openmp/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenMP Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import genfromtxt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pi number computation\n",
    "\n",
    "The first problem to analyze is the computation of the Pi number using the Monte Carlo method. This will be implemented in 4 different ways:\n",
    "- Using the CPU with OpenMP parallelization and a critical section to sum the results.\n",
    "- Using the CPU with OpenMP parallelization and an atomic variable to sum the results.\n",
    "- Using the CPU with OpenMP parallelization and a reduction algorithm to aggregate the results. Each task compute an specific point.\n",
    "- Using the GPU with OpenMP parallelization and a reduction algorithm to aggregate the results. Each task compute a batch of points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compilation and performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "!make pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>num_steps</th>\n",
       "      <th>num_threads</th>\n",
       "      <th>result</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>critical</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.14159</td>\n",
       "      <td>0.000904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>critical</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.14159</td>\n",
       "      <td>0.000315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>critical</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.14159</td>\n",
       "      <td>0.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>critical</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.14159</td>\n",
       "      <td>0.000347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>critical</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>3.14159</td>\n",
       "      <td>0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>split</td>\n",
       "      <td>10000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.14162</td>\n",
       "      <td>0.007750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>split</td>\n",
       "      <td>10000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.14162</td>\n",
       "      <td>0.007029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>split</td>\n",
       "      <td>10000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.14162</td>\n",
       "      <td>0.008722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>split</td>\n",
       "      <td>10000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.14162</td>\n",
       "      <td>0.008116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>split</td>\n",
       "      <td>10000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3.14162</td>\n",
       "      <td>0.008243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>640 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       method  num_steps  num_threads   result   runtime\n",
       "0    critical      10000            1  3.14159  0.000904\n",
       "1    critical      10000            1  3.14159  0.000315\n",
       "2    critical      10000            1  3.14159  0.000335\n",
       "3    critical      10000            1  3.14159  0.000347\n",
       "4    critical      10000            1  3.14159  0.000310\n",
       "..        ...        ...          ...      ...       ...\n",
       "635     split   10000000            8  3.14162  0.007750\n",
       "636     split   10000000            8  3.14162  0.007029\n",
       "637     split   10000000            8  3.14162  0.008722\n",
       "638     split   10000000            8  3.14162  0.008116\n",
       "639     split   10000000            8  3.14162  0.008243\n",
       "\n",
       "[640 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pi = pd.read_csv(\"pi.csv\")\n",
    "df_pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_steps</th>\n",
       "      <th>num_threads</th>\n",
       "      <th>result</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.400000e+02</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.777500e+06</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.143765</td>\n",
       "      <td>0.308784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.191117e+06</td>\n",
       "      <td>2.683048</td>\n",
       "      <td>0.007569</td>\n",
       "      <td>1.299593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.141590</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.750000e+04</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>3.141590</td>\n",
       "      <td>0.000480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.141590</td>\n",
       "      <td>0.003689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.250000e+06</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.141598</td>\n",
       "      <td>0.035385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.172910</td>\n",
       "      <td>10.391600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          num_steps  num_threads      result     runtime\n",
       "count  6.400000e+02   640.000000  640.000000  640.000000\n",
       "mean   2.777500e+06     3.750000    3.143765    0.308784\n",
       "std    4.191117e+06     2.683048    0.007569    1.299593\n",
       "min    1.000000e+04     1.000000    3.141590    0.000179\n",
       "25%    7.750000e+04     1.750000    3.141590    0.000480\n",
       "50%    5.500000e+05     3.000000    3.141590    0.003689\n",
       "75%    3.250000e+06     5.000000    3.141598    0.035385\n",
       "max    1.000000e+07     8.000000    3.172910   10.391600"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pi.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_num_steps = {1 : \"darkgreen\", 2 : \"green\", 4 : \"springgreen\", 8 : \"lime\"}\n",
    "ax_methods = { \"critical\": [0,0], \"atomic\": [1, 0], \"reduction\": [0, 1], \"split\": [1, 1]}\n",
    "\n",
    "x = 'num_steps'\n",
    "y = 'runtime'\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 10), sharex=True, sharey=True)\n",
    "\n",
    "for x_value in df_pi[x].unique():\n",
    "    for method in df_pi['method'].unique(): \n",
    "        ax_x, ax_y = ax_methods[method];\n",
    "        ax = axs[ax_x, ax_y]\n",
    "\n",
    "        df_x_value = df_pi[(df_pi[x] == int(x_value))]\n",
    "        df_plot = df_x_value[df_x_value['method'] == method]\n",
    "        \n",
    "        mean_stats = df_plot.groupby([x,'method', 'num_threads']).mean().reset_index()\n",
    "        \n",
    "        ax.plot(mean_stats['num_threads'], mean_stats[y], linestyle=\"solid\", label=x_value)\n",
    "        sct = ax.scatter(df_plot['num_threads'], df_plot[y], c=df_plot['num_threads'].apply(lambda n : color_num_steps[n]))\n",
    "        ax.set_xlabel(\"N threads\")\n",
    "        ax.set_ylabel(\"Runtime\")\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')\n",
    "        ax.set_title(\"Method: \" + method)\n",
    "        ax.legend(title=\"N steps\")\n",
    "    \n",
    "fig.suptitle('Runtime vs N threads by each method', fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous diagrams, we can see the comparaison between the 4 methods according the number of threads used vs the runtime. Something interesting to notice is that the runtime of the first two methods is always increasing while adding more threads. However, the runtime of the last two (reduce and split) is reducing as the number of threads increase. This is due to the last two methods create multiple tasks that can be done by different threads in parallel. On the other side, the first two methods block a part of the memory (atomic) or the execution (critical) and generate a bottleneck where the threads have to wait to continue doing their work. \n",
    "\n",
    "We can also see that the run times of different number of steps come closer as the number of threads increase for the last two methods. This is due to the fact that the more threads we have, the more tasks we can create and the more work we can do in parallel.\n",
    "\n",
    "Last but not least, we can see that the run times of each repetition are more spread on critical and atomic, in other words, it is more difficult to determine what is going to be the final runtime. This differs from the last two methods that doesn't have too much spreads on the samples and the final runtime is more predictable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Runtime Analysis of each method by N steps')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_num_steps = { 10000: [0,0], 100000: [1, 0], 1000000: [0, 1], 10000000: [1, 1]}\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 10), sharex=True, sharey=True)\n",
    "\n",
    "for x_value in df['num_steps'].unique():\n",
    "  ax_x, ax_y = ax_num_steps[x_value];\n",
    "  ax = axs[ax_x, ax_y]\n",
    "\n",
    "  df_x_value = df[(df['num_steps'] == int(x_value))]\n",
    "  df_threads = df_x_value.pivot(columns='method', values='runtime')\n",
    "  df_threads.boxplot(ax=ax)\n",
    "  plt.yscale('log')\n",
    "  ax.set_title('Results with ' + str(x_value) + ' steps')\n",
    "  ax.set_xlabel('Method')\n",
    "  ax.set_ylabel('Runtime')\n",
    "\n",
    "fig.suptitle('Runtime Analysis of each method by N steps', fontsize='large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the latest diagram, we have the general results of all executions, all the number of threads and steps with a repetition of 10 times by each execution. They are divided by method and by N steps.\n",
    "\n",
    "We can look that the critical method is the worst between all the methods. With a mean between `E-2` and `E-1`. This result shows that the **critical sections** in competing software must be thought of with calm because they can cause all the software to lose performance. By the contrary, methods without critical sections are faster due to the threads doesn't have to wait in queue for each other to finish doing what they are doing. In this case reduction and split (which uses reduce under the hood) are faster with a mean around `E-3`. We can look that even in the worst reduction execution or split, it is faster than the critical mean. Also, we can look that even if the split's mean says that split is faster than reduction, the standard deviation is almost the same, or in other words, the best reduction case is almost the same as the best of split.\n",
    "\n",
    "As conclusion, we can say that for similar problems is better use reduction, but it is important to visualize that it uses more memory in the machine. So if the data allows it, we can use reduction and only if the difference is really large, we can use split. The split method is not the best in all cases, because it makes more complex the code, but it can be efficient in the case that you really have a lot of data and not enough memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compilation and performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!make vector\n",
    "!make vector_omp\n",
    "!make vector_simd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>num_threads</th>\n",
       "      <th>nrepeat</th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>Gbytes</th>\n",
       "      <th>time</th>\n",
       "      <th>method</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1.120000e-07</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>vector</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>1.216000e-06</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>vector</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>256</td>\n",
       "      <td>128</td>\n",
       "      <td>2.652160e-04</td>\n",
       "      <td>0.014426</td>\n",
       "      <td>vector</td>\n",
       "      <td>32768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1024</td>\n",
       "      <td>512</td>\n",
       "      <td>4.206590e-03</td>\n",
       "      <td>0.186366</td>\n",
       "      <td>vector</td>\n",
       "      <td>524288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>4096</td>\n",
       "      <td>2048</td>\n",
       "      <td>6.715800e-02</td>\n",
       "      <td>2.917000</td>\n",
       "      <td>vector</td>\n",
       "      <td>8388608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>256</td>\n",
       "      <td>128</td>\n",
       "      <td>2.652160e-04</td>\n",
       "      <td>0.119393</td>\n",
       "      <td>vector_simd</td>\n",
       "      <td>32768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>1024</td>\n",
       "      <td>512</td>\n",
       "      <td>4.206590e-03</td>\n",
       "      <td>0.202780</td>\n",
       "      <td>vector_simd</td>\n",
       "      <td>524288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>4096</td>\n",
       "      <td>2048</td>\n",
       "      <td>6.715800e-02</td>\n",
       "      <td>1.392180</td>\n",
       "      <td>vector_simd</td>\n",
       "      <td>8388608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>16384</td>\n",
       "      <td>8192</td>\n",
       "      <td>1.073940e+00</td>\n",
       "      <td>19.079700</td>\n",
       "      <td>vector_simd</td>\n",
       "      <td>134217728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>100</td>\n",
       "      <td>32768</td>\n",
       "      <td>16384</td>\n",
       "      <td>4.295360e+00</td>\n",
       "      <td>118.382000</td>\n",
       "      <td>vector_simd</td>\n",
       "      <td>536870912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    num_threads  nrepeat      N      M        Gbytes        time       method  \\\n",
       "0             1      100      4      2  1.120000e-07    0.000008       vector   \n",
       "1             1      100     16      8  1.216000e-06    0.000080       vector   \n",
       "2             1      100    256    128  2.652160e-04    0.014426       vector   \n",
       "3             1      100   1024    512  4.206590e-03    0.186366       vector   \n",
       "4             1      100   4096   2048  6.715800e-02    2.917000       vector   \n",
       "..          ...      ...    ...    ...           ...         ...          ...   \n",
       "23            8      100    256    128  2.652160e-04    0.119393  vector_simd   \n",
       "24            8      100   1024    512  4.206590e-03    0.202780  vector_simd   \n",
       "25            8      100   4096   2048  6.715800e-02    1.392180  vector_simd   \n",
       "26            8      100  16384   8192  1.073940e+00   19.079700  vector_simd   \n",
       "27            8      100  32768  16384  4.295360e+00  118.382000  vector_simd   \n",
       "\n",
       "            S  \n",
       "0           8  \n",
       "1         128  \n",
       "2       32768  \n",
       "3      524288  \n",
       "4     8388608  \n",
       "..        ...  \n",
       "23      32768  \n",
       "24     524288  \n",
       "25    8388608  \n",
       "26  134217728  \n",
       "27  536870912  \n",
       "\n",
       "[63 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vector = pd.read_csv(\"vector.csv\")\n",
    "df_omp = pd.read_csv(\"vector_omp.csv\")\n",
    "df_simd = pd.read_csv(\"vector_simd.csv\")\n",
    "\n",
    "\n",
    "df_vector[\"method\"] = \"vector\"\n",
    "df_omp[\"method\"] = \"vector_omp\"\n",
    "df_simd[\"method\"] = \"vector_simd\"\n",
    "\n",
    "df_vector_methods = pd.concat([df_vector, df_omp, df_simd])\n",
    "\n",
    "df_vector_methods[\"S\"] = df_vector_methods[\"N\"] * df_vector_methods[\"M\"]\n",
    "df_vector_methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>num_threads</th>\n",
       "      <th>nrepeat</th>\n",
       "      <th>N</th>\n",
       "      <th>M</th>\n",
       "      <th>Gbytes</th>\n",
       "      <th>time</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.300000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.444444</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7792.571429</td>\n",
       "      <td>3896.285714</td>\n",
       "      <td>7.772759e-01</td>\n",
       "      <td>46.650025</td>\n",
       "      <td>9.714778e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.692749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11650.139999</td>\n",
       "      <td>5825.069999</td>\n",
       "      <td>1.494128e+00</td>\n",
       "      <td>112.761056</td>\n",
       "      <td>1.867491e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.120000e-07</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>8.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.216000e-06</td>\n",
       "      <td>0.008436</td>\n",
       "      <td>1.280000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1024.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>4.206590e-03</td>\n",
       "      <td>0.167159</td>\n",
       "      <td>5.242880e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>16384.000000</td>\n",
       "      <td>8192.000000</td>\n",
       "      <td>1.073940e+00</td>\n",
       "      <td>16.267350</td>\n",
       "      <td>1.342177e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32768.000000</td>\n",
       "      <td>16384.000000</td>\n",
       "      <td>4.295360e+00</td>\n",
       "      <td>470.555000</td>\n",
       "      <td>5.368709e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_threads  nrepeat             N             M        Gbytes  \\\n",
       "count    63.000000     63.0     63.000000     63.000000  6.300000e+01   \n",
       "mean      3.444444    100.0   7792.571429   3896.285714  7.772759e-01   \n",
       "std       2.692749      0.0  11650.139999   5825.069999  1.494128e+00   \n",
       "min       1.000000    100.0      4.000000      2.000000  1.120000e-07   \n",
       "25%       1.000000    100.0     16.000000      8.000000  1.216000e-06   \n",
       "50%       2.000000    100.0   1024.000000    512.000000  4.206590e-03   \n",
       "75%       4.000000    100.0  16384.000000   8192.000000  1.073940e+00   \n",
       "max       8.000000    100.0  32768.000000  16384.000000  4.295360e+00   \n",
       "\n",
       "             time             S  \n",
       "count   63.000000  6.300000e+01  \n",
       "mean    46.650025  9.714778e+07  \n",
       "std    112.761056  1.867491e+08  \n",
       "min      0.000008  8.000000e+00  \n",
       "25%      0.008436  1.280000e+02  \n",
       "50%      0.167159  5.242880e+05  \n",
       "75%     16.267350  1.342177e+08  \n",
       "max    470.555000  5.368709e+08  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vector_methods.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_num_threads = {1 : \"darkgreen\", 2 : \"green\", 4 : \"springgreen\", 8 : \"lime\"}\n",
    "color_s = {8 : \"darkgreen\", 128 : \"green\", 32768 : \"springgreen\", 524288 : \"lime\", 8388608: \"red\", \"134217728\": \"black\", 536870912: \"blue\"}\n",
    "ax_methods = { \"vector\": [0,0], \"vector_omp\": [1, 0], \"vector_simd\": [2, 0]}\n",
    "\n",
    "x = 'S'\n",
    "y = 'time'\n",
    "\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True)\n",
    "\n",
    "for x_value in df_vector_methods[x].unique():\n",
    "    for method in df_vector_methods['method'].unique(): \n",
    "        ax_x, ax_y = ax_methods[method];\n",
    "        ax = axs[ax_x]\n",
    "\n",
    "        df_x_value = df_vector_methods[(df_vector_methods[x] == int(x_value))]\n",
    "        df_plot = df_x_value[df_x_value['method'] == method]\n",
    "        \n",
    "        mean_stats = df_plot.groupby([x,'method', 'num_threads']).mean().reset_index()\n",
    "        \n",
    "        ax.plot(mean_stats['num_threads'], mean_stats[y], linestyle=\"solid\", label=x_value)\n",
    "        sct = ax.scatter(df_plot['num_threads'], df_plot[y], c=df_plot['num_threads'].apply(lambda n : color_num_threads[n]))\n",
    "        ax.set_xlabel(\"N threads\")\n",
    "        ax.set_ylabel(\"Runtime\")\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')\n",
    "        ax.set_title(\"Method: \" + method)\n",
    "        ax.legend(title=\"Matrix Size\")\n",
    "    \n",
    "fig.suptitle('Runtime vs N threads using specific matrix sizes by each method', fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous diagram we can see the comparaison between the 3 methods according the number of threads used vs the runtime. The first method doesn't use OMP or SIMD, so it is done sequential, this mean that it is only executed in one thread. \n",
    "\n",
    "The other two methods are executed using OMP and OMP+SIMD. The second one uses OMP on the first iteration (rows) and sequential in the second one (columns) and the third one uses OMP on the first iteration (rows) and SIMD in the second one (columns).\n",
    "\n",
    "We can see that the performance in one thread is the same for the all the methods due to they are the same. However, when we add more threads, the concurrent methods are increasing the runtime (going slower) for small matrix sizes but reducing the runtime (going faster) for higher matrix sizes. This is due to the fact that dividing the work is also a work that have to be done, this add extra time to the execution. Therefore, for small sizes it is better to do it sequentially, but for higher sizes it is better to divide the work and do it in parallel as we can see in the diagrams.\n",
    "\n",
    "We can see that if we count with a sufficient amount of thread and a substantial matrix size we should opt for OMP+SIMD. If we don't have a lot of threads but the matrix size is still big, we should opt for only OMP and if the matrix size is small we should opt for sequential. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Runtime Analysis of each method by matrix size')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_s = {8 : \"violet\", 128 : \"green\", 32768 : \"yellow\", 524288 : \"lime\", 8388608: \"red\", 134217728: \"black\", 536870912: \"blue\"}\n",
    "\n",
    "x = 'S'\n",
    "\n",
    "for x_value in df_vector_methods[x].unique():\n",
    "  df_x_value = df_vector_methods[(df_vector_methods[x] == int(x_value))]\n",
    "  df_threads = df_x_value.pivot(columns='method', values='time')\n",
    "  df_threads.boxplot(color=color_s[x_value])\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.xlabel('Method')\n",
    "plt.ylabel('Runtime')\n",
    "plt.title('Runtime Analysis of each method by matrix size', fontsize='large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the latest diagram, we have the general results of all executions, all the number of threads and matrix sizes. The idea is to visualize the mean, medium and standard deviation of each method for each matrix size. Each color is a different matrix size. More in the top, bigger the matrix size. \n",
    "\n",
    "We can compare the last two methods against the first one and see that the mean is increasing for the same small matrix size while using the parallel methods. This is due to the fact that the parallel methods are adding extra time to the execution while dividing the tasks between threads. However, for bigger matrix sizes, the mean is decreasing for the parallel methods. This is due to the fact that the parallel methods are efficiently dividing the tasks. We can see that the standard deviation is higher while using OMP+SIMD. This is due to the fact that the SIMD method is more complex and it is more difficult to divide the tasks between threads. This means that we're adding extra complexity to the system and we should plan it carefully."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}