scaling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85409644-fb29-4109-9a7d-5e87bebf9f26",
   "metadata": {},
   "source": [
    "# Empirical Observations On Scaled Running Times\n",
    "\n",
    "In the survey article [Route Planning in Transportation Networks](https://doi.org/10.1007/978-3-319-49487-6_2), a [methodology](https://i11www.iti.kit.edu/~pajor/survey/) for scaling running times from different machines is proposed to make them comparable.\n",
    "The methodology is based on a benchmark score for each machine, measuring the running time of a few one-to-all searches with Dijkstra's algorithm.\n",
    "However, Dijkstra's algorithm on road networks is primarily bottlenecked by memory bandwidth.\n",
    "In contrast, the speedup techniques considered in the article are often much more sensitive to CPU frequency, cache performance and other features of modern hardware.\n",
    "Here, we try to gather some empirical evidence on the quality of the predictions made by these benchmark scores.\n",
    "We compare benchmark scores with average running times of one million random CH queries on DIMACS Europe with the implementation of [RoutingKit](https://github.com/RoutingKit/RoutingKit) on all machines of the KIT-Algo group.\n",
    "This notebook contains the raw results and some visualizations comparing the observed performance to predictions made by the proposed scaling methodology.\n",
    "Benchmark machine hardware properties are reported in the `machines.txt` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dac7381f-f2d1-4cde-a561-659a6cb44abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c4fd22-568d-4494-93ca-596e2298c194",
   "metadata": {},
   "source": [
    "The following table contains the raw observations. The Dijkstra column contains the benchmark score in milliseconds, CH the average CH query running time in microseconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b01ff0d5-8f01-4bb4-a7a7-d5b2561d960f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>machine</th>\n",
       "      <th>Dijkstra</th>\n",
       "      <th>CH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>compute1</td>\n",
       "      <td>69621.1</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>compute3</td>\n",
       "      <td>32669.5</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>compute4</td>\n",
       "      <td>34789.8</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>compute6</td>\n",
       "      <td>35208.0</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>compute8</td>\n",
       "      <td>116318.0</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>compute10</td>\n",
       "      <td>64806.3</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>compute11</td>\n",
       "      <td>39028.7</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gpute1</td>\n",
       "      <td>33584.3</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>phipute1</td>\n",
       "      <td>37360.8</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>laptop</td>\n",
       "      <td>33940.4</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     machine  Dijkstra   CH\n",
       "0   compute1   69621.1  226\n",
       "1   compute3   32669.5   91\n",
       "2   compute4   34789.8   86\n",
       "3   compute6   35208.0  110\n",
       "4   compute8  116318.0  429\n",
       "5  compute10   64806.3  249\n",
       "6  compute11   39028.7  104\n",
       "7     gpute1   33584.3   97\n",
       "8   phipute1   37360.8  101\n",
       "9     laptop   33940.4  132"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('scaling.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b4a981-b0a9-487d-8ccf-8ace564a2e50",
   "metadata": {},
   "source": [
    "The following figure contains all measurements as datapoints. The line indicates the running time as predicted by the scaling methodology based on the performance of our machine with the fastest Dijkstra score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9031cdd0-5fd1-48ad-87fc-502892580d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f642d265510>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=data, x='Dijkstra', y='CH')\n",
    "plt.plot([32669.5, 32669.5 * 4], [91, 91 * 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6524ca6f-898a-46d3-b14b-5c3f3534b663",
   "metadata": {},
   "source": [
    "The following figure contains the measurements only for the faster machines. The line indicates the running time as predicted by the scaling methodology based on the performance of our machine with the fastest Dijkstra score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de7b6027-9285-4222-9f6b-625ee56c91c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f642d2661a0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=data.query('Dijkstra < 40000'), x='Dijkstra', y='CH')\n",
    "plt.plot([32669.5, 32669.5 * 1.2], [91, 91 * 1.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6504f8e-c1f8-450d-bc0d-a78692474ec6",
   "metadata": {},
   "source": [
    "## Observations\n",
    "\n",
    "- There is some roughly linear correlation between Dijkstra performance and CH performance.\n",
    "- Extrapolating from one measurement, i.e. using a linear function through (0,0) and the measurements on one machine roughly predicts performance tendencies. Still, the slope of that function appears to be slightly off. A linear regression through multiple measurements would yield more accurate predictions. Nevertheless, such a linear function's slope will likely vary between different algorithms.\n",
    "- There is a cluster of benchmark scores between 30000 and 40000 but nothing faster than 30000. However, these scores were obtained with machines with highly heterogeneous hardware properties. Prediction quality for these machines is [bad](https://xkcd.com/1725/)! More investigation is necessary!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}