SuperLU.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "# Sequential SuperLU Traces\n",
       "\n",
       "The traces share the following properties: \n",
       "\n",
       "* CFD Problems\n",
       "* 100% Pattern symmetry\n",
       "* <100% Numeric symmetry\n",
       "* Full rank\n",
       "\n",
       "This trace is from `EXAMPLE/ditersol`, which ships with SuperLU. \n",
       "We have slightly modified it to accept matrices as command line arguments, \n",
       "as opposed to reading them off of `stdin`. \n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%markdown\n",
    "\n",
    "# Sequential SuperLU Traces\n",
    "\n",
    "The traces share the following properties: \n",
    "\n",
    "* CFD Problems\n",
    "* 100% Pattern symmetry\n",
    "* <100% Numeric symmetry\n",
    "* Full rank\n",
    "\n",
    "This trace is from `EXAMPLE/ditersol`, which ships with SuperLU. \n",
    "We have slightly modified it to accept matrices as command line arguments, \n",
    "as opposed to reading them off of `stdin`. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sveCacheSim as sim\n",
    "import CacheModels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as nppace\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import importlib\n",
    "importlib.reload(sim)\n",
    "pass\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/storage/home/hhive1/plavin3/data/ModelSwapping')\n",
    "slu = {}\n",
    "\n",
    "## Choose a group of runs\n",
    "#GROUP='slu'\n",
    "GROUP='parsec'\n",
    "\n",
    "traces_slu = ['steam1', 'steam2', 'orsirr_1', 'orsirr_2', 'orsreg_1']\n",
    "traces_parsec = ['blackscholes', 'bodytrack', 'ferret', 'fluidanimate', 'freqmine']\n",
    "models = ['BASE', 'FR', 'M4', 'M8', 'ALL']\n",
    "\n",
    "if GROUP =='slu':\n",
    "    traces = traces_slu\n",
    "elif GROUP == 'parsec':\n",
    "    traces = traces_parsec\n",
    "else:\n",
    "    raise ValueError()\n",
    "    \n",
    "for tr in traces:\n",
    "    slu[tr] = {}\n",
    "    for mo in models:\n",
    "        if GROUP == 'slu':\n",
    "            filename = 'DataV4/slu_{}-{}-data.pkl'.format(tr, mo)\n",
    "        elif GROUP=='parsec':\n",
    "            filename = 'DataV4/{}-{}-data.pkl'.format(tr, mo)\n",
    "            \n",
    "        try:\n",
    "            slu[tr][mo] = sim.load_object(filename)\n",
    "\n",
    "\n",
    "            \n",
    "        except:\n",
    "            print('Failed to load {}'.format(filename))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "### Phase Trace Object\n",
       "We first need to grab the phase trace object from the stats of our simulation. It is stored in `stats_*.phase_trace`. This object\n",
       "has type `PhaseTrace` which is defined in `PhaseDetector.py` and includes 4 fields.\n",
       "\n",
       "* `trace`: The phase assigned to each interval. -1 represents an interval not identified as part of a phase. \n",
       "* `nphases`: The number of unique phases identified.\n",
       "* `phase_count`: A list of length nphases where phase_count[i] is the number of intervals assigned to phase i\n",
       "* `phase_unique`: A list of length nphases where phase_unique[i] is the number of times phase i was entered\n",
       "\n",
       "Let's take a look at these for the 'large' trace. \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%markdown\n",
    "\n",
    "### Phase Trace Object\n",
    "We first need to grab the phase trace object from the stats of our simulation. It is stored in `stats_*.phase_trace`. This object\n",
    "has type `PhaseTrace` which is defined in `PhaseDetector.py` and includes 4 fields.\n",
    "\n",
    "* `trace`: The phase assigned to each interval. -1 represents an interval not identified as part of a phase. \n",
    "* `nphases`: The number of unique phases identified.\n",
    "* `phase_count`: A list of length nphases where phase_count[i] is the number of intervals assigned to phase i\n",
    "* `phase_unique`: A list of length nphases where phase_unique[i] is the number of times phase i was entered\n",
    "\n",
    "Let's take a look at these for the 'large' trace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blackscholes :\n",
      "  Identified 1 phases\n",
      "  Number of intervals assigned to each phase [24]\n",
      "bodytrack :\n",
      "  Identified 20 phases\n",
      "  Number of intervals assigned to each phase [33, 347, 5, 227, 1217, 2434, 2184, 2176, 15, 24, 14, 30, 605, 604, 83, 56, 57, 56, 7, 21]\n",
      "ferret :\n",
      "  Identified 22 phases\n",
      "  Number of intervals assigned to each phase [129, 233, 224, 42, 43, 91, 33, 72, 300, 48, 31, 167, 75, 116, 938, 90, 86, 191, 72, 16, 188, 2]\n",
      "fluidanimate :\n",
      "  Identified 11 phases\n",
      "  Number of intervals assigned to each phase [33, 1106, 12, 63, 15, 2544, 3981, 93, 64, 63, 1133]\n",
      "freqmine :\n",
      "  Identified 17 phases\n",
      "  Number of intervals assigned to each phase [32, 140, 126, 35, 2, 1, 47, 46, 6, 96, 312, 4, 8, 4, 14, 185, 141]\n"
     ]
    }
   ],
   "source": [
    "def phase_summary(name, trace):\n",
    "    tr = trace.phase_trace\n",
    "    print(name, ':')\n",
    "    print('  Identified', tr.nphases, 'phases')\n",
    "    print('  Number of intervals assigned to each phase', tr.phase_count)\n",
    "    #print('  Number of times each phase was entered', tr.phase_unique)\n",
    "    \n",
    "for tr in slu.keys():\n",
    "    if 'BASE' in slu[tr].keys():\n",
    "        phase_summary(tr, slu[tr]['BASE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_trace(ax, label, trace, axis_labels):\n",
    "    ax.scatter([*range(len(trace))], trace,s=2, color='black')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.set_title(label)\n",
    "    if axis_labels:\n",
    "        ax.set_ylabel('Phase Number (-1 is uncategorized)')\n",
    "        ax.set_xlabel('Interval number (Interval = 10k inst)')\n",
    "\n",
    "fig, ax = plt.subplots(1, len(slu.keys()), figsize=(16,4))\n",
    "for idx, tr in enumerate(slu.keys()):\n",
    "    if 'BASE' in slu[tr].keys():\n",
    "        plot_trace(ax[idx], tr,  slu[tr]['BASE'].phase_trace.trace,  idx == 0)\n",
    "#plt.savefig('plots/phase-traces.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blackscholes Accuracy\n",
      "   Fixed Rate  Markov 4  Markov 8       All\n",
      "0    0.716759  0.729032  0.758609  0.758491\n",
      "\n",
      "bodytrack Accuracy\n",
      "    Fixed Rate  Markov 4  Markov 8       All\n",
      "0     0.607297  0.622994  0.699826  0.700290\n",
      "1     0.751069  0.752088  0.789465  0.790097\n",
      "2     0.626709  0.766467  0.796967  0.793333\n",
      "3     0.985080  0.624175  0.714450  0.713941\n",
      "4     0.625822  0.984446  0.983992  0.985062\n",
      "5     0.983805  0.627209  0.667981  0.667693\n",
      "6     0.828115  0.983568  0.983672  0.983819\n",
      "7     0.654131  0.830630  0.830548  0.828175\n",
      "8     0.802577  0.665862  0.722054  0.720269\n",
      "9     0.802896  0.815927  0.825582  0.814968\n",
      "10    0.650708  0.665625  0.722067  0.720249\n",
      "11    0.641686  0.661354  0.722071  0.971615\n",
      "12    0.971744  0.971502  0.978004  0.948579\n",
      "13    0.948729  0.952262  0.954219  0.931777\n",
      "14    0.931444  0.933269  0.935148  0.938414\n",
      "15    0.879914  0.937922  0.938287  0.932109\n",
      "16    0.875540  0.932765  0.932191  0.915143\n",
      "17    0.869915  0.915181  0.916935  0.500480\n",
      "18    0.502780  0.453820  0.460660  0.638779\n",
      "19    0.622174  0.625200  0.638853       NaN\n",
      "\n",
      "ferret Accuracy\n",
      "    Fixed Rate  Markov 4  Markov 8       All\n",
      "0     0.907705  0.687914  0.703953  0.704166\n",
      "1     0.771708  0.676380  0.930655  0.929179\n",
      "2     0.940608  0.929281  0.797270  0.788317\n",
      "3     0.755566  0.788036  0.952422  0.940260\n",
      "4     0.751885  0.941442  0.774578  0.771668\n",
      "5     0.662168  0.772010  0.790171  0.773606\n",
      "6     0.670296  0.773601  0.766461  0.768871\n",
      "7     0.612412  0.666355  0.750789  0.749809\n",
      "8     0.860178  0.704350  0.664835  0.665005\n",
      "9     0.860693  0.621817  0.864063  0.858570\n",
      "10    0.993873  0.858040  0.847483  0.860990\n",
      "11    0.809297  0.859410  0.991336  0.993865\n",
      "12    0.849650  0.993750  0.814452  0.831592\n",
      "13    0.687529  0.831223  0.808889  0.860777\n",
      "14    0.646932  0.861021  0.732296  0.726721\n",
      "15    0.715500  0.712050  0.748599  0.738442\n",
      "16    0.734877  0.736484  0.707357  0.717794\n",
      "17    0.581447  0.620516  0.793034  0.591446\n",
      "18    0.961527  0.590927  0.953173  0.786267\n",
      "19    0.593075  0.786143  0.621411  0.961618\n",
      "20         NaN  0.961627       NaN  0.911480\n",
      "21         NaN  0.912160       NaN       NaN\n",
      "\n",
      "fluidanimate Accuracy\n",
      "    Fixed Rate  Markov 4  Markov 8       All\n",
      "0     0.610732  0.622842  0.697877  0.697948\n",
      "1     0.890768  0.889668  0.895172  0.890743\n",
      "2     0.575080  0.879430  0.863310  0.880130\n",
      "3     0.789115  0.790472  0.803802  0.791703\n",
      "4     0.680792  0.705700  0.795517  0.795667\n",
      "5     0.780603  0.782122  0.788843  0.782133\n",
      "6     0.854113  0.856286  0.858260  0.854160\n",
      "7     0.764326  0.795821  0.879531  0.879835\n",
      "8     0.785221  0.787724  0.801558  0.787818\n",
      "9     0.783951  0.787131  0.800784  0.784161\n",
      "10    0.901735  0.900078  0.901992  0.901906\n",
      "\n",
      "freqmine Accuracy\n",
      "    Fixed Rate  Markov 4  Markov 8       All\n",
      "0     0.608670  0.620047  0.696967  0.695840\n",
      "1     0.973826  0.972751  0.970854  0.973651\n",
      "2     0.972050  0.973557  0.976518  0.972049\n",
      "3     0.738970  0.759045  0.763285  0.758006\n",
      "4          NaN       NaN       NaN       NaN\n",
      "5          NaN       NaN       NaN       NaN\n",
      "6     0.739442  0.739416  0.831982  0.831504\n",
      "7     0.851459  0.856595  0.856891  0.855902\n",
      "8     0.844800  0.799650  0.803775  0.800650\n",
      "9     0.882490  0.890065  0.889406  0.890449\n",
      "10    0.927526  0.929205  0.929063  0.927613\n",
      "11    0.880150  0.879700  0.876200  0.877050\n",
      "12    0.541517  0.570317  0.616867  0.615583\n",
      "13    0.552000  0.611900  0.622200  0.619900\n",
      "14    0.542417  0.932125  0.922692  0.931208\n",
      "15    0.743754  0.742178  0.743683  0.742561\n",
      "16    0.656622  0.661349  0.687119  0.657445\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def get_acc(st1, st2, phase):\n",
    "    # t1 is assumed to be from the base cache\n",
    "    \n",
    "    t1 = st1.cache_trace[0] # cache trace from st1\n",
    "    t2 = st2.cache_trace[0] # cache trace from st2\n",
    "    \n",
    "    # Just get accesses where were are in phase `phase` and in state is Swapped (2)\n",
    "    pick_state = t2['state'] == 2\n",
    "    pick_phase = t2['phase'] == phase\n",
    "    \n",
    "    pick = np.logical_and(pick_state, pick_phase)\n",
    "    \n",
    "    # Get the hit/miss from each \n",
    "    t1_hits = t1['isHit'][pick]\n",
    "    t2_hits = t2['isHit'][pick]\n",
    "    \n",
    "    num_correct = np.sum(np.equal(t1_hits, t2_hits))\n",
    "    acc = num_correct / len(t1_hits)\n",
    "    \n",
    "    return acc\n",
    "    \n",
    "    \n",
    "def get_stats(stats):\n",
    "    nphases = stats['BASE'].phase_trace.nphases\n",
    "    name_map={'FR':'Fixed Rate', 'M4': 'Markov 4', 'M8':'Markov 8', 'ALL':'All'}\n",
    "    data = {}\n",
    "    for name in stats:\n",
    "        if name is 'BASE':\n",
    "            continue\n",
    "        acc = []\n",
    "        for i in range(nphases):\n",
    "            acc.append(get_acc(stats['BASE'], stats[name], i))\n",
    "        data[name_map[name]] = acc\n",
    "    return pd.DataFrame(data)\n",
    "acc = {}\n",
    "for tr in slu.keys():\n",
    "    if 'BASE' in slu[tr].keys():\n",
    "        print('{} Accuracy'.format(tr))\n",
    "        acc[tr] = get_stats(slu[tr])\n",
    "        print(acc[tr])\n",
    "        print()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn=1\n",
    "\n",
    "def acc_plot(ax, acc, slu, tr):\n",
    "    df = acc[tr]\n",
    "    ny = slu[tr]['BASE'].phase_trace.nphases\n",
    "    #ax=plt.gca()\n",
    "    df.plot(xticks=[*range(0,ny,1)],\n",
    "            yticks=[i/nn for i in range(0,nn+1)], \n",
    "                 title='{} per-phase accuracy'.format(tr),ax=ax, \n",
    "                 marker='o', linestyle=(0, (2,4)))\n",
    "    ax.set_ylabel('Accuracy')\n",
    "    ax.set_xlabel('Phase')\n",
    "    #ax.get_legend().remove()\n",
    "\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    cmap = plt.get_cmap(\"tab10\")\n",
    "    #ax.text( x=ny-1+.1, y=df['Fixed Rate'][ny-1], s='Fixed Rate', color=cmap(0))\n",
    "    #ax.text( x=4.1, y=df['Markov 4'][ny-1], s='Markov 4', color=cmap(1))\n",
    "    #ax.text( x=4.1, y=df['Markov 8'][ny-1], s='Markov 8', color=cmap(2))\n",
    "    plt.tight_layout()\n",
    "\n",
    "fig, ax = plt.subplots(len(slu.keys()), 1, figsize=(8.5,10))\n",
    "for idx, tr in enumerate(slu.keys()):\n",
    "    if 'BASE' in slu[tr].keys():\n",
    "        acc_plot(ax[idx], acc, slu, tr)\n",
    "    #acc_plot(acc, slu, 'steam2')\n",
    "#plt.savefig('plots/per-phase-accuracy.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blackscholes\n",
      "bodytrack\n",
      "ferret\n",
      "fluidanimate\n",
      "freqmine\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nn=1\n",
    "maxidx=len(slu.keys())\n",
    "barwidth=.2\n",
    "\n",
    "colors = plt.cm.get_cmap('tab10').colors\n",
    "colormap={'All': colors[0], 'Markov 4':colors[1], 'Markov 8':colors[2], 'Fixed Rate':colors[3]}\n",
    "\n",
    "\n",
    "def acc_plot(ax, acc, slu, tr, idx):\n",
    "    df = acc[tr]\n",
    "    ny = slu[tr]['BASE'].phase_trace.nphases\n",
    "    #ax=plt.gca()\n",
    "    print(tr)\n",
    "    for idx,k in enumerate(acc[tr]):\n",
    "        bars=acc[tr][k]\n",
    "        r = np.arange(len(bars)) + barwidth*idx\n",
    "        ax.bar(r, bars, color=colormap[k], width=barwidth, edgecolor='white', label='var1')\n",
    "\n",
    "    #ax.xticks([r + barwidth for r in range(len(bars))], ['A', 'B', 'C', 'D'])\n",
    "    ax.set_yticks([0,1])\n",
    "    ax.set_xticks([*range(0,22,1)])\n",
    "    \n",
    "\n",
    "    \n",
    "    #df.plot(xticks=[*range(0,ny,1)],\n",
    "    #        yticks=[i/nn for i in range(0,nn+1)], \n",
    "    #             title='{}'.format(tr),ax=ax, \n",
    "    #             marker='o', linestyle=(0, (2,4)))\n",
    "    #if idx == maxidx - 1:\n",
    "    #    ax.set_ylabel('Accuracy')\n",
    "    #    ax.set_xlabel('Phase')\n",
    "\n",
    "    #ax.get_legend().remove()\n",
    "\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    #cmap = plt.get_cmap(\"tab10\")\n",
    "\n",
    "    #plt.tight_layout()\n",
    "\n",
    "fig, ax = plt.subplots(len(slu.keys()), 1, figsize=(10,9))\n",
    "for idx, tr in enumerate(slu.keys()):\n",
    "    if 'BASE' in slu[tr].keys():\n",
    "        acc_plot(ax[idx], acc, slu, tr, idx)\n",
    "    #acc_plot(acc, slu, 'steam2')\n",
    "#plt.savefig('plots/per-phase-accuracy.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## Accuracy Over Time\n",
       "Let's now take a look at how accuracy changes over the course of a simulation. Does it get worse over time? \n",
       "\n",
       "As we're using the small trace, we won't see much here. Please re-run the notebook to get a better large chart. \n",
       "\n",
       "The reason a legend isn't generated is because it is hard to place it well. To get a legend, re-run the plot and uncomment the line labeled as such. \n",
       "Then grab the legend and paste it onto the plot in a good area. \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%markdown\n",
    "\n",
    "## Accuracy Over Time\n",
    "Let's now take a look at how accuracy changes over the course of a simulation. Does it get worse over time? \n",
    "\n",
    "As we're using the small trace, we won't see much here. Please re-run the notebook to get a better large chart. \n",
    "\n",
    "The reason a legend isn't generated is because it is hard to place it well. To get a legend, re-run the plot and uncomment the line labeled as such. \n",
    "Then grab the legend and paste it onto the plot in a good area. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval_len = 10000\n",
    "def acc_over_time(st1, st2, phase):\n",
    "    \n",
    "    t1 = st1.cache_trace[0] # cache trace from st1\n",
    "    t2 = st2.cache_trace[0] # cache trace from st2\n",
    "    \n",
    "    # Just get accesses where were are in phase `phase` and in state is Swapped (2)\n",
    "    pick_state = t2['state'] == 2\n",
    "    pick_phase = t2['phase'] == phase\n",
    "    \n",
    "    pick = np.logical_and(pick_state, pick_phase)\n",
    "    \n",
    "    # Get the hit/miss from each \n",
    "    t1_hits = t1['isHit'][pick]\n",
    "    t2_hits = t2['isHit'][pick]\n",
    "    \n",
    "    nintervals = int(len(t1_hits) // interval_len)-1\n",
    "    \n",
    "    acc = []\n",
    "    \n",
    "    for i in range(nintervals):\n",
    "        start = i*interval_len\n",
    "        end   = (i+1)*interval_len\n",
    "        t1_hits_rest = t1_hits[start:end]\n",
    "        t2_hits_rest = t2_hits[start:end]\n",
    "    \n",
    "        num_correct = np.sum(np.equal(t1_hits_rest, t2_hits_rest))\n",
    "        acc.append(num_correct / interval_len)\n",
    "    \n",
    "    return acc\n",
    "\n",
    "nphases = stats['base'].phase_trace.nphases\n",
    "name_map={'fr':'Fixed Rate', 'm4': 'Markov 4', 'm8':'Markov 8', 'all':'All'}\n",
    "all_df = {}\n",
    "for i in range(nphases):\n",
    "    data = {}\n",
    "    for name in stats:\n",
    "        if name is 'base':\n",
    "            continue\n",
    "        data[name_map[name]] = acc_over_time(stats['base'], stats[name], i)\n",
    "    data_df = pd.DataFrame(data)    \n",
    "    all_df[i] = data_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretty=['Initialization', 'Marker', 'High Locality', 'Vector Add', 'Random']\n",
    "def acc_over_time_plot(ax, approx, m4, m8, phase):\n",
    "    ax.plot(approx, label='Fixed Rate')\n",
    "    ax.plot(m4, label='Markov 4')\n",
    "    ax.plot(m8, label='Markov 8')    \n",
    "    ax.set_ylim((0,1))\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.get_yaxis().set_ticks([])\n",
    "    ax.set_title('Phase {}'.format(phase))\n",
    "    ax.text(x=0, y=.05, s=pretty[phase], color='grey')\n",
    "    if phase == 0:\n",
    "        ax.set_ylabel('Accuracy')\n",
    "        ax.get_yaxis().set_ticks([.2, .4, .6, .8, 1.])\n",
    "        ax.set_xlabel('Interval number')\n",
    "    if phase == 4:\n",
    "        pass\n",
    "        #ax.legend(loc=10) ##UNCOMMENT ME FOR LEGEND\n",
    "\n",
    "fig, ax = plt.subplots(1, 5, figsize=(12,3), squeeze=False)\n",
    "for phase in range(5):    \n",
    "    acc_over_time_plot(ax[0,phase], \n",
    "          all_df[phase]['Fixed Rate'],\n",
    "          all_df[phase]['Markov 4'],\n",
    "          all_df[phase]['Markov 8'],\n",
    "          phase)\n",
    "#plt.savefig('plots/acc-over-time.svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## Accuracy as a function of Model Size\n",
       "\n",
       "This is just a fun little plot. \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%markdown\n",
    "\n",
    "## Accuracy as a function of Model Size\n",
    "\n",
    "This is just a fun little plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bs=8192\n",
    "sz = {'base':1, 'fr':24/bs, 'm4':(3 * (4 * 4) * 8)/bs, 'm8':(3 * (8 * 8) * 8)/bs}\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "cmap = plt.get_cmap(\"tab10\")\n",
    "for i in range(5):\n",
    "    ax.scatter([sz['fr']], accuracy_df['Fixed Rate'][i], marker='${}$'.format(i), color=cmap(0), s=60)\n",
    "for i in range(5):\n",
    "    ax.scatter([sz['m4']], accuracy_df['Markov 4'][i], marker='${}$'.format(i), color=cmap(1), s=60)\n",
    "for i in range(5):\n",
    "    ax.scatter([sz['m8']], accuracy_df['Markov 8'][i], marker='${}$'.format(i), color=cmap(2), s=60)\n",
    "ax.scatter(1,1,  color=cmap(3))\n",
    "ax.set_xscale('log')\n",
    "\n",
    "\n",
    "labs=np.float64([24/bs, (3 * (4 * 4) * 8)/bs, (3 * (8 * 8) * 8)/bs, 1])*100\n",
    "ax.get_xaxis().set_ticks([24/bs, (3 * (4 * 4) * 8)/bs, (3 * (8 * 8) * 8)/bs, 1])\n",
    "ax.set_xticklabels(['{:.2f}%'.format(labs[i]) for i in range(4)])\n",
    "\n",
    "ax.get_yaxis().set_ticks([.4,.6, .8, 1])\n",
    "\n",
    "ax.set_xlabel('Model Size (% of Base Cache)')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_ylim(0,1.05)\n",
    "ax.set_xlim(0,1.5)\n",
    "ax.text(x=labs[0]/100, y=.05, s='Fixed Rate', rotation=90, color='grey')\n",
    "ax.text(x=labs[1]/100, y=.05, s='Markov 4', rotation=90, color='grey')\n",
    "ax.text(x=labs[2]/100, y=.05, s='Markov 8', rotation=90, color='grey')\n",
    "ax.text(x=labs[3]/100, y=.05, s='Base', rotation=90, color='grey')\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "ax.set_title('Points are Phase Numbers', size=10)\n",
    "fig.suptitle('Accuracy as a function of Model Size')\n",
    "\n",
    "#plt.savefig('plots/acc_vs_modelsize.svg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## Locality Analysis\n",
       "\n",
       "Finally, we get to the hardest plot to generate, the locality plot. \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%markdown\n",
    "\n",
    "## Locality Analysis\n",
    "\n",
    "Finally, we get to the hardest plot to generate, the locality plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def index(array, item):\n",
    "    for idx, val in enumerate(array):\n",
    "        if val == item:\n",
    "            return idx\n",
    "    return -1\n",
    "\n",
    "def rightshift(val: np.uint64, shift: np.uint64) -> np.uint64:\n",
    "    return val >> shift\n",
    "\n",
    "def reuse(trace, shift, outfile=None):\n",
    "    stack = np.array([], dtype=np.int64)\n",
    "    out = []\n",
    "    for i in tqdm(range(len(trace))):\n",
    "        addr = rightshift(trace[i], np.uint64(shift)) # Cache line\n",
    "        idx = index(stack, addr)\n",
    "        if idx == -1:\n",
    "            stack = np.insert(stack, 0, addr)\n",
    "        else:\n",
    "            out.append(idx)\n",
    "            stack = np.delete(stack, idx)\n",
    "            stack = np.insert(stack, 0, addr)\n",
    "\n",
    "    if outfile is not None:\n",
    "        save_object(out, outfile)\n",
    "        print('Wrote reuse trace to: {}'.format(outfile))\n",
    "        \n",
    "    return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26515/26515 [00:07<00:00, 3328.31it/s]\n",
      "100%|██████████| 27694/27694 [00:02<00:00, 10488.76it/s]\n",
      "100%|██████████| 26426/26426 [00:02<00:00, 9462.64it/s] \n",
      "100%|██████████| 26080/26080 [00:03<00:00, 7609.16it/s]\n",
      "100%|██████████| 4826/4826 [00:02<00:00, 2337.25it/s]\n",
      "0it [00:00, ?it/s]\n",
      "100%|██████████| 10253/10253 [00:01<00:00, 6556.16it/s]\n",
      "100%|██████████| 10851/10851 [00:01<00:00, 6900.54it/s]\n",
      "100%|██████████| 38604/38604 [00:44<00:00, 863.72it/s] \n",
      "100%|██████████| 39157/39157 [00:15<00:00, 2585.33it/s]\n",
      "100%|██████████| 39890/39890 [00:15<00:00, 2531.24it/s]\n",
      "100%|██████████| 39154/39154 [00:40<00:00, 970.48it/s] \n",
      "100%|██████████| 27781/27781 [00:05<00:00, 4956.51it/s]\n",
      "100%|██████████| 29881/29881 [00:04<00:00, 6106.55it/s]\n",
      "100%|██████████| 29946/29946 [00:05<00:00, 5900.33it/s]\n",
      "100%|██████████| 30012/30012 [00:05<00:00, 5731.53it/s]\n",
      "100%|██████████| 33025/33025 [00:02<00:00, 15125.95it/s]\n",
      "100%|██████████| 33649/33649 [00:01<00:00, 27364.02it/s]\n",
      "100%|██████████| 32104/32104 [00:01<00:00, 21853.44it/s]\n",
      "100%|██████████| 36357/36357 [00:02<00:00, 17691.74it/s]\n"
     ]
    }
   ],
   "source": [
    "reuse_perphase_base    = {}\n",
    "reuse_perphase_approx  = {}\n",
    "reuse_perphase_markov4 = {}\n",
    "reuse_perphase_markov8 = {}\n",
    "gran=6\n",
    "\n",
    "def get_addrs(data, phase):\n",
    "    df = pd.DataFrame(data)\n",
    "    return np.array(df[df['phase']==phase]['addr'], dtype=np.uint64)\n",
    "\n",
    "for phase in range(0,5):\n",
    "    reuse_perphase_base[phase] = reuse(get_addrs(stats['base'].cache_trace[1], phase), gran)\n",
    "    reuse_perphase_approx[phase] = reuse(get_addrs(stats['fr'].cache_trace[1], phase), gran)\n",
    "    reuse_perphase_markov4[phase] = reuse(get_addrs(stats['m4'].cache_trace[1], phase), gran)\n",
    "    reuse_perphase_markov8[phase] = reuse(get_addrs(stats['m8'].cache_trace[1], phase), gran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe4AAAHSCAYAAAAqryiAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAYjUlEQVR4nO3dTahdZ70G8Odv0lDBcmNMKSEtWFGQIlhrKREcFYSQSTtw4MggQrkzHRbEgejAKigITgIFIwj189KgWKjSqbFB20obbVMntlSLXOsHiGJ57+CsXA7HE8/XPmvv/zm/H2zOOu/eK+ftk9Xz9F1r7d0aYwQA6OEty54AALB9ihsAGlHcANCI4gaARhQ3ADSiuAGgkaPLnkAS70fbudrj/jLfub1kLu+dc4zPS97z23XmVtwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI4obABpR3ADQiOIGgEYUNwA0orgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANLJlcVfVzVX186p6tqqer6rPTeN3VtXlqrpWVd+uqmPT+Deq6qP7PXEAOIy2s+L+R5L7xxjvT3J3krNVdSbJI0m+OsZ4d5I/Jfnk/k0TAEi2Udxjzd+mb2+aHiPJ/Um+N41fTPLgxn2r6vPTCvzIguYLAIfatq5xV9WRqnomyetJnkzycpI3xhj/ml7ySpLTG/b5cpJbk3xijPHmhuceqqorVXXlwoULe/1nAIBD4+h2XjQV791VdTzJ/yR57xa7fDbJ5THGQzf48y4kud7YY5tzBYBDb0d3lY8x3kjyVJIPJTleVdeL//Ykr6576dNJPlhVJxYySwAgyfbuKr91Wmmnqt6a5CNJrmatwK/fPX4+yePrdnsiyReT/KiqblnojAHgENvOqfJTSS5ON5i9Jcl3xhg/rKoXkjxWVV9I8sskj67faYzx3am0L1XVuTHG3xc9eQA4bGqMpV9iXvoEGqo97i/zndtL5vLeOcf4vOQ9v11n7pPTAKARxQ0AjShuAGhEcQNAI4obABpR3ADQiOIGgEYUNwA0orgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgkaPLngAAB19VbTo+xph5Jv1ZcQNAI4obABpR3ADQiOIGgEYUNwA0orgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARvxPRoADzf/cgoPGihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBopM37uL0Xk4Nis2PZcQxsV5viBmD13WiRxeI4VQ4AjShuAGhEcQNAI+2vcbvRB4DDxIobABpR3ADQiOIGgEYUNwA0snI3p3nzPsDh4VMxd86KGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0Ajazc28GYl7diAPRyIItbGe2dDDnoHON0Vcs+SKvqiSQn1w2dTPLHJU2ni5vHGO/b7c4bMpf31k4m+fUY4+xudnaM74pjfF7yntfefqcsu7g3qqorY4x7lz2PVbbIjOS9tUVnJPOtOcbnJe957TUjN6cBQCOKGwAaWcXivrDsCTSwyIzkvbVFZyTzrTnG5yXvee0po5W7xg0A3NgqrrgBgBtQ3ADQyMoUd1WdrarfVNW1qnp42fNZlqq6o6qeqqoXqur5qvrUNH6iqp6sqpemr2+fxquqvjbl9lxV3bODnyXzzJe5vNc4xucl7/nte+ZjjKU/khxJ8nKSdyU5luTZJHcte15LyuJUknum7VuSvJjkriRfSvLwNP5wkkem7XNJfpykkpxJclnmq5e5vOfNW+byPsiZr8qK+74k18YYvx1j/DPJY0keWPKclmKM8doY4xfT9l+TXE1yOmt5XJxedjHJg9P2A0m+Odb8LMnxqjq1jR8l88lMmct74hifl7znt9+Zr0pxn07yu3XfvzKNHWpV9c4kH0hyOcltY4zXpqd+n+S2aXu32cl8E/uYubw34Rifl7zntx+Zr0pxs0FVvS3J95N8eozxl/XPjbVzK97Ht2Ayn5e85yXv+e1X5qtS3K8muWPd97dPY4dSVd2Utb/sb40xfjAN/+H6qZPp6+vT+G6zk/k6M2Qu73Uc4/OS9/z2M/NVKe6nk7ynqu6sqmNJPpbk0pLntBRVVUkeTXJ1jPGVdU9dSnJ+2j6f5PF14x+f7ko8k+TP607F/Ccyn8yUubwnjvF5yXt++575su++u/7I2l11L2btrsTPLHs+S8zhw1k7ffJckmemx7kk70jy0yQvJflJkhPT6yvJ16fcfpXkXpmvZubynjdvmcv7oGbuI08BoJFVOVUOAGyD4gaARo4uewLxFoTdqD3uL/Od20vm8t45x/i85D2/XWduxQ0AjShuAGhEcQNAI4obABpR3ADQiOIGgEYUNwA0orgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI1sWd1XdXFU/r6pnq+r5qvrcNH5nVV2uqmtV9e2qOjaNf6OqPrrfEweAw2g7K+5/JLl/jPH+JHcnOVtVZ5I8kuSrY4x3J/lTkk/u3zQBgGQbxT3W/G369qbpMZLcn+R70/jFJA9u3LeqPj+twI8saL4AcKht6xp3VR2pqmeSvJ7kySQvJ3ljjPGv6SWvJDm9YZ8vJ7k1ySfGGG8ubsoAcHhtq7jHGG+OMe5OcnuS+5K8d4tdPpvkv8YY/z3GGBufrKqHqupKVV25cOHCjicNAIfV0Z28eIzxRlU9leRDSY5X1dFp1X17klfXvfTpJB+sqhNjjP/d5M+5kOR6Y/9bsQMAm9vOXeW3VtXxafutST6S5GqSp5Jcv3v8fJLH1+32RJIvJvlRVd2y0BkDsLKqatMHi7OdFfepJBenG8zekuQ7Y4wfVtULSR6rqi8k+WWSR9fvNMb47lTal6rq3Bjj74uePAAcNrXJJei5LX0CDe31P19lvnN7yVzeO+cYn9fC8r7R6noFumbV7Dpzn5wGAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGdvTJacDebfZ2GW+VAbbLihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0MjKfeTpZh8HmfhISABIrLgBoBXFDQCNKG4AaERxA0AjihsAGlm5u8oBFsk7VThorLgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI4obABo5uuwJAD1U1abjY4yZZwKHmxU3ADSiuAGgEafKAQ4wlzgOnjbF7eADgEbFDUBfFl+L4xo3ADRixQ1wANxoRcvBY8UNAI0obgBopP2p8s1OD7nZAXbPKVdYbe2LG4C+3G2+cweyuB0IdOOYnZ/M6aqWfZBW1RNJTq4bOpnkj0uaThc3jzHet9udN2Qu762dTPLrMcbZ3ezsGN8Vx/i85D2vvf1OWXZxb1RVV8YY9y57HqtskRnJe2uLzkjmW3OMz0ve89prRu4qB4BGFDcANLKKxX1h2RNoYJEZyXtri85I5ltzjM9L3vPaU0Yrd40bALixVVxxAwA3sDLFXVVnq+o3VXWtqh5e9nyWparuqKqnquqFqnq+qj41jZ+oqier6qXp69un8aqqr025PVdV92zz58g78+U97SvzyHxu8p7fvmc+xlj6I8mRJC8neVeSY0meTXLXsue1pCxOJbln2r4lyYtJ7krypSQPT+MPJ3lk2j6X5MdJKsmZJJflvVp5y1zm8j5cj/3OfFVW3PcluTbG+O0Y459JHkvywJLntBRjjNfGGL+Ytv+a5GqS01nL4+L0sotJHpy2H0jyzbHmZ0mOV9WpLX6MvCcz5Z3I/P/JfF7ynt9+Z74qxX06ye/Wff/KNHaoVdU7k3wgyeUkt40xXpue+n2S26bt3WQn703sY9572e9Ak/m85D2//ch8VYqbDarqbUm+n+TTY4y/rH9urJ1b8XaABZL3/GQ+L3nPb78yX5XifjXJHeu+v30aO5Sq6qas/WV/a4zxg2n4D9dPnUxfX5/Gd5OdvNeZIe+97HcgyXxe8p7ffma+KsX9dJL3VNWdVXUsyceSXFrynJaiqirJo0mujjG+su6pS0nOT9vnkzy+bvzj012JZ5L8ed2pmBuR92SmvBOZ/z+Zz0ve89v3zJd99931R9buqnsxa3clfmbZ81liDh/O2umT55I8Mz3OJXlHkp8meSnJT5KcmF5fSb4+5farJPfKe/XylrnM5X14HvuduU9OA4BGVuVUOQCwDYobABpR3ADQyNFlTyDeO7gbtcf9Zb5ze8lc3jvnGJ+XvOe368ytuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI4obABpR3ADQiOIGgEYUNwA0orgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoZMvirqqbq+rnVfVsVT1fVZ+bxu+sqstVda2qvl1Vx6bxb1TVR/d74gBwGG1nxf2PJPePMd6f5O4kZ6vqTJJHknx1jPHuJH9K8sn9myYAkGyjuMeav03f3jQ9RpL7k3xvGr+Y5MGN+1bV56cV+JEFzRcADrVtXeOuqiNV9UyS15M8meTlJG+MMf41veSVJKc37PPlJLcm+cQY480Nzz1UVVeq6sqFCxf2+s8AAIfG0e28aCreu6vqeJL/SfLeLXb5bJLLY4yHbvDnXUhyvbHHNucKAIfeju4qH2O8keSpJB9Kcryqrhf/7UleXffSp5N8sKpOLGSWAECS7d1Vfuu00k5VvTXJR5JczVqBX797/HySx9ft9kSSLyb5UVXdstAZA8Ahtp1T5aeSXJxuMHtLku+MMX5YVS8keayqvpDkl0keXb/TGOO7U2lfqqpzY4y/L3ryAHDY1BhLv8S89Ak0VHvcX+Y7t5fM5b1zjvF5yXt+u87cJ6cBQCOKGwAaUdwA0IjiBoBGFDcANKK4AaCRbX3kKbA4Vf/+LpAVeFsm0IQVNwA0orgBoBHFDQCNuMYNwMJsdg9H4j6ORbLiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI4obABpR3ADQiOIGgEZW7pPTfOoOANyYFTcANKK4AaCRlTtVDrBILr9x0FhxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGjk6LInAMD+qapNx8cYM8+ERVHch5x/qYE5+F2zOE6VA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgkTZvB/NWAlgu/w7CarDiBoBG2qy4gdVkJQ7zsuIGgEYUNwA0orgBoJH217g3u77m2hpAD+6R2DkrbgBopP2Km/3hv4I56BzjdFXLPkir6okkJ9cNnUzyxyVNp4ubxxjv2+3OGzKX99ZOJvn1GOPsbnZ2jO+KY3xe8p7X3n6nLLu4N6qqK2OMe5c9j1W2yIzkvbVFZyTzrTnG5yXvee01I9e4AaARxQ0AjaxicV9Y9gQaWGRG8t7aojOS+dYc4/OS97z2lNHKXeMGAG5sFVfcAMANKG4AaGRliruqzlbVb6rqWlU9vOz5LEtV3VFVT1XVC1X1fFV9aho/UVVPVtVL09e3T+NVVV+bcnuuqu7Zwc+SeebLXN5rHOPzkvf89j3zMcbSH0mOJHk5ybuSHEvybJK7lj2vJWVxKsk90/YtSV5McleSLyV5eBp/OMkj0/a5JD9OUknOJLks89XLXN7z5i1zeR/kzFdlxX1fkmtjjN+OMf6Z5LEkDyx5TksxxnhtjPGLafuvSa4mOZ21PC5OL7uY5MFp+4Ek3xxrfpbkeFWd2saPkvlkpszlPXGMz0ve89vvzFeluE8n+d2671+Zxg61qnpnkg8kuZzktjHGa9NTv09y27S92+xkvol9zFzem3CMz0ve89uPzFeluNmgqt6W5PtJPj3G+Mv658bauRXv41swmc9L3vOS9/z2K/NVKe5Xk9yx7vvbp7FDqapuytpf9rfGGD+Yhv9w/dTJ9PX1aXy32cl8nRkyl/c6jvF5yXt++5n5qhT300neU1V3VtWxJB9LcmnJc1qKqqokjya5Osb4yrqnLiU5P22fT/L4uvGPT3clnkny53WnYv4TmU9mylzeE8f4vOQ9v33PfNl3311/ZO2uuhezdlfiZ5Y9nyXm8OGsnT55Lskz0+Ncknck+WmSl5L8JMmJ6fWV5OtTbr9Kcq/MVzNzec+bt8zlfVAz95GnANDIqpwqBwC2QXEDQCNHlz2BeAvCbtQe95f5zu0lc3nvnGN8XvKe364zt+IGgEYUNwA0orgBoBHFDQCNKG4AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI4obABpR3ADQiOIGgEYUNwA0orgBoJEti7uqbq6qn1fVs1X1fFV9bhq/s6ouV9W1qvp2VR2bxr9RVR/d74kDwGG0nRX3P5LcP8Z4f5K7k5ytqjNJHkny1THGu5P8Kckn92+aAECyjeIea/42fXvT9BhJ7k/yvWn8YpIHN+5bVZ+fVuBHFjRfADjUtnWNu6qOVNUzSV5P8mSSl5O8Mcb41/SSV5Kc3rDPl5PcmuQTY4w3FzdlADi8tlXcY4w3xxh3J7k9yX1J3rvFLp9N8l9jjP8eY4yNT1bVQ1V1paquXLhwYceTBoDD6uhOXjzGeKOqnkryoSTHq+rotOq+Pcmr6176dJIPVtWJMcb/bvLnXEhyvbH/rdgBgM1t567yW6vq+LT91iQfSXI1yVNJrt89fj7J4+t2eyLJF5P8qKpuWeiMAeAQ286K+1SSi9MNZm9J8p0xxg+r6oUkj1XVF5L8Msmj63caY3x3Ku1LVXVujPH3RU8eAA6b2uQS9NyWPoGGao/7y3zn9pK5vHfOMT4vec9v15n75DQAaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBoRHEDQCOKGwAaUdwA0IjiBoBGFDcANKK4AaARxQ0AjShuAGhEcQNAI4obABpR3ADQyNFlTwCAg6OqNh0fY8w8k4PLihsAGlHcANCI4gaARlzjBg4011w5aBQ3APvOf0AtjlPlANCI4gaARhQ3ADSiuAGgkZW7Oc0NDABwY1bcANCI4gaARhQ3ADSycte44aDb7D4O93AA22XFDQCNKG4AaERxA0AjihsAGlHcANCIu8oPOZ9UB9CLFTcANKK4AaCRNqfKndIF2Dm/Ow8eK24AaERxA0AjihsAGlHcANCI4gaARhQ3ADSiuAGgEcUNAI0obgBopM0np93IZp8K5BOBAHrwyW47Z8UNAI0obgBopP2pcoDdcIqWrg5kcXf+F3JV5r4q89hvq/LPuSrzAFZfLfsXQ1U9keTkuqGTSf64pOl0cfMY43273XlD5vLe2skkvx5jnN3Nzo7xXXGMz0ve89rb75RlF/dGVXVljHHvsuexyhaZkby3tuiMZL41x/i85D2vvWbk5jQAaERxA0Ajq1jcF5Y9gQYWmZG8t7bojGS+Ncf4vOQ9rz1ltHLXuAGAG1vFFTcAcAMrU9xVdbaqflNV16rq4WXPZ1mq6o6qeqqqXqiq56vqU9P4iap6sqpemr6+fRqvqvralNtzVXXPDn6WzDNf5vJe4xifl7znt++ZjzGW/khyJMnLSd6V5FiSZ5Pctex5LSmLU0numbZvSfJikruSfCnJw9P4w0kembbPJflxkkpyJsllma9e5vKeN2+Zy/sgZ74qK+77klwbY/x2jPHPJI8leWDJc1qKMcZrY4xfTNt/TXI1yems5XFxetnFJA9O2w8k+eZY87Mkx6vq1DZ+lMwnM2Uu74ljfF7ynt9+Z74qxX06ye/Wff/KNHaoVdU7k3wgyeUkt40xXpue+n2S26bt3WYn803sY+by3oRjfF7ynt9+ZL4qxc0GVfW2JN9P8ukxxl/WPzfWzq14O8CCyXxe8p6XvOe3X5mvSnG/muSOdd/fPo0dSlV1U9b+sr81xvjBNPyH66dOpq+vT+O7zU7m68yQubzXcYzPS97z28/MV6W4n07ynqq6s6qOJflYkktLntNSVFUleTTJ1THGV9Y9dSnJ+Wn7fJLH141/fLor8UySP687FfOfyHwyU+bynjjG5yXv+e175su+++76I2t31b2YtbsSP7Ps+Swxhw9n7fTJc0memR7nkrwjyU+TvJTkJ0lOTK+vJF+fcvtVkntlvpqZy3vevGUu74OauU9OA4BGVuVUOQCwDYobABpR3ADQiOIGgEYUNwA0orgBoBHFDQCNKG4AaOT/AFP6Dpyy0ZBEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "binwidth=16\n",
    "#maxbin={1:50, 2:850, 3:225, 4:50}\n",
    "maxbin={0:200,1:200, 2:200, 3:200, 4:200}\n",
    "\n",
    "yl=45000\n",
    "ylim={0:yl,1:yl, 2:yl, 3:yl, 4:yl}\n",
    "\n",
    "\n",
    "def bins(data, binwidth, maxbin):\n",
    "    #return range(min(data), min(maxbin, max(data) + binwidth), binwidth)\n",
    "   # print(data)\n",
    "    return range(min(data), maxbin, binwidth)\n",
    "\n",
    "def plot(ax, data, title, phase):\n",
    "    if not data:\n",
    "        data = [1]\n",
    "    ax.hist(data, bins=bins(data, binwidth, maxbin=maxbin[phase]), color='black')\n",
    "    ax.set_ylim((0,ylim[phase]))\n",
    "    #ax.suptitle(title)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.get_yaxis().set_ticks([])\n",
    "    if phase == 0:\n",
    "        ax.get_yaxis().set_ticks([30000])\n",
    "        ax.set_yticklabels(['30k'])\n",
    "    \n",
    "#plot(reuse_perphase_base[1], 'Base Cache', maxbin=maxbin[1])\n",
    "#plot(reuse_perphase_approx[1], 'Fixed Rate Cache', maxbin=maxbin[1])\n",
    "#plot(reuse_perphase_markov4[1], '4-State Markov Cache', maxbin=maxbin[1])\n",
    "#plot(reuse_perphase_markov8[1], '8-State Markov Cache', maxbin=maxbin[1])\n",
    "\n",
    "#i = 1\n",
    "\n",
    "fig, ax = plt.subplots(4, 5, figsize=(8,8))\n",
    "for i in range(5):\n",
    "    #phase = i+1\n",
    "    phase=i\n",
    "    \n",
    "    plot(ax[0,i], reuse_perphase_base[phase], 'Base Cache', phase)\n",
    "    plot(ax[1,i], reuse_perphase_approx[phase], 'Fixed Rate Cache', phase)\n",
    "    plot(ax[2,i], reuse_perphase_markov4[phase], '4-State Markov Cache', phase)\n",
    "    plot(ax[3,i], reuse_perphase_markov8[phase], '8-State Markov Cache', phase)\n",
    "\n",
    "#plt.savefig('plots/locality_per_phase.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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