Tutorial_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple benchmark example\n",
    "\n",
    "Refer to the [tutorial page](Tutorial.md) for more insight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plumed_bench_pp.parser import parse_full_benchmark_output\n",
    "from plumed_bench_pp.tabulate import convert_to_table\n",
    "from plumed_bench_pp.plot import plot_histo\n",
    "import plumed_bench_pp.constants as plmdbppconst\n",
    "import plumed_bench_pp.utils as plmdbpputils\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "nthreads=[1,2,4,6,8,10,12]\n",
    "\n",
    "def lighten_color(color, amount=0.5):\n",
    "    \"\"\"\n",
    "    Lightens the given color by multiplying (1-luminosity) by the given amount.\n",
    "    Input can be matplotlib color string, hex string, or RGB tuple.\n",
    "\n",
    "    Examples:\n",
    "    >> lighten_color('g', 0.3)\n",
    "    >> lighten_color('#F034A3', 0.6)\n",
    "    >> lighten_color((.3,.55,.1), 0.5)\n",
    "    \"\"\"\n",
    "    import matplotlib.colors as mc\n",
    "    import colorsys\n",
    "\n",
    "    try:\n",
    "        c = mc.cnames[color]\n",
    "    except:\n",
    "        c = color\n",
    "    c = colorsys.rgb_to_hls(*mc.to_rgb(c))\n",
    "    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the switcing function\n",
    "`plumed plotswitch -h` will return:\n",
    "\n",
    "```\n",
    "Usage: plotswitch [options] \n",
    "\n",
    "\n",
    "The following arguments are compulsory: \n",
    "\n",
    "               --switch - ( default=RATIONAL NN=6 R_0=1.0 ) the input to pass to the \n",
    "                          switching function, please remeber the quotes \n",
    "                --steps - ( default=50 ) the number of steps between 0 and R_O, or in \n",
    "                          the specified interval \n",
    "                 --from - ( default=-1 ) the start of the interval, if negative will \n",
    "                          be set to 0 \n",
    "                   --to - ( default=-1 ) the end of the interval, will be D_MAX or \n",
    "                          2*R_0 if D_MAX is not set \n",
    "        --plotprecision - ( default=8 ) the precision to use for the tabulated \n",
    "                          results \n",
    "          --rationalR_0 - ( default=-1 ) The r_0 parameter of the switching function, \n",
    "                          this will activate the --rational options, note that this will \n",
    "                          ignore the --switch option silently \n",
    "           --rationalNN - ( default=6 ) The n parameter of the switching function \n",
    "           --rationalMM - ( default=0 ) The m parameter of the switching function; 0 \n",
    "                          implies 2*NN \n",
    "          --rationalD_0 - ( default=0.0 ) The d_0 parameter of the switching function \n",
    "          --centerrange - ( default=-1 ) centers the visualization in R_0 in a range \n",
    "                          given epsilons times r_0, note that specifying this will overide \n",
    "                          all the other range options \n",
    "\n",
    "In addition you may use the following options: \n",
    "\n",
    "              --help/-h - ( default=off ) print this help \n",
    "             --nosquare - ( default=off ) use calculate instead of calculateSqr \n",
    "```\n",
    "\n",
    "Now we will use plotswitch for visualizing the swithcing function that we are going to use\n",
    "\n",
    "```bash\n",
    "plumed plotswitch --switch=\"EXP D_0=1 R_0=0.25 D_MAX=2\" --steps=200 > ./run/switchExp.out\n",
    "plumed plotswitch --rationalR_0 --steps=200 > ./run/switchR_0eq1.out\n",
    "```\n",
    "This is useful to decide where to put the neighbourlist cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f89ebfc4490>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "switchexp=np.loadtxt(\"./run/switchExp.out\")\n",
    "rationalswitch=np.loadtxt(\"./run/switchR_0eq1.out\")\n",
    "\n",
    "plt.plot(switchexp[:,0],switchexp[:,1],label=\"SWITCH={EXP D_0=1 R_0=0.25 D_MAX=2}\")\n",
    "plt.plot(rationalswitch[:,0],rationalswitch[:,1],label=\"R_0=1\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The benchmark are run with the following plumed.dat file:\n",
    "\n",
    "```plumed\n",
    "cpu: COORDINATION GROUPA=@mdatoms R_0=1\n",
    "\n",
    "PRINT ARG=* FILE=Colvar FMT=%8.4f STRIDE=1\n",
    "\n",
    "FLUSH STRIDE=1\n",
    "\n",
    "```\n",
    "\n",
    "and we use the following script for the running the benchmark:\n",
    "```bash\n",
    "export PLUMED_MAXBACKUP=0\n",
    "cd run\n",
    "for nt in 1 2 4 6 8 10 12; do\n",
    "    export PLUMED_NUM_THREADS=$nt\n",
    "    for nat in 100 500 1000; do\n",
    "        plumed benchmark --nsteps=500 --natoms=$nat --atom-distribution=sc > \"./run/sc_${nt}_${nat}.out\"\n",
    "    done\n",
    "done\n",
    "```\n",
    "the plumed*.dat files are in the \"run\" directory of the support repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bash script to prepare the data to be plotted\n",
    "!for threads in 1 2 4 6 8 10 12; do \\\n",
    "    { for natoms in 100 500 1000; do \\\n",
    "        echo -n \"$natoms \";\\\n",
    "        fname=\"./run/sc_${threads}_${natoms}.out\";\\\n",
    "        sed -n '/PLUMED: *Cycles *Total *Average *Minimum *Maximum/{n ; p}' \"${fname}\" | \\\n",
    "        awk '{printf \"%f \", $3}';\\\n",
    "        echo \"\";\\\n",
    "        done;\\\n",
    "    } > \"./run/times_${threads}.out\";\\\n",
    "done\n",
    "\n",
    "simPerThread={}\n",
    "for threads in nthreads:\n",
    "    simPerThread[threads]=np.loadtxt(f\"./run/times_{threads}.out\")\n",
    "fig,ax =plt.subplots()\n",
    "\n",
    "ncols = len(simPerThread)\n",
    "x = simPerThread[nthreads[0]][:,0]\n",
    "x_coord=np.arange(len(x))\n",
    "width = 0.8/ncols\n",
    "ax.set_xticks(x_coord + width * 0.5 * (ncols - 1), x)\n",
    "\n",
    "for multiplier, nt in enumerate(nthreads):\n",
    "    offset = width * multiplier\n",
    "\n",
    "    toplot = simPerThread[nt][:,1]\n",
    "    ax.bar(\n",
    "            x_coord + offset,\n",
    "            toplot,\n",
    "            width,\n",
    "            label=f\"{nt} threads\",\n",
    "        )\n",
    "       \n",
    "ax.legend()\n",
    "ax.set_xlabel(\"number of Atoms\")\n",
    "_=ax.set_ylabel(\"time (s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simPerThread={}\n",
    "for threads in nthreads:\n",
    "    simPerThread[threads]=np.loadtxt(f\"./run/times_{threads}.out\")\n",
    "fig,ax =plt.subplots()\n",
    "\n",
    "ncols = len(simPerThread)\n",
    "x = simPerThread[nthreads[0]][:,0]\n",
    "x_coord=np.arange(len(x))\n",
    "width = 0.8/ncols\n",
    "ax.set_xticks(x_coord + width * 0.5 * (ncols - 1), x)\n",
    "\n",
    "for multiplier, nt in enumerate(nthreads):\n",
    "    offset = width * multiplier\n",
    "\n",
    "    toplot = simPerThread[nt][:,1]\n",
    "    ax.bar(\n",
    "            x_coord + offset,\n",
    "            toplot,\n",
    "            width,\n",
    "            label=f\"{nt} threads\",\n",
    "        )\n",
    "       \n",
    "ax.legend()\n",
    "ax.set_xlabel(\"number of Atoms\")\n",
    "ax.set_yscale(\"log\")\n",
    "_=ax.set_ylabel(\"[log]time (s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "benchmarks={}\n",
    "\n",
    "for threads in nthreads:\n",
    "    benchmarks[threads]=[]\n",
    "    for atoms in [100, 500, 1000]:\n",
    "        with open(f\"./run/sc_{threads}_{atoms}.out\") as f:\n",
    "            benchmarks[threads].append(parse_full_benchmark_output(f.readlines()))\n",
    "\n",
    "t=[]\n",
    "for threads in nthreads:\n",
    "    t.append(convert_to_table(benchmarks[threads],kernel=\"this\",inputlist=\".dat\",rows_to_extract=[plmdbppconst.TOTALTIME]))\n",
    "fig,axes =plt.subplots(2,1,sharex=True)\n",
    "plot_histo(axes[0],t,plmdbppconst.TOTALTIME, titles=[f\"{threads} threads\" for threads in nthreads],relative_to=0)\n",
    "plot_histo(axes[1],t,plmdbppconst.TOTALTIME, titles=[f\"{threads} threads\" for threads in nthreads])\n",
    "\n",
    "axes[1].set_xlabel(\"number of Atoms\")\n",
    "axes[1].legend()\n",
    "for ax in axes:\n",
    "    ax.set_ylabel(\"time (s)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "```bash\n",
    "export PLUMED_MAXBACKUP=0\n",
    "export PLUMED_NUM_THREADS=8\n",
    "cd run\n",
    "for nat in 500 1000 2000; do\n",
    "    plumed benchmark --nsteps=500 \\\n",
    "        --natoms=$nat \\\n",
    "        --plumed=\"plumed.dat:plumedNL110_shortstride.dat:plumedNL150_shortstride.dat:plumedNL200_shortstride.dat\" \\\n",
    "        --atom-distribution=sc > sc_NL_shortstride_${nat}.out\n",
    "\n",
    "    plumed benchmark --nsteps=500 \\\n",
    "        --natoms=$nat \\\n",
    "        --plumed=\"plumed.dat:plumedNL110.dat:plumedNL150.dat:plumedNL200.dat\" \\\n",
    "        --atom-distribution=sc > sc_NL_${nat}.out\n",
    "done\n",
    "\n",
    "```\n",
    "the plumed*.dat files are in the \"run\" directory of the support repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'time/(time no NL)')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "benchmarks=[]\n",
    "\n",
    "#preload the full files in memory\n",
    "for atoms in [500, 1000, 2000]:\n",
    "    with open(f\"./run/sc_NL_all_{atoms}.out\") as f:\n",
    "        benchmarks.append(parse_full_benchmark_output(f.readlines()))\n",
    "\n",
    "#extract the rows we want to plot from the preloaded files\n",
    "rows_to_extract=[plmdbppconst.TOTALTIME,plmdbppconst.CALCULATE]\n",
    "t=[]\n",
    "\n",
    "for perc in [110, 150, 200]:\n",
    "    t.append(convert_to_table(benchmarks,kernel=\"this\",inputlist=f\"plumedNL{perc}_shortstride.dat\",rows_to_extract=rows_to_extract))\n",
    "    t.append(convert_to_table(benchmarks,kernel=\"this\",inputlist=f\"plumedNL{perc}.dat\",rows_to_extract=rows_to_extract))\n",
    "t.append(convert_to_table(benchmarks,kernel=\"this\",inputlist=\"plumed.dat\",rows_to_extract=rows_to_extract))\n",
    "\n",
    "#these are the label of the columns\n",
    "NL=[\"110%, 10 steps\" , \"110%, 100 steps\", \"150%, 10 steps\", \"150%, 100 steps\", \"200%, 10 steps\", \"200%, 100 steps\",\"no\"]\n",
    "#plot the histogram with a fancy layout\n",
    "fig,ax =plt.subplots()\n",
    "colors=[f\"C{i}\" for i in range(1,len(t))]+[\"C0\"]\n",
    "lc=[lighten_color(c) for c in colors]\n",
    "plot_histo(ax,t,plmdbppconst.TOTALTIME, titles=[f\"NL={nl} \" for nl in NL],relative_to=-1,colors=colors) \n",
    "plot_histo(ax,t,plmdbppconst.CALCULATE, relative_to=-1, relative_to_row=plmdbppconst.TOTALTIME,colors=lc) \n",
    "\n",
    "ax.set_xlabel(\"number of Atoms\")\n",
    "ax.legend(ncols=4,loc=\"lower center\", bbox_to_anchor=(0.5, -0.35))\n",
    "ax.set_ylabel(\"time/(time no NL)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra cache:\n",
    "It felt strange to see that the calculate part of the simulation is different intuitively it should be identical, since \"sc\" is a fixed shaped system, so I added an extra run and I looked at the minimimum, the maximum and the average.\n",
    "\n",
    "I think the discrepancy is due to the cpu loading and unloading the instruction for the calculation of the NL or of the coordination that that could bring in cache misses.\n",
    "In these examples the mimimum and the maximum are quite similar, but the average is higher in the runs with the NL caclulated every fewer steps\n",
    "\n",
    "The bars span from minimum time to maximum time registered by the plumed timer.\n",
    "\n",
    "(the data has been loaded by the previous cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t=[]\n",
    "t.append(convert_to_table(benchmarks,\n",
    "                 kernel=\"this\",\n",
    "                 inputlist=\"plumedNL110_shortstride.dat\",\n",
    "                 rows_to_extract=[plmdbppconst.CALCULATE]))\n",
    "t.append(convert_to_table(benchmarks,\n",
    "                 kernel=\"this\",\n",
    "                 inputlist=\"plumedNL110_mediumstride.dat\",\n",
    "                 rows_to_extract=[plmdbppconst.CALCULATE]))\n",
    "t.append(convert_to_table(benchmarks,\n",
    "                 kernel=\"this\",\n",
    "                 inputlist=\"plumedNL110.dat\",\n",
    "                 rows_to_extract=[plmdbppconst.CALCULATE]))\n",
    "fig,axes =plt.subplots(2,1,sharex=True)\n",
    "\n",
    "ncols = 3\n",
    "x = [500,1000,2000]\n",
    "x_coord=np.arange(len(x))\n",
    "width = 0.8/ncols\n",
    "Labels=[\"nl@110%, 10 steps\", \"nl@110%,50 steps\", \"nl@110%, 100 steps\"]\n",
    "for ax in axes:\n",
    "    ax.set_xticks(x_coord + width * 0.5 * (ncols - 1), x)\n",
    "    for multiplier, df in enumerate(t):\n",
    "        offset = width * multiplier\n",
    "\n",
    "        toplot = simPerThread[nt][:,1]\n",
    "        ax.bar(\n",
    "                x_coord + offset,\n",
    "                df[plmdbppconst.CALCULATE].Maximum.values-df[plmdbppconst.CALCULATE].Minimum.values,\n",
    "                width,\n",
    "                bottom=df[plmdbppconst.CALCULATE].Minimum.values,\n",
    "                color=[f\"C{multiplier}\"],\n",
    "                label=Labels[multiplier]\n",
    "                )\n",
    "        ax.bar(\n",
    "                x_coord + offset,\n",
    "                0,\n",
    "                width,\n",
    "                bottom=df[plmdbppconst.CALCULATE].Average.values,\n",
    "                fill=False,\n",
    "                label=\"average\" if multiplier==2 else None\n",
    "                )\n",
    "axes[0].set_ylabel(\"time (s)\")\n",
    "axes[1].set_ylabel(\"log time (s)\")\n",
    "axes[1].set_xlabel(\"number of Atoms\")\n",
    "axes[1].set_yscale(\"log\")\n",
    "_=axes[0].legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "plot",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}