HLTperf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f6172512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "47fe248b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import math\n",
    "import re\n",
    "import numpy as np\n",
    "#import pandas as pd\n",
    "from bisect import *\n",
    "from decimal import Decimal\n",
    "#from scipy.optimize import curve_fit\n",
    "#import scipy.special as sps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ae9b6246",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseCountsNorm(di,denName):\n",
    "    do = {}\n",
    "    for wf in di :\n",
    "      den = 1.\n",
    "      if (denName!='') : den = di[wf][denName]\n",
    "      if (denName=='task-clock') : den*=1000.*1000.\n",
    "      for k in di[wf] :\n",
    "        ko = k\n",
    "        if (k=='task-clock') :\n",
    "          ko='nsec'\n",
    "        if(k=='seconds') : ko='wall-clock-ns'\n",
    "        if not ko in do : do[ko] = {}\n",
    "        do[ko][wf] = di[wf][k]/den\n",
    "        if (ko=='nsec') :  do[ko][wf]*=1000.*1000.\n",
    "        if (ko=='wall-clock-ns') :  do[ko][wf]*=1000*1000.*1000.\n",
    "    return do\n",
    "\n",
    "\n",
    "def doPrint(di) :\n",
    "  s = '|' + ' | '\n",
    "  for wf  in di['cycles'] :\n",
    "    s+= ' ' + wf + ' |'\n",
    "  s += '|'\n",
    "  print(s)\n",
    "  for k in di :\n",
    "    s = '|' + k + ' | '\n",
    "    for wf  in di[k] :\n",
    "        v = di[k][wf]\n",
    "        s+= ' ' + \"{:6.4f}\".format(v)  + ' | '\n",
    "    s += '|'\n",
    "    print(s)\n",
    "\n",
    "\n",
    "def parseCounts(fname) :\n",
    "  nop=0\n",
    "  d ={}\n",
    "  with open(fname) as f:\n",
    "    print('\\n\\n---++ '+fname)\n",
    "    for line in f:\n",
    "      try:\n",
    "        (wf, val, key) = line.split()\n",
    "        d[wf] = {}\n",
    "      except:\n",
    "        nop+=1\n",
    "  with open(fname) as f:\n",
    "    for line in f:\n",
    "      try:\n",
    "        (wf, val, key) = line.split()\n",
    "        d[wf][key]= float(val)\n",
    "      except:\n",
    "        nop+=1\n",
    "  return d\n",
    "\n",
    "def parseOne(fname) :\n",
    "  d = parseCounts(fname)\n",
    "  dr = parseCountsNorm(d,'')\n",
    "  dc = parseCountsNorm(d,'cycles')\n",
    "  di = parseCountsNorm(d,'instructions')\n",
    "  ds = parseCountsNorm(d,'task-clock')\n",
    "  return [dc,di,ds,dr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "1d705762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "---++ /Users/innocent/cernbox/HLTperf/HLTPerfCountsFullMachine\n"
     ]
    }
   ],
   "source": [
    "dir = '/Users/innocent/cernbox/HLTperf/'\n",
    "# files = [dir+\"Haswell/haswell.count\",dir+\"Skylake/skylake.count\",dir+\"Icelake/icelake.count\"]\n",
    "files = [dir+\"HLTPerfCounts\",dir+\"HLTPerfCountsHalfMachine\",dir+\"HLTPerfCountsFullMachine\"]\n",
    "scores={}\n",
    "scores['HLT']=parseOne(files[2])\n",
    "#scores['HLT'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "5b5b7ca1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FullMachine1000_MilanSEE', 'FullMachine2000_MilanSEE', 'FullMachine4000_MilanSEE', 'FullMachine400_MilanSEE', 'FullMachine8000_MilanSEE', 'FullMachineIdle_Milan']\n",
      "FullMachine1000_MilanSEE\n",
      "FullMachine2000_MilanSEE\n",
      "FullMachine4000_MilanSEE\n",
      "FullMachine400_MilanSEE\n",
      "FullMachine8000_MilanSEE\n",
      "FullMachineIdle_Milan\n"
     ]
    }
   ],
   "source": [
    "norNames = ['cycles','instructions','task-clock','']\n",
    "theONE= 'HLT'\n",
    "di = scores[theONE][0]['cycles']\n",
    "labels = []\n",
    "for wf in di : labels.append(wf)\n",
    "print(labels)\n",
    "for wf in labels :\n",
    "        if re.search(\"Milan\",wf) : print(wf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a494ef73",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def doSum(vars,nor,code) :\n",
    "    di = scores[theONE][nor]\n",
    "    ret = []\n",
    "    for wf in labels :\n",
    "        if re.search(code,wf) :\n",
    "          q = 0.\n",
    "          for var in vars : q += di[var][wf]\n",
    "          ret.append(q)\n",
    "    return ret\n",
    "def instrBD() :\n",
    "    code = \"Milan\"\n",
    "    plt.rcParams[\"figure.figsize\"] = (30,15)\n",
    "    tot = ['instructions']\n",
    "    load = ['ls_dispatch.ld_dispatch']\n",
    "    store = ['ls_dispatch.store_dispatch']\n",
    "    ref = ['cache-references']\n",
    "    fp = ['ex_ret_mmx_fp_instr.sse_instr']\n",
    "    br = ['branch-instructions']\n",
    "    intop = ['de_dis_cops_from_decoder.disp_op_type.any_integer_dispatch']\n",
    "    fpop = ['de_dis_cops_from_decoder.disp_op_type.any_fp_dispatch']\n",
    "    ll = []\n",
    "    for wf in labels :\n",
    "        if re.search(code,wf) : ll.append(wf)\n",
    "            \n",
    "    x = np.arange(len(ll))  # the label locations\n",
    "    width = 0.4  # the width of a bar\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    rects0 = ax.bar(x - width/2, doSum(load,3,code), width, label='memory load')\n",
    "    all = load\n",
    "    rects1 = ax.bar(x - width/2, doSum(store,3,code), width, label='memory store',bottom=doSum(all,3,code))\n",
    "    all += store\n",
    "    rects2 = ax.bar(x - width/2, doSum(ref,3,code), width, label='cache refs',bottom=doSum(all,3,code))\n",
    "    all += ref\n",
    "    #rects3 = ax.bar(x - width/2, doSum(br,3,code), width, label='branches',bottom=doSum(all,3,code))\n",
    "    #all += br\n",
    "    #rects4 = ax.bar(x - width/2, doSum(fp,3,code), width, label='fp',bottom=doSum(all,3,code))\n",
    "    #\n",
    "    rectsA = ax.bar(x + width/2, doSum(intop,3,code), width, label='int')\n",
    "    all = intop\n",
    "    rectsB = ax.bar(x + width/2, doSum(fp,3,code), width, label='fp',bottom=doSum(all,3,code))\n",
    "    all += fp\n",
    "    rectsC = ax.bar(x + width/2, doSum(br,3,code), width, label='branches',bottom=doSum(all,3,code))\n",
    "    all += br\n",
    "    #rectsD = ax.bar(x + width/2, doSum(store,3), width, label='memory store',bottom=doSum(all,3))\n",
    "    #all += store\n",
    "    rectsQ = ax.bar(x, doSum(tot,3,code), width/2, label='total')\n",
    "    \n",
    "\n",
    "    # ax.set_ylim(0.,1.)\n",
    "    ax.set_title('Instruction breakdown')\n",
    "    ax.set_xticks(x,ll)\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "\n",
    "instrBD()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "bd969998",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def instrFP() :\n",
    "    code = \"Genoa\"\n",
    "    plt.rcParams[\"figure.figsize\"] = (30,15)\n",
    "    \n",
    "    tot = ['instructions']\n",
    "    fp = ['fp_ops_retired_by_width.all']\n",
    "    into = ['packed_int_op_type.all']\n",
    "    scalar = ['fp_ops_retired_by_width.scalar_uops_retired']\n",
    "    w128 = ['fp_ops_retired_by_width.pack_128_uops_retired']\n",
    "    w256 = ['fp_ops_retired_by_width.pack_256_uops_retired']\n",
    "    w512 = ['fp_ops_retired_by_width.pack_512_uops_retired']\n",
    "    flops = ['fp_ret_sse_avx_ops.all'] \n",
    "    add = ['fp_ret_sse_avx_ops.add_sub_flops'] \n",
    "    mul = ['fp_ret_sse_avx_ops.mult_flops']\n",
    "    mac = ['fp_ret_sse_avx_ops.mac_flops']\n",
    "    div = ['fp_ret_sse_avx_ops.div_flops']\n",
    "    \n",
    "    ll = []\n",
    "    for wf in labels :\n",
    "        if re.search(code,wf) : ll.append(wf)\n",
    "            \n",
    "    x = np.arange(len(ll))  # the label locations\n",
    "    width = 0.4  # the width of a bar\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    #rectsQ = ax.bar(x, doSum(tot,3,code), width/2, label='total')\n",
    "    rects1 = ax.bar(x - width/2, doSum(scalar,3,code), width, label='scalar')\n",
    "    all = scalar\n",
    "    rects2 = ax.bar(x - width/2, doSum(w128,3,code), width, label='w128',bottom=doSum(all,3,code))\n",
    "    all += w128\n",
    "    rects3 = ax.bar(x - width/2, doSum(w256,3,code), width, label='w256',bottom=doSum(all,3,code))\n",
    "    all += w256\n",
    "    rects4 = ax.bar(x - width/2, doSum(w512,3,code), width, label='w512',bottom=doSum(all,3,code))\n",
    "    all += w512\n",
    "    rectsI = ax.bar(x - width/8, doSum(into,3,code), width/4, label='vec-int')\n",
    "    \n",
    "    #\n",
    "    rectsA = ax.bar(x + width/2, doSum(add,3,code), width, label='add-sub')\n",
    "    all = add\n",
    "    rectsB = ax.bar(x + width/2, doSum(mul,3,code), width, label='mul',bottom=doSum(all,3,code))\n",
    "    all += mul\n",
    "    rectsC = ax.bar(x + width/2, doSum(mac,3,code), width, label='mac',bottom=doSum(all,3,code))\n",
    "    all += mac\n",
    "    rectsD = ax.bar(x + width/2, doSum(div,3,code), width, label='div',bottom=doSum(all,3,code))\n",
    "    all += div\n",
    "    #rectsP = ax.bar(x + width/8, doSum(flops,3,code), width/4, label='tot flops')\n",
    "    #rectsI = ax.bar(x + width/8, doSum(into,3,code), width/4, label='int',bottom=doSum(flops,3,code))\n",
    "   \n",
    "\n",
    "    # ax.set_ylim(0.,1.)\n",
    "    ax.set_title('FP Instruction breakdown')\n",
    "    ax.set_xticks(x,ll)\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "\n",
    "instrFP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "dd6d5019",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotOne(var,nor) :\n",
    "    den =1 \n",
    "    global iden\n",
    "    norName = norNames[nor]\n",
    "    plt.rcParams[\"figure.figsize\"] = (30,10)\n",
    "    lu = []\n",
    "    di = scores[theONE][nor][var]\n",
    "    if iden != '' : den = di[iden]\n",
    "    for wf in labels : lu.append(di[wf]/den)\n",
    "  \n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.4  # the width of a bar\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    rects2 = ax.bar(x - width/2, lu, width, label='HLT')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    label = var\n",
    "    if (len(norName)>0) : label = var+'/'+norName\n",
    "    ax.set_ylabel(label)\n",
    "    ax.set_title(label)\n",
    "    ax.set_xticks(x,labels)\n",
    "    ax.legend()\n",
    "    ax.bar_label(rects2, padding=3)\n",
    "\n",
    "\n",
    "    # fig.tight_layout()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "def plotSum(vars,nor,title) :\n",
    "    norName = norNames[nor]\n",
    "    plt.rcParams[\"figure.figsize\"] = (30,10)\n",
    "    lu = []\n",
    "    di = scores[theONE][nor]\n",
    "    for wf in labels :\n",
    "        q = 0.\n",
    "        for var in vars : q += di[var][wf]\n",
    "        lu.append(q)\n",
    "  \n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.4  # the width of a bar\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    rects2 = ax.bar(x - width/2, lu, width, label='LU on Milan')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel(title+'/'+norName)\n",
    "    ax.set_title(title)\n",
    "    ax.set_xticks(x,labels)\n",
    "    ax.legend()\n",
    "    ax.bar_label(rects2, padding=3)\n",
    "\n",
    "\n",
    "    # fig.tight_layout()\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "8bd82606",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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3UravJB+oqi8kube//WFD1r+7ql47ZPll/X9ftEg/80+ttfMeQ53wROhvstT+Zkk13Jnkn5MF0xMvzfFJvlZVf0ry3+mNIFuSDZL8W1U9Ob33Ltdn4emCzq6qe/uvb22tzZ/u7BH9TWtt1nAKhGVkVe1b3pTk5Koakd4op/l/j97LeC/DymFV7Zvmb/OX/nkv6H/u8aMkGy7mcO9M8pmquqp/nB8mecswahhWP9W32HvTFfXwyDAAAAAAAADoFtMMAgAAAAAA0FmmGYSVWFUdk+SVizR/rbU23GfXACyRfgYYtKr6TJIXLNJ8cmvtKyuiHmDlV1WXpTcN81Cva61dvSLqAVZt3ssAK0pVHZ7kHxZp/klr7e0rop5BM80gAAAAAAAAnWWaQQAAAAAAADpLmAUAAAAAAEBnCbMAAAAAAADoLGEWAAAAAAAAnfX/A/crH4h7/LhLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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wLb3r8+wkz0zyqmVsX0n+uar2TDI/vTdmo2n7da21Gf3nl6Q/fL219oGq+mp604K8LL3/SOzV325xU4ElpgJj+Fa7fqaq3pXet42++iDP88HSz7A6Wi36mKp6eJJ/SO9vb7ws7foszppJTqiqyUnmJXn8mHWLO/f/aq39bVU9KcmzkhyZ3pSsh/f3WdL0X6boYWXS56xYc5LcUlWHJLkqyZ0D7DMxyder943qtZJcN2bdX1yH/pRfuyV5Wnpf4Pl6VR3TWju5v48pB+mC1aJv6W//hiRva62dXlUvSXJSev/dX1m8n4HBrU5906gnpPd5yK/6+34lyWvzl56V3kix0dfrV9W6rbWl3bN8WpI3pzc69e/Te6+1LIu7NnOS3JtkdCT7Jen1MyuVQIsuuS/3jxpcZ2kbLuLr6f0BndJamz/6B11Ve6X3Rz61tXZn9YaiLuu494x5Pi+L/xtZ3DaV5IrW2tQHUPeN6XUIoybm/nlcF7d8TnpD30f6oyfGbr8030ny4STTW2u31ZLvVTrq5el13ru11uZW1czcf90WPffRURBprf1vkk9X1eeTzB6T3EOXrLL9TFUdnt59Gp7Zn/oi0c/AyrbK9TH9D0W2SfLzfl0Tk1xavRupL6mPuTH3B86jy8/rL5+4mO2XZbHXZyneluT3SXZO7+dx95h1S7w+rbVfJPlFVX05vQ+qDx+gNhhP+pyl9znL8vX07il6+ADbJr1vVn+0tXZm/1q9b8y6xV6H1tq8fi3nVdUvkhyW5OQB24Pxssr1LX2HJXlL//k3cv+oBu9n4KFhVe2bHqg10hsxfvcyt+xrrV3Uf491Z2vtl8vqf5ZxbeaO+cxpSddgqEw5SJfMTG9IZXL/FFPL1Fq7Psm7knxqkVUbJLml/4f3hCRP6S+/IMmeVbVNklTvRp/L65r0hrBO7R9zzaracRn7nJnkkKpau1/L45JclOTiJI+rqm2qaq30vkVwZr+z+HGSg/v7H5bkW8sqrLV2Z5Kjs+wpEEdtkOQP/Q+Zn5Fkq2XtUFX7Vy00Xdi89L59AF0zM6tgP1NV+6U37c0B/b/5UfoZWLlmZhXrY1prv2itbdpa27q1tnV630DctbX2u/T6mFdWz1OS/Km19tskZyfZt6oeWb3pPfZNcnZ/3W1V9ZT+3/MrM1gfs6TrsyQbJPlta21+klek9w3IJareff32GrNocpLrB2wLxtPM6HOW2OcMUMM3k/zLgNsmvesz+qH1YcvauKq2r6rHjVk0OfoWHhpmZhXrW/rrbkoyOvXn3kl+1X/u/Qw8NMzMqtk3jbo6ydZ1/7Sfhy7hWD9I8n9HX1RvFOcgjslgI7OSJV+bTjBCiy55f5KTqur/ZbBv1C3QWvvsYhafleT1VXVVeh3HBf1tZ1fvZnZnVG8+4T9kOYdHttburaqDk3yiqjZI72/r40muqN7wzE+mNxrhu1U1o7X27NbaFVX170muTO9bBm/sf4MvVfWm9N48TUjyb621K/pNHZ1kWlV9MMnP0hsiP0h9g8zPPOqrSb7d/wbh9PQ61GV5RZKPVdWd/XN5eWttXv+z55dW1V+P2fbv0nsj+djqDb0d9W+ttbFzQsMwrJL9TJITkqyd+4evX9Bae71+Rj/DSreq9jFL8r0kz01ybXrTdb2qf6w/9q/Bxf3tPjBmqo6/S290wsPSu9/f9zOAJVyfJflUktOr6pXpXcM/L2P7SvKOqvpskrv62x8+Zv3bqupvxrw+sP/v0xbpYz7YWjvtAdQJy0ufk2X2OUur4fYkxyULpipelvcl+UZV3ZLk3PRGki3Nukk+WVUbpvfe5dosPHXQV6vqrv7zm1tro1Of/UWf01qbOUiBsIKsqn3La5IcX1Uj6Y12Gv179H7G+xkeGlbVvml0m7v77X63/7nH+UnWW8zh3pzkxKq6rH+c/0zy+gFqGKif6lvstemKun+EGAAAAAAAAHSPKQcBAAAAAADoNFMOwkNcVb0ryYsXWfyN1tqg97IBWCr9DDBMVXVikqcusvj41toXx6MeYNVQVRemNyXzWK9orf1iPOoBVm3ezwDjpapeleQtiyz+aWvtjeNRz7CZchAAAAAAAIBOM+UgAAAAAAAAnSbQAgAAAAAAoNMEWgAAAAAAAHSaQAsAAAAAAIBO+/8iYw6+QCjy1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iden = ''\n",
    "#plotOne('duration',3)\n",
    "plotOne('wall-clock-ns',3)\n",
    "plotOne('wall-clock-ns',2)\n",
    "plotOne('cycles',2)\n",
    "plotOne('stalled-cycles-frontend',0)\n",
    "plotOne('stalled-cycles-backend',0)\n",
    "plotOne('instructions',0)\n",
    "iden = 'FullMachine1000_MilanSEE'\n",
    "plotOne('wall-clock-ns',3)\n",
    "plotOne('instructions',3)\n",
    "plotOne('cycles',3)\n",
    "plotOne('stalled-cycles-frontend',3)\n",
    "plotOne('stalled-cycles-backend',3)\n",
    "#plotOne('ex_ret_mmx_fp_instr.sse_instr',3)\n",
    "plotOne('fp_ret_sse_avx_ops.all',3)\n",
    "plotOne('fp_ret_sse_avx_ops.mac_flops',3)\n",
    "#plotOne('all_data_cache_accesses',3)\n",
    "plotOne('cache-references',3)\n",
    "plotOne('cache-misses',3)\n",
    "plotOne('L1-dcache-loads',3)\n",
    "plotOne('L1-icache-loads',3)\n",
    "plotOne('L1-dcache-load-misses',3)\n",
    "plotOne('L1-icache-load-misses',3)\n",
    "plotOne('l2_cache_misses_from_dc_misses',3)\n",
    "#plotOne('l3_cache_accesses',3)\n",
    "#plotOne('l3_misses',3)\n",
    "plotOne('iTLB-loads',3)\n",
    "plotOne('iTLB-load-misses',3)\n",
    "# plotOne('branches',3)\n",
    "plotOne('branch-instructions',3)\n",
    "plotOne('branch-misses',3)\n",
    "plotOne('ex_div_busy',3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97e7da84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}