7wk0K5J4mohZWr5p4EknAyKvaEVepYyz3JRR7gzews4E6gGbjP3RfkPH4YsBh4H7AduNjdfz/SayrASy0IC/yZ/6gj1ekXE/Qr4R1vxjBabf/B+3JPCrm3i/mkEHZSgNJPJFE8J64TUiW0tzRz64XHlxTkIw3wZtYM/D/go0Af8AJwqbtvyjrmamCGu19lZpcAH3f3i0d6XQV4qXUjBf/cn/+8Z5Dzm57lhnTQ/7MfzpG2d0ggjlOhTwphnxxKPZFE9Zw93sr8wc/UTZDv6mjnF/PnFH18KQF+TBHHnAxsdvfX0i/+IHA+sCnrmPOBm9M/LwXuNjPzuPI/IlVwwayukmZey9ecwMWPf/TgCeFs/zmfS32PiU3b2cXhHO5DA35Uufxi5L6PFbh9mA1vKVGt54y1fXx5zBJW7KuPAL8loj5MYYoJ8F1AdoOOPuD9+Y5x9/1mtgsYD7yZfZCZXQlcCXDMMceUOWSR2jT8hHAG8F8BOAqG7SH7u47ZTPzDI7TzzsFn5M5wG9VE2x73ECIzscRNcUpRTICPjLvfC9wLQYqmmu8tkngz5g5Z+PQ3AOtPHxL0bcoZh1bIth8F+3bDgay8flNLcAbIus8JLmjmu527KKsWbPHxcQ8hEu0tzdxw5tSKvX4xAb4fmJR1uzt9X9gxfWY2BhhHcLFVREYjJ+gPkzPr5/SbgvvznRRCbjdNOQPWfa+0JmshJ5JynjMsB8/Qk89+mklhtDI0B//Npss4amxLQ1bRlKKYi6xjCC6ynk4QyF8ALnP3jVnHfA44Pusi64XuPuIabF1kFUmQ3BNFzklg2O2QE0nVntPgLR4qUSb5MeAOgjLJ+939v5nZLcAqd19hZm3A/wJmATuASzIXZfNRgBcRKV3UVTS4+2PAYzn33ZT1817golIGKSIilVVr11ZERKRICvAiInVKAV5EpE4pwIuI1KnYukma2TbgDyU8pZOclbEJo/GNXtLHqPGNjsY3Opnx/bW7TyjmCbEF+FKZ2apiS4PioPGNXtLHqPGNjsY3OuWMTykaEZE6pQAvIlKnainA3xv3AArQ+EYv6WPU+EZH4xudksdXMzl4EREpTS3N4EVEpAQK8CIidaomAryZnWVmr5jZZjObH/d4spnZJDN7ysw2mdlGM/ti3GMKY2bNZrbGzH4U91hymVmHmS01s9+Y2ctmdkrcY8pmZtel/25fMrPvp7unxj2m+83sDTN7Keu+o83sCTP7bfr7UQkb3+3pv+P1ZvaImXUkaXxZj33JzNzMOuMYW3oMoeMzs8+n/ww3mtlthV4n8QE+ven3N4GzgWnApWY2Ld5RDbEf+JK7TwM+AHwuYePL+CLwctyDyONO4P+4+98CM0nQOM2sC/gC0Ovu0wlaZl8S76gAWASclXPffOBJd58CPJm+HZdFDB/fE8B0d59BsMfEjdUeVJZFDB8fZjaJYC/FP1Z7QDkWkTM+MzuNYP/rme7+XuAbhV4k8QGerE2/3X0fkNn0OxHcfau7v5j++S8EwalyW7SUwcy6gb8H7ot7LLnMbBzwIeA7AO6+z913xjqo4cYA7enNb8YCW2IeD+7+DMHeC9nOB76b/vm7wAXVHFO2sPG5+0/dPbM1068IdoeLRZ4/P4CFwJcJNpeKTZ7xfRZY4O7vpI95o9Dr1EKAD9v0O1EBNMPMegg2Pfl1zEPJdQfBP9pUzOMIMxnYBvzPdArpPjM7PO5BZbh7P8FM6Y/AVmCXu/803lHl9VfuvjX985+Av4pzMAX8J+AncQ8im5mdD/S7+7q4x5LHe4B/Z2a/NrOnzeykQk+ohQBfE8zsCOAHwLXu/lbc48kws3OAN9x9ddxjyWMMcCLwLXefBbxNvKmFIdJ57PMJTkQTgcPN7D/EO6rCPKh/TmQNtJl9lSC1+UDcY8kws7HAV4CbCh0bozHA0QSp4BuAJWbZO9oOVwsBvphNv2NlZi0Ewf0Bd18W93hyzAbOM7PfE6S35pjZ/453SEP0AX3unvnUs5Qg4CfF3wG/c/dt7j4ILAM+GPOY8vlXM3s3QPp7wY/w1WZm84BzgE96shbh/A3BSXxd+v9KN/Cimb0r1lEN1Qcs88DzBJ/IR7wQXAsB/gVgiplNNrNWggtcK2Ie00HpM+h3gJfd/Z/iHk8ud7/R3bvdvYfgz26luydmBurufwJeN7Op6btOBzbFOKRcfwQ+YGZj03/Xp5Ogi8A5VgCfTv/8aeCHMY5lGDM7iyBVeJ6774l7PNncfYO7/1t370n/X+kDTkz/+0yK5cBpAGb2HqCVAt0vEx/g0xdlrgEeJ/iPtcTdN8Y7qiFmA5cTzIzXpr8+FvegaszngQfMbD1wAvDf4x3OIelPFkuBF4ENBP9nYl/SbmbfB34JTDWzPjP7B2AB8FEz+y3BJ48FCRvf3cCRwBPp/yf3JGx8iZFnfPcDx6ZLJx8EPl3oU5BaFYiI1KnEz+BFRKQ8CvAiInVKAV5EpE4pwIuI1CkFeBGROqUALyJSpxTgRUTq1P8H0O8Rf0GHW40AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"fcuda = \"iter_cuda.txt\"\n",
"fcpu = \"iter.txt\"\n",
"\n",
"v_cuda = []\n",
"v_cpu = []\n",
"t_cuda = []\n",
"t_cpu = []\n",
"\n",
"\n",
"# open file and read\n",
"with open(fcuda, \"r\") as f:\n",
" cuda = f.readlines()\n",
" t = 0\n",
" for line in cuda:\n",
" v = float(line.split(',')[0].split(\":\")[1])\n",
" v_cuda.append(v)\n",
" t += float(line.split(',')[-1])\n",
" t_cuda.append(t)\n",
"\n",
"with open(fcpu, \"r\") as f:\n",
" cpu = f.readlines()\n",
" t = 0\n",
" for line in cpu:\n",
" v = float(line.split(',')[0].split(\":\")[1])\n",
" v_cpu.append(v)\n",
" t += float(line.split(',')[-1])\n",
" t_cpu.append(t)\n",
"\n",
"# plot\n",
"plt.scatter(t_cuda, v_cuda, label=\"cuda\")\n",
"plt.scatter(t_cpu, v_cpu, label=\"cpu\")\n",
"#plt.plot(v_cuda, label=\"cuda\")\n",
"#plt.plot(v_cpu, label=\"cpu\")\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.1 64-bit ('3.9.1')",
"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": "3.9.1"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "02f83e1f4cd9619657a6845405e2dd67c4de23753ba48bca5dce2ebf57b3813a"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}