ipdps_chart_generator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5bd61e9c-6f0a-4dbe-b1b6-40608ebcaf5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ba27e56e-42ad-4479-b610-a056ccb55b25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getTotalTime(exp):\n",
    "    ops = 2 * exp['alg_info']['nnz'] * 2 * exp['alg_info']['r'] * exp['num_trials']\n",
    "    return ops / (exp['overall_throughput'] * 1e9)\n",
    "\n",
    "def get_best_cval_exps(exps, alg_name, fused):    \n",
    "    filtered = [exp for exp in exps if exp['alg_name']==alg_name and exp['fused'] == fused]\n",
    "    pValues = [exp['alg_info']['p'] for exp in filtered]\n",
    "    pValues = sorted(list(dict.fromkeys(pValues)))\n",
    "    \n",
    "    res = []\n",
    "    \n",
    "    for p in pValues:\n",
    "        sexps = sorted([exp for exp in filtered if exp['alg_info']['p'] == p], key=getTotalTime)\n",
    "        res.append(sexps[0])\n",
    "\n",
    "    return res\n",
    "\n",
    "def getExps(filename):\n",
    "    f = open('distributed_sddmm/{}'.format(filename), 'r')\n",
    "    return json.load(f)\n",
    "\n",
    "def getExps_progress(filename):\n",
    "    f = open('distributed_sddmm/{}'.format(filename), 'r')\n",
    "    lines = '\\n'.join(f.readlines())\n",
    "    lines = \"[\" + lines[:-1].rstrip(',') + \"]\"\n",
    "    return json.loads(lines)\n",
    "\n",
    "def generateScalingPlot(results, alg_name, fused, label, cm, ax, fil=0):\n",
    "    color, marker = cm\n",
    "    best_c_exps = get_best_cval_exps(results, alg_name, fused)\n",
    "    xVals = [exp['alg_info']['p'] for exp in best_c_exps if exp['alg_info']['p'] > fil]\n",
    "    yVals = [getTotalTime(exp) for exp in best_c_exps if exp['alg_info']['p'] > fil]\n",
    "    ax.plot(xVals, yVals, marker=marker, color=color, label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7491f38f-d049-4579-8eb0-ff2415703378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def getBreakdowns(exps, extra=False):\n",
    "    sCategories = {'Replication Time' : 'Replication', 'Cyclic Shift Time' : 'Propagation', 'Computation Time' : 'Computation', \\\n",
    "                   'Dense Cyclic Shift Time' : 'Propagation', 'Sparse Cyclic Shift Time' : 'Propagation', \\\n",
    "                   'Dense Fiber Communication Time' : 'Replication', 'Sparse Fiber Communication Time' : 'Replication', 'Setup Shift Time' : 'Outside'}\n",
    "    stups = []\n",
    "    for exp in exps:\n",
    "        m = {'Replication' : 0, 'Propagation' : 0, 'Computation' : 0, 'Outside' : 0}\n",
    "        for key, value in exp['perf_stats'].items():\n",
    "            m[sCategories[key]] += value\n",
    "            \n",
    "        if extra:\n",
    "            m['Outside'] += exp['application_communication_time']\n",
    "            \n",
    "        stups.append((exp['alg_info']['p'], m))\n",
    "        \n",
    "    labels = [str(x[0]) for x in stups]\n",
    "    replications = np.array([x[1]['Replication'] for x in stups])\n",
    "    propagations = np.array([x[1]['Propagation'] for x in stups])\n",
    "    computations = np.array([x[1]['Computation'] for x in stups])\n",
    "    outside = np.array([x[1]['Outside'] for x in stups])\n",
    "    \n",
    "    if extra:\n",
    "        return labels, replications, propagations, computations, outside\n",
    "    else:\n",
    "        return labels, replications, propagations, computations\n",
    "\n",
    "def createBreakdownplot(data, alg_name, fused, ax, offset1, wparam, alg, verbose=False, labelMarker=False, labelColor=False):\n",
    "    exps = get_best_cval_exps(data, alg_name, fused)[1:]\n",
    "    labels, replications, propagations, computations=getBreakdowns(exps)\n",
    "    width = wparam / len(labels)\n",
    "    \n",
    "    x = np.linspace(offset1, offset1 + wparam, num=len(labels), endpoint=True)\n",
    "    \n",
    "    ax.grid(True)\n",
    "    if labelColor:\n",
    "        ax.bar(x, replications, width, label='Replication', edgecolor = \"black\", color='green')\n",
    "        ax.bar(x, propagations, width, label='Propagation', bottom=replications, edgecolor = \"black\", color='goldenrod')\n",
    "        ax.bar(x, computations, width, label='Computation', bottom=(replications+propagations), edgecolor = \"black\", color='slateblue')\n",
    "    else:\n",
    "        ax.bar(x, replications, width, edgecolor = \"black\", color='green')\n",
    "        ax.bar(x, propagations, width, bottom=replications, edgecolor = \"black\", color='goldenrod')\n",
    "        ax.bar(x, computations, width, bottom=(replications+propagations), edgecolor = \"black\", color='slateblue')\n",
    "        \n",
    "    basepoint = (replications[1] + propagations[1])\n",
    "    vals = []\n",
    "    \n",
    "    if alg==\"15d\":\n",
    "        coeff = np.sqrt(2)\n",
    "        color='fuchsia'\n",
    "        label='p^(1/2) Comm. Scaling'\n",
    "    else:\n",
    "        coeff = np.cbrt(3)\n",
    "        color='cyan'\n",
    "        label='p^(1/3) Comm. Scaling'\n",
    "    \n",
    "    for i in range(len(x) - 1):\n",
    "        vals.append(basepoint)\n",
    "        basepoint *= coeff\n",
    "        \n",
    "    if labelMarker:\n",
    "        ax.plot(x[1:], vals, marker='+', color=color, label=label)\n",
    "    else:\n",
    "        ax.plot(x[1:], vals, marker='+', color=color)\n",
    "    \n",
    "    #basepoint\n",
    "    #if alg==\"1.5d\":\n",
    "        \n",
    "    \n",
    "    if verbose:\n",
    "        print(replications)\n",
    "        print(propagations)\n",
    "        print(computations)\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15 * (2/3), 4.5 * (2/3))\n",
    "exps1 = getExps('weak_scaling_experiments/15d_scaling.out')\n",
    "exps2 = getExps('weak_scaling_experiments/25d_scaling.out')\n",
    "\n",
    "createBreakdownplot(exps1, \"15d_fusion1\", True,  ax, 0.40, 0.234, \"15d\", labelColor=True)\n",
    "createBreakdownplot(exps1, \"15d_fusion2\", True,  ax, 0.73, 0.234, \"15d\")\n",
    "createBreakdownplot(exps1, \"15d_sparse\", True,  ax, 1.08, 0.234, \"15d\", labelMarker=True)\n",
    "\n",
    "createBreakdownplot(exps2, \"25d_dense_replicate\", True,  ax, 1.43, 0.234, \"25d\")\n",
    "createBreakdownplot(exps2, \"25d_sparse_replicate\", False,  ax, 1.8, 0.234, \"25d\", labelMarker=True)\n",
    "\n",
    "# ax.set_yscale('log')\n",
    "ax.set_ylim([0.1, 23])\n",
    "ax.set_ylabel(\"Time for 5 FusedMM Calls (s)\")\n",
    "\n",
    "x = np.array([0.5, 0.85, 1.205, 1.57, 1.92])\n",
    "labels = [\"1.5D Dense Shift\\nReplication Reuse\", \"1.5D Dense Shift\\nLocal Kernel\\n Fusion\", \"1.5D Sparse Shift\\nReplication\\nReuse\", \"2.5 Dense Repl.\\nReplication\\nReuse\", \"2.5D Sparse Repl.\\nNo Elision\"]\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_title(\"Weak Scaling Setup 1 Time Breakdown\")\n",
    "ax.legend(bbox_to_anchor=(1.045, -0.23), ncol=5)\n",
    "\n",
    "fig.savefig(\"ipdps_images/weakscaling_v1_break.pdf\", bbox_inches='tight')\n",
    "#createBreakdownplot(scaling_15d_results, \"15d_fusion2\", True, '1.5D Fused Strategy 2')\n",
    "#createBreakdownplot(scaling_15d_results, \"15d_sparse\", True, '1.5D Sparse Shift Fused')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "id": "1b3b24e1-b800-494a-9317-c2f430f8a5a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8, 16, 8, 16, 8]\n",
      "4\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exps_als = getExps_progress('application_experiments/15d_als_breakdown_strong.out') + getExps_progress('application_experiments/25d_als_breakdown_strong.out')\n",
    "alg_names1 = [('15d_sparse', True), ('25d_sparse_replicate', False), ('15d_fusion2', True), ('25d_dense_replicate', True), ('15d_fusion1', True)\n",
    "             ]\n",
    "\n",
    "lastexps = [get_best_cval_exps(exps_als, alg_names1[i][0], alg_names1[i][1])[-1] for i in range(len(alg_names1))]\n",
    "\n",
    "print([exp['alg_info']['c'] for exp in lastexps])\n",
    "\n",
    "labels, replications, propagations, computations, outside =getBreakdowns(lastexps, extra=True)\n",
    "elapsed = np.array([x['elapsed'] for x in lastexps])\n",
    "delta = elapsed - replications - propagations - computations - outside\n",
    "\n",
    "x1 = np.linspace(0, 0.5, num=5)\n",
    "width = 0.1\n",
    "    \n",
    "#x = np.linspace(offset1, offset1 + wparam, num=len(labels), endpoint=True)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.grid(True)\n",
    "ax.barh(x1, replications, width, label='FusedMM Replication', edgecolor = \"black\", color='green')\n",
    "ax.barh(x1, propagations, width, label='FusedMM Propagation', left=replications, edgecolor = \"black\", color='goldenrod')\n",
    "ax.barh(x1, computations, width, label='FusedMM Computation', left=(replications+propagations), edgecolor = \"black\", color='slateblue')\n",
    "ax.barh(x1, outside, width, label='Communication outside FusedMM', left=(replications+propagations + computations), edgecolor = \"black\", color='pink')\n",
    "ax.barh(x1, delta, width, label='Computation outside FusedMM', left=(replications+propagations+computations + outside), edgecolor = \"black\", color='brown')\n",
    "\n",
    "#===========================================\n",
    "\n",
    "exps_als = getExps('application_experiments/15d_gat_strong.out') + getExps('application_experiments/25d_gat_strong.out')\n",
    "alg_names2 = [('15d_sparse', True),('25d_sparse_replicate', False), ('25d_dense_replicate', True), ('15d_fusion1', True)\n",
    "             ]\n",
    "\n",
    "lastexps = [get_best_cval_exps(exps_als, alg_names2[i][0], alg_names2[i][1])[-1] for i in range(len(alg_names2))]\n",
    "labels, replications, propagations, computations=getBreakdowns(lastexps)\n",
    "print(len(lastexps))\n",
    "elapsed = np.array([x['elapsed'] for x in lastexps])\n",
    "delta = elapsed - replications - propagations - computations\n",
    "\n",
    "x2 = np.linspace(1.0, 1.4, num=4, endpoint=True)\n",
    "width = 0.1\n",
    "    \n",
    "#x = np.linspace(offset1, offset1 + wparam, num=len(labels), endpoint=True)\n",
    "\n",
    "ax.grid(True)\n",
    "ax.barh(x2, replications, width, edgecolor = \"black\", color='green')\n",
    "ax.barh(x2, propagations, width, left=replications, edgecolor = \"black\", color='goldenrod')\n",
    "ax.barh(x2, computations, width, left=(replications+propagations), edgecolor = \"black\", color='slateblue')\n",
    "ax.barh(x2, delta, width, left=(replications+propagations+computations), edgecolor = \"black\", color='brown')\n",
    "ax.set_xlabel(\"Application Runtime (s)\")\n",
    "print()\n",
    "ax.set_yticks(np.concatenate((x1, x2)))\n",
    "ax.set_yticklabels(['1.5D Sparse Shift', '2.5D Sparse Repl.', '1.5D Local Kernel Fusion', '2.5D Dense Repl.', '1.5D Dense Repl. Reuse', \n",
    "                    '1.5D Sparse Shift', '2.5D Sparse Repl.', '2.5D Dense Repl. Reuse', '1.5D Dense Repl. Reuse'])\n",
    "\n",
    "ax.text(-0.45, 0.88, \"GAT\", transform=ax.transAxes, fontsize=14,\n",
    "        verticalalignment='top', rotation=90)\n",
    "\n",
    "ax.text(-0.45, 0.30, \"ALS\", transform=ax.transAxes, fontsize=14,\n",
    "        verticalalignment='top', rotation=90)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(0.405, 0.738), framealpha=1.0)\n",
    "\n",
    "fig.show()\n",
    "fig.savefig(\"ipdps_images/app_benchmark.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9e9a8e-c9a1-474d-8d46-1f9e8eb549fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86d45260-60f1-4f53-bdf8-f3600df1693b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history saving thread hit an unexpected error (OperationalError('disk I/O error')).History will not be written to the database.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1188x435.6 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def makeStrongScalingPlot(alg, filename, ax, logscale=True, base_alg=None, baseline_file=None, prog=False, fil=0):\n",
    "    if not prog:\n",
    "        exps = getExps(filename)\n",
    "    else:\n",
    "        exps = getExps_progress(filename)\n",
    "    \n",
    "    if alg==\"15d\":\n",
    "        generateScalingPlot(exps, \"15d_fusion1\", False, \"1.5D Dense Shift, No Elision\", ('blue', 's'), ax, fil=fil)\n",
    "        generateScalingPlot(exps, \"15d_fusion1\", True, \"1.5D Dense Shift, Repl. Reuse\", ('darkorange', 'o'), ax,fil=fil)\n",
    "        generateScalingPlot(exps, \"15d_fusion2\", True, \"1.5D Dense Shift, Local Kernel Fusion\", ('green', 'o'), ax, fil=fil)\n",
    "        generateScalingPlot(exps, \"15d_sparse\", False, \"1.5D Sparse Shift, No Elision\",('royalblue', 's'), ax, fil=fil)\n",
    "        generateScalingPlot(exps, \"15d_sparse\", True, \"1.5D Sparse Shift, Repl. Reuse\",('red', 'o'), ax, fil=fil)\n",
    "    elif alg==\"25d\":\n",
    "        generateScalingPlot(exps, \"25d_sparse_replicate\", False, \"2.5D Sparse Repl., No Elision\", ('chocolate', 's'), ax, fil=fil)\n",
    "        generateScalingPlot(exps, \"25d_dense_replicate\", True, \"2.5D Dense Repl., Repl. Reuse\", ('indigo', 'o'), ax, fil=fil)\n",
    "        generateScalingPlot(exps, \"25d_dense_replicate\", False, \"2.5D Dense Repl., No Elision\", ('steelblue', 's'), ax, fil=fil)\n",
    "        \n",
    "    if logscale:\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')\n",
    "        \n",
    "    #if base_alg is not None:\n",
    "    #    exps = get_best_cval_exps(exps, base_alg[0], base_alg[1])\n",
    "    #    basepoint = (exps[0]['alg_info']['p'], getTotalTime(exps[0]))\n",
    "    #    h_axis = []\n",
    "    #    h_counter = basepoint[0]\n",
    "    #    counter = basepoint[1]\n",
    "    #    values = []\n",
    "    #    while h_counter <= 256:\n",
    "    #        values += [counter]\n",
    "    #        h_axis += [h_counter]\n",
    "    #        counter /= 2\n",
    "    #        h_counter *= 2\n",
    "        \n",
    "        #ax.plot(h_axis, values, color='black', linestyle='dashed', label=\"Ideal Scaling\")\n",
    "    \n",
    "    if baseline_file is not None:\n",
    "        exps = getExps(baseline_file)\n",
    "        xvals = [exp['alg_info']['p'] / 68 for exp in exps]\n",
    "        yvals = [exp['elapsed'] for exp in exps]\n",
    "        \n",
    "        ax.plot(xvals, yvals, c='black', label=\"PetSC (Baseline)\", marker='s')\n",
    "        \n",
    "        # There was a timeout for this baseline, so we\n",
    "        if baseline_file=='petsc_baseline/petsc_eukarya_strong64.out':\n",
    "            highpt = yvals[-1]\n",
    "            ax.plot(xvals, yvals, c='black')\n",
    "            ax.scatter(xvals, yvals, c='black', marker='s')\n",
    "            ax.scatter([128, 256], [highpt, highpt], c='black', marker='^', label=\"PetSC Timeout\")\n",
    "        \n",
    "    \n",
    "    ax.grid(True)\n",
    "\n",
    "#fig, axs = plt.subplots(2, 3, sharex=True, sharey=True, tight_layout=True)\n",
    "#fig.set_size_inches(21.5 * (2/3), 10.5 * (2/3))\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(15 * (1.1), 5.5 * (1.1))\n",
    "spec = fig.add_gridspec(2, 5, wspace=0, hspace=0.0, left=0.0, right=0.8)\n",
    "axs = [[fig.add_subplot(spec[i, j]) for j in range(5)] for i in range(2)]\n",
    "\n",
    "makeStrongScalingPlot('15d', 'strong_scaling_experiments/15d_amazon_strong.out', axs[0][0], base_alg=('15d_sparse', True), baseline_file='petsc_baseline/petsc_amazon_strong64.out')\n",
    "#axs[0][0].set_ylabel(\"Time for 5 FusedMM Calls (s)\")\n",
    "#axs[0, 0].set_ylim([2.0, 80])\n",
    "axs[0][0].set_title(\"amazon-large.mtx\")\n",
    "makeStrongScalingPlot('15d', 'strong_scaling_experiments/15d_uk_strong.out', axs[0][1], base_alg=('15d_sparse', True), baseline_file='petsc_baseline/petsc_uk_strong64.out')\n",
    "axs[0][1].set_title(\"uk-2002.mtx\")\n",
    "makeStrongScalingPlot('15d', 'strong_scaling_experiments/15d_eukarya_strong.out', axs[0][2], base_alg=('15d_fusion2', True), baseline_file='petsc_baseline/petsc_eukarya_strong64.out')\n",
    "axs[0][2].set_title(\"eukarya.mtx\")\n",
    "\n",
    "makeStrongScalingPlot('25d', 'strong_scaling_experiments/25d_amazon_strong.out', axs[1][0], base_alg=('25d_dense_replicate', True))\n",
    "#axs[1][0].set_ylabel(\"Time for 5 FusedMM Calls (s)\")\n",
    "makeStrongScalingPlot('25d', 'strong_scaling_experiments/25d_uk_strong.out', axs[1][1], base_alg=('25d_dense_replicate', True))\n",
    "makeStrongScalingPlot('25d', 'strong_scaling_experiments/25d_eukarya_strong.out', axs[1][2], base_alg=('25d_sparse_replicate', False)) \n",
    "\n",
    "makeStrongScalingPlot('15d', 'strong_scaling_experiments/15d_twitter7_strong.out', axs[0][4], base_alg=('25d_sparse_replicate', False), prog=True)\n",
    "makeStrongScalingPlot('25d', 'strong_scaling_experiments/25d_twitter7_strong.out', axs[1][4], base_alg=('25d_sparse_replicate', False), prog=True)\n",
    "axs[0][4].set_title(\"twitter7.mtx\")\n",
    "\n",
    "makeStrongScalingPlot('15d', 'strong_scaling_experiments/15d_arabic_strong.out', axs[0][3], base_alg=('25d_sparse_replicate', False), prog=True, fil=4)\n",
    "makeStrongScalingPlot('25d', 'strong_scaling_experiments/25d_arabic_strong.out', axs[1][3], base_alg=('25d_sparse_replicate', False), prog=True, fil=4)\n",
    "axs[0][3].set_title(\"arabic-2005.mtx\")\n",
    "\n",
    "fig.text(0.40, 0.046, 'Node Count', ha='center')\n",
    "fig.text(-0.03, 0.32, \"Time for 5 FusedMM Calls (s)\", rotation='vertical')\n",
    "h25d, l25d = axs[1][1].get_legend_handles_labels()\n",
    "h15d, l15d = axs[0][2].get_legend_handles_labels()\n",
    "\n",
    "axs[0][0].sharey(axs[0][0])\n",
    "axs[0][1].sharey(axs[0][0])\n",
    "axs[1][0].sharey(axs[0][0])\n",
    "axs[0][2].sharey(axs[0][0])\n",
    "axs[0][3].sharey(axs[0][0])\n",
    "axs[0][4].sharey(axs[0][0])\n",
    "#axs[0][4].sharex(axs[0][1])\n",
    "\n",
    "axs[1][1].sharey(axs[0][0])\n",
    "axs[1][2].sharey(axs[0][0])\n",
    "axs[1][3].sharey(axs[0][0])\n",
    "axs[1][4].sharey(axs[0][0])\n",
    "axs[1][4].sharex(axs[0][4])\n",
    "\n",
    "axs[0][1].sharex(axs[1][1])\n",
    "\n",
    "axs[0][1].label_outer()\n",
    "axs[1][1].label_outer()\n",
    "axs[0][2].label_outer()\n",
    "axs[0][3].label_outer()\n",
    "axs[1][2].label_outer()\n",
    "axs[1][3].label_outer()\n",
    "\n",
    "axs[0][4].label_outer()\n",
    "axs[1][4].label_outer()\n",
    "\n",
    "fig.legend(h15d+h25d, l15d+l25d, bbox_to_anchor=(0.825, 0.035), ncol=5)\n",
    "#fig.legend(h25d, l25d, bbox_to_anchor=(1.0, 0.4))\n",
    "fig.show()\n",
    "fig.savefig(\"ipdps_images/strong_scaling_baseline.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "561e977f-7e1b-4b88-8067-563633d06a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#exps = getExps_progress(\"strong_scaling_experiments/15d_twitter7_strong.out\")\n",
    "#for exp in exps:\n",
    "    #if exp['alg_info']['p']==128:\n",
    "    #print(\"{}, {}, {}, {}\".format(exp['alg_info']['p'], exp['alg_info']['c'], exp['alg_name'], exp['elapsed']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "id": "95c6815d-a05f-4cc2-8075-40560ff00726",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x198 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "spec = fig.add_gridspec(1, 2, wspace=0, left=0.0, right=0.6)\n",
    "fig.set_size_inches(15 * (1/2), 5.5 * (1/2))\n",
    "\n",
    "tick_locations = [1, 64, 128, 192, 256]\n",
    "\n",
    "ax0 = fig.add_subplot(spec[0, 0])\n",
    "ax1 = fig.add_subplot(spec[0, 1])\n",
    "makeStrongScalingPlot('15d', 'weak_scaling_experiments/15d_scaling.out', ax0, logscale=False)\n",
    "ax0.set_ylabel(\"Time for 5 FusedMM Calls (s)\")\n",
    "makeStrongScalingPlot('25d', 'weak_scaling_experiments/25d_scaling.out', ax1, logscale=False)\n",
    "\n",
    "ax0.sharey(ax1)\n",
    "ax1.label_outer()\n",
    "ax0.set_xticks(tick_locations)\n",
    "ax1.set_xticks(tick_locations)\n",
    "\n",
    "#fig.show()\n",
    "#fig.savefig(\"ipdps_images/weakscaling_setup1.pdf\", bbox_inches='tight')\n",
    "\n",
    "h15d, l15d = ax0.get_legend_handles_labels()\n",
    "\n",
    "#fig, axs = plt.subplots(1, 4, sharey='row', tight_layout=True)\n",
    "#fig.set_size_inches(15 * (1/2), 5.5 * (1/2))\n",
    "\n",
    "spec = fig.add_gridspec(1, 2, wspace=0, left=0.65, right=1.25)\n",
    "ax2 = fig.add_subplot(spec[0, 0])\n",
    "ax3 = fig.add_subplot(spec[0, 1])\n",
    "#fig.suptitle(\"Weak Scaling Setup 2\",y=1.05, fontsize=14)\n",
    "makeStrongScalingPlot('15d', 'weak_scaling_experiments/15d_scaling_v2.out', ax2, logscale=False)\n",
    "#axs[0].set_ylabel(\"Time for 5 FusedMM Calls (s)\")\n",
    "#axs[0].set_xlabel(\"Number of Nodes\")\n",
    "makeStrongScalingPlot('25d', 'weak_scaling_experiments/25d_scaling_v2.out', ax3, logscale=False)\n",
    "ax2.sharey(ax3)\n",
    "ax3.label_outer()\n",
    "\n",
    "ax2.set_xticks(tick_locations)\n",
    "ax3.set_xticks(tick_locations)\n",
    "\n",
    "\n",
    "fig.text(0.62, -0.05, 'Node Count', ha='center')\n",
    "fig.text(0.3, 0.9, 'Weak Scaling Setup 1', ha='center')\n",
    "fig.text(0.95, 0.9, 'Weak Scaling Setup 2', ha='center')\n",
    "#axs[1].set_xlabel(\"Number of Nodes\")\n",
    "#fig.savefig(\"ipdps_images/weakscaling_combined.pdf\", bbox_inches='tight')\n",
    "\n",
    "h25d, l25d = ax1.get_legend_handles_labels()\n",
    "\n",
    "#fig.set_size_inches(15 * (1/2), 5.5 * (1/2))\n",
    "leg = fig.legend(h15d + h25d, l15d + l25d, loc='upper center', bbox_to_anchor=(1.47, 0.91))\n",
    "\n",
    "#leg = fig.legend(h25d, l25d, loc='upper center', bbox_to_anchor=(0.87, 0.5))\n",
    "\n",
    "fig.show()\n",
    "fig.savefig(\"ipdps_images/weakscaling_unified.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "3e4b7e3c-7fc1-4ead-a347-99083152a2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8585d75e-28c7-4b31-a81e-58888cf2eef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "weak_exps = getExps('weak_scaling_experiments/15d_scaling.out')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "41c6826d-a37c-4b79-841c-7e67652f626a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\t15d_sparse\tTrue\t0.837529797\n",
      "2\t15d_sparse\tTrue\t1.036933992\n",
      "4\t15d_sparse\tTrue\t1.463007906\n",
      "8\t15d_sparse\tTrue\t1.969684017\n",
      "16\t15d_sparse\tTrue\t2.761679464\n",
      "32\t15d_sparse\tTrue\t3.814447761\n",
      "64\t15d_sparse\tTrue\t5.369184414\n",
      "128\t15d_sparse\tTrue\t7.899415726000001\n",
      "256\t15d_sparse\tTrue\t14.241181273\n",
      "Dummy Throughput: 6.468324e+09\n"
     ]
    }
   ],
   "source": [
    "alg_names2 = [('15d_sparse', True),('25d_sparse_replicate', False), ('25d_dense_replicate', True), ('15d_fusion1', True),\n",
    "              ('15d_sparse', False), ('15d_fusion1', False), ('25d_dense_replicate', False), ('15d_fusion2', True)\n",
    "             ]\n",
    "\n",
    "firstexps = get_best_cval_exps(weak_exps, '15d_sparse', True)\n",
    "\n",
    "for exp in firstexps:\n",
    "    print(\"{}\\t{}\\t{}\\t{}\".format(exp['alg_info']['p'], exp['alg_name'], exp['fused'], getTotalTime(exp)))\n",
    "    \n",
    "    \n",
    "    \n",
    "throughput_dummy = (2 ** 16) * 1 * 32 * 256 * 5 * 2 / 0.83\n",
    "print(\"Dummy Throughput: {:e}\".format(throughput_dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb2a9b16-a750-4801-9907-b0bda9fc6dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 660x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('default')\n",
    "\n",
    "def fusion2model(exp, c):\n",
    "    p = exp['alg_info']['p']\n",
    "    r = exp['alg_info']['r']\n",
    "    n = exp['alg_info']['n']\n",
    "    \n",
    "    return n * r / c + (2 * (c - 1) * n * r / p)\n",
    "\n",
    "def unfusedmodel(exp, c):\n",
    "    p = exp['alg_info']['p']\n",
    "    r = exp['alg_info']['r']\n",
    "    n = exp['alg_info']['n']\n",
    "    \n",
    "    return 2 * n * r / c + (2 * (c - 1) * n * r / p)\n",
    "\n",
    "def fusionmodel1(exp, c):\n",
    "    p = exp['alg_info']['p']\n",
    "    r = exp['alg_info']['r']\n",
    "    n = exp['alg_info']['n']\n",
    "    \n",
    "    return 2 * n * r / c + (1 * (c - 1) * n * r / p)\n",
    "\n",
    "def getOptimal(exp, model):\n",
    "    testVals = np.array([1, 2, 4, 8, 16, 32])\n",
    "    return testVals[np.argmin(model(exp, testVals))]\n",
    "\n",
    "\n",
    "exps = getExps('weak_scaling_experiments/15d_scaling.out')\n",
    "predfusion2 = [getOptimal(x, fusion2model)  for x in get_best_cval_exps(exps, '15d_fusion2', True)][1:]\n",
    "predunfused = [getOptimal(x, unfusedmodel) for x in get_best_cval_exps(exps, '15d_fusion1', False)][1:]\n",
    "predfusion1 = [getOptimal(x, fusionmodel1) for x in get_best_cval_exps(exps, '15d_fusion1', True)][1:]\n",
    "\n",
    "fusion2_cvals = [x['alg_info']['c'] for x in get_best_cval_exps(exps, '15d_fusion2', True)][1:]\n",
    "unfused_cvals = [x['alg_info']['c'] for x in get_best_cval_exps(exps, '15d_fusion1', False)][1:]\n",
    "fusion1_cvals = [x['alg_info']['c'] for x in get_best_cval_exps(exps, '15d_fusion1', True)][1:]\n",
    "\n",
    "pvals = np.array([x['alg_info']['p'] for x in get_best_cval_exps(exps, '15d_fusion1', True)][1:])\n",
    "labels = [str(x) for x in pvals]\n",
    "\n",
    "x = np.arange(len(labels))\n",
    "width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(16.5 * (0.4), 10.5 * (0.4))\n",
    "ax.bar(x - width/3*2, fusion2_cvals, width * 2 / 3, label='1.5D Local Kernel Fusion',color='green', edgecolor = \"black\")\n",
    "ax.bar(x, unfused_cvals, width * 2 / 3, label='1.5D No Elision',color='darkorange', edgecolor = \"black\")\n",
    "ax.bar(x + width/3*2, fusion1_cvals, width * 2 / 3, label='1.5D Replication Reuse',color='blue', edgecolor = \"black\")\n",
    "\n",
    "plt.scatter(x-width/3*2, predfusion2, marker=\"+\", color=\"fuchsia\", zorder=3)\n",
    "plt.scatter(x, predunfused, marker=\"+\", color=\"fuchsia\", zorder=3)\n",
    "plt.scatter(x+width/3*2, predfusion1, marker=\"+\", color=\"fuchsia\", zorder=3, label=\"Prediction\")\n",
    "ax.set_yscale('log')\n",
    "ax.set_yticks([1, 2, 4, 8, 16])\n",
    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax.set_ylabel(\"Optimal Replication Factor\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.set_xlabel(\"Processor Count\")\n",
    "ax.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "fig.savefig(\"ipdps_images/optimal_replication.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1177,
   "id": "723e123e-21da-4b54-9bbf-410fb6c75293",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15d_sparse\t\tTrue\t5.242632729\n",
      "25d_sparse_replicate\t\tFalse\t2.345798563\n",
      "25d_dense_replicate\t\tTrue\t2.16074976\n",
      "15d_fusion1\t\tTrue\t4.219745769\n",
      "15d_sparse\t\tFalse\t5.129978375\n",
      "15d_fusion1\t\tFalse\t4.37230205\n",
      "25d_dense_replicate\t\tFalse\t3.115372773\n",
      "15d_fusion2\t\tTrue\t2.704648542\n"
     ]
    }
   ],
   "source": [
    "# Get the best fused speedup on strong scaling\n",
    "\n",
    "exps_als = getExps('strong_scaling_experiments/15d_eukarya_strong.out') + getExps('strong_scaling_experiments/25d_eukarya_strong.out')\n",
    "alg_names2 = [('15d_sparse', True),('25d_sparse_replicate', False), ('25d_dense_replicate', True), ('15d_fusion1', True),\n",
    "              ('15d_sparse', False), ('15d_fusion1', False), ('25d_dense_replicate', False), ('15d_fusion2', True)\n",
    "             ]\n",
    "\n",
    "lastexps = [get_best_cval_exps(exps_als, alg_names2[i][0], alg_names2[i][1])[-1] for i in range(len(alg_names2))]\n",
    "\n",
    "dict = {}\n",
    "for exp in lastexps:\n",
    "    print(\"{}\\t\\t{}\\t{}\".format(exp['alg_name'], exp['fused'], getTotalTime(exp)))\n",
    "    dict[(exp['alg_name'], exp['fused'])] = getTotalTime(exp)\n",
    "    \n",
    "# Print the speedup of Eukarya on fusion version 2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1181,
   "id": "3dc882f5-683e-43fd-8ddf-b51d6091443c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eukarya Best Speedup, Fused: 1.6165878790176649\n"
     ]
    }
   ],
   "source": [
    "# Get the best speedup on Eukarya\n",
    "\n",
    "eukarya_best_speedup = dict[\"15d_fusion1\", False] / dict[\"15d_fusion2\", True]\n",
    "\n",
    "print(\"Eukarya Best Speedup, Fused: {}\".format(eukarya_best_speedup))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d7e894a-9284-4fb8-84dd-22639b645123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15d_sparse\t\tTrue\t5.242632729\n",
      "25d_sparse_replicate\t\tFalse\t2.345798563\n",
      "25d_dense_replicate\t\tTrue\t2.16074976\n",
      "15d_fusion1\t\tTrue\t4.219745769\n",
      "15d_sparse\t\tFalse\t5.129978375\n",
      "15d_fusion1\t\tFalse\t4.37230205\n",
      "25d_dense_replicate\t\tFalse\t3.115372773\n",
      "15d_fusion2\t\tTrue\t2.704648542\n",
      "=======================\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats\n",
    "\n",
    "# Get the best fused speedup on strong scaling\n",
    "\n",
    "exps_als = getExps('strong_scaling_experiments/15d_eukarya_strong.out') + getExps('strong_scaling_experiments/25d_eukarya_strong.out')\n",
    "alg_names2 = [('15d_sparse', True),('25d_sparse_replicate', False), ('25d_dense_replicate', True), ('15d_fusion1', True),\n",
    "              ('15d_sparse', False), ('15d_fusion1', False), ('25d_dense_replicate', False), ('15d_fusion2', True)\n",
    "             ]\n",
    "\n",
    "lastexps = [get_best_cval_exps(exps_als, alg_names2[i][0], alg_names2[i][1])[-1] for i in range(len(alg_names2))]\n",
    "\n",
    "dict = {}\n",
    "for exp in lastexps:\n",
    "    print(\"{}\\t\\t{}\\t{}\".format(exp['alg_name'], exp['fused'], getTotalTime(exp)))\n",
    "    dict[(exp['alg_name'], exp['fused'])] = getTotalTime(exp)\n",
    "\n",
    "    \n",
    "print(\"=======================\")\n",
    "# Find the strong scaling efficiency of each.\n",
    "\n",
    "for name in alg_names2:\n",
    "    exps = get_best_cval_exps(exps_als, name[0], name[1])\n",
    "    pvals = np.array([exp['alg_info']['p'] for exp in exps])\n",
    "    times = np.array([getTotalTime(exp) for exp in exps])\n",
    "    \n",
    "    \n",
    "    res = scipy.stats.linregress(np.log2(pvals), y=np.log2(times))\n",
    "    #print(\"{}\\t{}\\t{}\".format(name[0], name[1], dict[name]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1203,
   "id": "d515edb2-face-45c0-b6e6-b11af2850a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1666666666666665"
      ]
     },
     "execution_count": 1203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "15.34/7.08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1204,
   "id": "891e1a82-d613-4ded-a09e-59bfb75c90f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0098870056497176"
      ]
     },
     "execution_count": 1204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7.15/7.08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1206,
   "id": "ecbc135a-864a-415f-b2e0-8867d24fc2a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9407407407407407"
      ]
     },
     "execution_count": 1206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5.24/2.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98b9a30c-86f5-47da-85a3-7a808901e331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15d_sparse\t\tTrue\t14.241181273\n",
      "25d_sparse_replicate\t\tFalse\t16.382786839\n",
      "25d_dense_replicate\t\tTrue\t18.379623188\n",
      "15d_fusion1\t\tTrue\t22.325811908\n",
      "15d_sparse\t\tFalse\t16.698089936\n",
      "15d_fusion1\t\tFalse\t25.856556266\n",
      "25d_dense_replicate\t\tFalse\t22.278187663\n",
      "15d_fusion2\t\tTrue\t18.64558319\n"
     ]
    }
   ],
   "source": [
    "exps_als = getExps('weak_scaling_experiments/15d_scaling.out') + getExps('weak_scaling_experiments/25d_scaling.out')\n",
    "alg_names2 = [('15d_sparse', True),('25d_sparse_replicate', False), ('25d_dense_replicate', True), ('15d_fusion1', True),\n",
    "              ('15d_sparse', False), ('15d_fusion1', False), ('25d_dense_replicate', False), ('15d_fusion2', True)\n",
    "             ]\n",
    "\n",
    "lastexps = [get_best_cval_exps(exps_als, alg_names2[i][0], alg_names2[i][1])[-1] for i in range(len(alg_names2))]\n",
    "\n",
    "dict = {}\n",
    "for exp in lastexps:\n",
    "    print(\"{}\\t\\t{}\\t{}\".format(exp['alg_name'], exp['fused'], getTotalTime(exp)))\n",
    "    dict[(exp['alg_name'], exp['fused'])] = getTotalTime(exp)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1213,
   "id": "61c822f4-e31e-425c-95b2-2cfae3190fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1581541218637994"
      ]
     },
     "execution_count": 1213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25.85/22.32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1214,
   "id": "4253ead1-34ef-4144-bce2-56efc87ad54f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15d_sparse\t\tTrue\t3.999968379\n",
      "25d_sparse_replicate\t\tFalse\t3.414387077\n",
      "25d_dense_replicate\t\tTrue\t2.337562412\n",
      "15d_fusion1\t\tTrue\t2.965727946\n",
      "15d_sparse\t\tFalse\t4.690710374\n",
      "15d_fusion1\t\tFalse\t2.4153661029999998\n",
      "25d_dense_replicate\t\tFalse\t2.906639443\n",
      "15d_fusion2\t\tTrue\t2.069167326\n"
     ]
    }
   ],
   "source": [
    "exps_als = getExps('weak_scaling_experiments/15d_scaling_v2.out') + getExps('weak_scaling_experiments/25d_scaling_v2.out')\n",
    "alg_names2 = [('15d_sparse', True),('25d_sparse_replicate', False), ('25d_dense_replicate', True), ('15d_fusion1', True),\n",
    "              ('15d_sparse', False), ('15d_fusion1', False), ('25d_dense_replicate', False), ('15d_fusion2', True)\n",
    "             ]\n",
    "\n",
    "lastexps = [get_best_cval_exps(exps_als, alg_names2[i][0], alg_names2[i][1])[-1] for i in range(len(alg_names2))]\n",
    "\n",
    "dict = {}\n",
    "for exp in lastexps:\n",
    "    print(\"{}\\t\\t{}\\t{}\".format(exp['alg_name'], exp['fused'], getTotalTime(exp)))\n",
    "    dict[(exp['alg_name'], exp['fused'])] = getTotalTime(exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'distributed_sddmm/heatmap_experiments/heatmap1.out'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m             configs\u001b[38;5;241m.\u001b[39mappend((i, j, c, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m15d_sparse\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# Find a list of missing configurations from our experiment list\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m exps \u001b[38;5;241m=\u001b[39m \u001b[43mgetExps_progress\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mheatmap_experiments/heatmap1.out\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m config_map \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m exp \u001b[38;5;129;01min\u001b[39;00m exps:\n",
      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36mgetExps_progress\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgetExps_progress\u001b[39m(filename):\n\u001b[0;32m---> 23\u001b[0m     f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdistributed_sddmm/\u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     24\u001b[0m     lines \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(f\u001b[38;5;241m.\u001b[39mreadlines())\n\u001b[1;32m     25\u001b[0m     lines \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m lines[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mrstrip(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'distributed_sddmm/heatmap_experiments/heatmap1.out'"
     ]
    }
   ],
   "source": [
    "# Expected configurations in the heatmap\n",
    "configs = []\n",
    "nnz_list = [21, 43, 64, 85, 107, 128, 149]\n",
    "r_list = [64, 128, 192, 256, 320, 384, 448]\n",
    "\n",
    "for i in nnz_list:\n",
    "    for j in r_list:\n",
    "        for c in [2, 8]:\n",
    "            configs.append((i, j, c, \"25d_sparse_replicate\"))\n",
    "            configs.append((i, j, c, \"25d_dense_replicate\"))\n",
    "            \n",
    "        for c in [1, 2, 4, 8]:\n",
    "            configs.append((i, j, c, \"15d_fusion1\"))\n",
    "            configs.append((i, j, c, \"15d_fusion2\"))\n",
    "            configs.append((i, j, c, \"15d_sparse\"))\n",
    "            \n",
    "# Find a list of missing configurations from our experiment list\n",
    "exps = getExps_progress(\"heatmap_experiments/heatmap1.out\")\n",
    "\n",
    "config_map = {}\n",
    "\n",
    "for exp in exps:\n",
    "    nnz_per_row = min(nnz_list, key=lambda x:abs(x - exp['alg_info']['nnz'] / exp['alg_info']['m']))\n",
    "    \n",
    "    key = (nnz_per_row, exp['alg_info']['r'], exp['alg_info']['c'], exp['alg_name'])\n",
    "    if key not in config_map.keys():\n",
    "        config_map[key] = [exp['elapsed']]\n",
    "    else:\n",
    "        config_map[key].append(exp['elapsed'])\n",
    "        \n",
    "exps = getExps_progress(\"heatmap_experiments/heatmap2.out\")\n",
    "\n",
    "for exp in exps:\n",
    "    nnz_per_row = min(nnz_list, key=lambda x:abs(x - exp['alg_info']['nnz'] / exp['alg_info']['m']))\n",
    "    \n",
    "    key = (nnz_per_row, exp['alg_info']['r'], exp['alg_info']['c'], exp['alg_name'])\n",
    "    if key not in config_map.keys():\n",
    "        config_map[key] = [exp['elapsed']]\n",
    "    else:\n",
    "        config_map[key].append(exp['elapsed'])\n",
    "\n",
    "def getWinnersLosers(config_map):\n",
    "    # Map down the configurations to find the best c value for each algorithm.\n",
    "    mapped_down = {}\n",
    "    for key in config_map.keys():\n",
    "        map_key = (key[0], key[1], key[3])\n",
    "        if map_key not in mapped_down.keys():\n",
    "            mapped_down[map_key] = [(key[2], config_map[key][0])]\n",
    "        else:\n",
    "            mapped_down[map_key].append((key[2], config_map[key][0]))\n",
    "        \n",
    "    best_cvalues = {}\n",
    "    for key in mapped_down.keys():\n",
    "        vals = [x[1] for x in mapped_down[key]]\n",
    "        best_cvalues[key] = mapped_down[key][np.argmin(vals)]\n",
    "    \n",
    "    mapped_down_second = {}\n",
    "    for key in best_cvalues.keys():\n",
    "        map_key = (key[0], key[1])\n",
    "        if map_key not in mapped_down_second.keys():\n",
    "            mapped_down_second[map_key] = [(key[2], best_cvalues[key][1])]\n",
    "        else:\n",
    "            mapped_down_second[map_key].append((key[2], best_cvalues[key][1]))\n",
    "        \n",
    "    winners = {}\n",
    "    losers = {}\n",
    "\n",
    "    for key in mapped_down_second.keys():\n",
    "        vals = [x[1] for x in mapped_down_second[key]]\n",
    "        winners[key] = mapped_down_second[key][np.argmin(vals)][0]\n",
    "        losers[key] = mapped_down_second[key][np.argmax(vals)][0]\n",
    "        \n",
    "    return winners, losers\n",
    "    \n",
    "# \"15d_fusion1\": ([147, 217, 9], # Same color; we don't care about the replication strategy here. \n",
    "\n",
    "color_scheme = {\"15d_fusion1\": ([147, 217, 9], \"1.5D Dense Shift w/\\n Local Kernel Fusion\"),\n",
    "                \"15d_fusion2\": ([147, 217, 9], \"1.5D Dense Shift w/\\n Local Kernel Fusion\"),\n",
    "                \"15d_sparse\": ([90, 133, 5], \"1.5D Sparse Shift w/\\n Replication Reuse\"),\n",
    "                \"25d_dense_replicate\": ([128, 10, 207], \"2.5D Dense Replicate, Kernel Overlap\"),\n",
    "                \"25d_sparse_replicate\": ([81, 7, 130], \"2.5D Sparse Replicate, Back-to-Back\")}\n",
    "\n",
    "def plot_heatmap(ax, algs, label=True):\n",
    "\n",
    "    img = np.zeros((len(nnz_list), len(r_list), 3), dtype=np.int32)\n",
    "    for i in range(len(nnz_list)):\n",
    "        for j in range(len(r_list)):\n",
    "            img[len(nnz_list) - i - 1, j] = color_scheme[algs[(nnz_list[i], r_list[j])]][0]\n",
    "    ax.imshow(img)\n",
    "\n",
    "    ax.set_xticks(np.arange(0, 7, 1))\n",
    "    ax.set_yticks(np.arange(0, 7, 1))\n",
    "\n",
    "    ax.set_xticklabels(r_list)\n",
    "    ax.set_yticklabels(list(reversed(nnz_list)))\n",
    "\n",
    "    ax.set_xticks(np.arange(-.5, 7, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, 7, 1), minor=True)\n",
    "    ax.grid(which='minor', color='w', linestyle='-', linewidth=2)\n",
    "    ax.plot([-0.5, 6.5], [6.5, -0.5], 'r--')\n",
    "    \n",
    "    if label:\n",
    "        ax.set_ylabel(\"NNZ per row of S\")\n",
    "    \n",
    "def heatmap_legend():\n",
    "    from matplotlib.patches import Patch\n",
    "    from matplotlib.lines import Line2D\n",
    "\n",
    "    legend_elements = [Patch(facecolor=tuple(i / 255 for i in color_scheme[key][0]), edgecolor='w',\n",
    "                         label=color_scheme[key][1]) for key in list(color_scheme.keys())[1:3]\n",
    "                  ]\n",
    "    legend_elements.append(Line2D([0], [0], color='r', linestyle='--', label='3 nnz(S) / r = 1'))\n",
    "\n",
    "    return legend_elements\n",
    "\n",
    "f = open(\"distributed_sddmm/heatmap_experiments/theoretical_heatmap.json\")\n",
    "theoreticalResults = json.load(f)\n",
    "\n",
    "from ast import literal_eval as make_tuple\n",
    "\n",
    "theoretical_map = {}\n",
    "\n",
    "for key in theoreticalResults.keys():\n",
    "    lst = key[1:-1].split(',')\n",
    "    theoretical_map[(int(lst[0]), int(lst[1]), int(lst[2]), lst[3])] = [theoreticalResults[key]]\n",
    "    \n",
    "fig, axs = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "plot_heatmap(axs[0], getWinnersLosers(theoretical_map)[0])\n",
    "plot_heatmap(axs[1], getWinnersLosers(config_map)[0], label=False)\n",
    "axs[0].set_title(\"Predicted\")\n",
    "axs[1].set_title(\"Observed\")\n",
    "plt.subplots_adjust(wspace=0.05, hspace=0.01)\n",
    "fig.text(0.5, 0.1, 'Value of r', ha='center')\n",
    "\n",
    "plt.legend(handles=heatmap_legend(), bbox_to_anchor=(1.91, 1.03), title=\"Fastest Algorithm\", title_fontsize=\"large\")\n",
    "fig.savefig(\"ipdps_images/heatmaps.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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