StrongWeakScaling.ipynb

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     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "MOLNs Benchmarks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook contains some of the benchmarks conducted for the paper. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import IPython.parallel\n",
      "from molnsutil import DistributedEnsemble\n",
      "rc = IPython.parallel.Client()\n",
      "view = rc.load_balanced_view()\n",
      "import pyurdme\n",
      "import matplotlib as mpl\n",
      "mpl.rc(\"savefig\", dpi=120)\n",
      "print \"MOLNs cluster running with number of engines: {0}\".format(len(rc.ids))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MOLNs cluster running with number of engines: 48\n"
       ]
      }
     ],
     "prompt_number": 2
    },
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     "input": [
      "import pyurdme\n",
      "class SimpleDiffusion(pyurdme.URDMEModel):\n",
      "    \"\"\" This Hello World model follows a single\n",
      "        species A diffusing on a unit square. Initially,  \n",
      "        all N molecules are placed in the voxel closest \n",
      "        to the center of the domain and we simulate how \n",
      "        A spread by diffusion. This model is useful for benchmarking, \n",
      "        since it is easy to control the ratio of compute/data-I/O\n",
      "        through the model paramters. \n",
      "    \"\"\"\n",
      "    \n",
      "    def __init__(self, N=5000,D=0.01):\n",
      "        \n",
      "        pyurdme.URDMEModel.__init__(self,name=\"simple_diffusion\")\n",
      "\n",
      "        A = pyurdme.Species(name=\"A\",diffusion_constant=D)\n",
      "        B = pyurdme.Species(name=\"B\",diffusion_constant=D)\n",
      "        C = pyurdme.Species(name=\"C\",diffusion_constant=D)\n",
      "\n",
      "        self.add_species([A,B,C])\n",
      "        \n",
      "       # R1 = pyurdme.Reaction(name=\"R1\", reactants={\"A\":1, \"B\":1}, products={\"C\":1}, rate)\n",
      "        \n",
      "        # A unit square mesh\n",
      "        self.mesh = pyurdme.URDMEMesh.generate_cube_mesh(1,10,10,10)\n",
      "        \n",
      "        # Place the A molecules in the voxel nearest the center of the square\n",
      "        self.set_initial_condition_scatter({A:N})\n",
      "        self.set_initial_condition_scatter({B:N})\n",
      "        self.set_initial_condition_scatter({C:N})\n",
      "        \n",
      "        # Set the time frame for the study to [0,1], with results reported each 1e-3 seconds. \n",
      "        self.timespan(numpy.linspace(0,10,1001))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We first execute pyurdme on all the engines once, so that the model definition will be available on all engines. This is not necessary for typical jobs, only for our customized storage throughput experiments below.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We run it once so that the class will be enabled on all the engines.\n",
      "ensemble = DistributedEnsemble(model_class=SimpleDiffusion)\n",
      "res  = ensemble.add_realizations(number_of_realizations=100)\n",
      "res['wall_time']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Generating 100 realizations of the model (chunk size=2)\n"
       ]
      },
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       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "111.301017"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_engines = len(rc.ids)\n",
      "engines = []\n",
      "while num_engines > 1:\n",
      "    engines.append(num_engines)\n",
      "    num_engines=num_engines/2\n",
      "engines.append(1)\n",
      "engines.reverse()\n",
      "print engines"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 3, 6, 12, 24, 48]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Clean up\n",
      "from molnsutil import PersistentStorage\n",
      "ps = PersistentStorage()\n",
      "ps.delete_all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
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     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Generating a distributed ensemble and Computing the mean value of a function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def g(result):\n",
      "    \"\"\" Extract the spatial distribution of species A at the final timepoint. \"\"\"\n",
      "    import numpy\n",
      "    A = result.get_species(\"A\",-1)\n",
      "    return A"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Strong scaling - the total work is constant and the number of engines increase (parallel efficiency)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We conduct scaling experiments by sequnentially doubling the number of engines used. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using the sshf SharedStorage"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "wall_time_strong_phase1 = []\n",
      "wall_time_strong_phase2 = []\n",
      "for num_engines in engines:\n",
      "    # Phase1 - generate all the realizations and write them to disk\n",
      "    ensemble = DistributedEnsemble(model_class=SimpleDiffusion,num_engines=num_engines)\n",
      "    tic = time.time()\n",
      "    res  = ensemble.add_realizations(number_of_realizations=100)\n",
      "    wall_time_1 = time.time()-tic\n",
      "    wall_time_strong_phase1.append(wall_time_1)\n",
      "    # Phase 2 - read all the realizations and compute the mean value\n",
      "    tic = time.time()\n",
      "    res = ensemble.mean(mapper=g)\n",
      "    wall_time_2 = time.time()-tic\n",
      "    wall_time_strong_phase2.append(wall_time_2)\n",
      "    \n",
      "    print \"Number of engines: %i\" % num_engines\n",
      "    print \"\\tPhase1, wall_time: %f\" % wall_time_1\n",
      "    print \"\\tPhase2, wall_time: %f\" % wall_time_2\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "stream": "stdout",
       "text": [
        "Generating 100 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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       "text": [
        "Running mapper & aggregator on the result objects (number of results=100, chunk size=100)\n"
       ]
      },
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       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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       "text": [
        "Running reducer on mapped and aggregated results (size=1)\n",
        "Number of engines: 1\n",
        "\tPhase1, wall_time: 540.544954\n",
        "\tPhase2, wall_time: 173.648684\n",
        "Generating 100 realizations of the model (chunk size=33)\n"
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        "Running mapper & aggregator on the result objects (number of results=100, chunk size=33)\n"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=4)\n",
        "Number of engines: 3\n",
        "\tPhase1, wall_time: 225.992203\n",
        "\tPhase2, wall_time: 78.098442\n",
        "Generating 100 realizations of the model (chunk size=17)\n"
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      },
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        "Number of engines: 6\n",
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        "\tPhase1, wall_time: 241.871217\n",
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        "Number of engines: 24\n",
        "\tPhase1, wall_time: 182.559441\n",
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       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=50)\n",
        "Number of engines: 48\n",
        "\tPhase1, wall_time: 60.407032\n",
        "\tPhase2, wall_time: 15.838594\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
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     "input": [
      "# print engines\n",
      "# print wall_time_strong_phase1\n",
      "# print wall_time_strong_phase2\n",
      "# plt.plot(engines, wall_time_strong_phase1, \".-b\",label=\"Phase1 - run pyurdme\")\n",
      "# plt.plot(engines, wall_time_strong_phase2, \".-g\",label=\"Phase2 - run postprocessing\")\n",
      "# total = numpy.array(wall_time_strong_phase1)+numpy.array(wall_time_strong_phase2)\n",
      "# plt.plot(engines, total, \".-r\",label=\"With storage\")\n",
      "# plt.legend()\n",
      "# plt.xlabel(\"Number of engines\")\n",
      "# plt.ylabel(\"Wall time (s)\")\n",
      "# plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
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     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using the object store - PersistentStorage"
     ]
    },
    {
     "cell_type": "code",
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     "input": [
      "import time\n",
      "wall_time_strong_persistent_phase1 = []\n",
      "wall_time_strong_persistent_phase2 = []\n",
      "for num_engines in engines:\n",
      "    # Phase1 - generate all the realizations and write them to disk\n",
      "    ensemble = DistributedEnsemble(model_class=SimpleDiffusion,num_engines=num_engines)\n",
      "    tic = time.time()\n",
      "    res  = ensemble.add_realizations(number_of_realizations=100, storage_mode=\"Persistent\")\n",
      "    wall_time_1 = time.time()-tic\n",
      "    wall_time_strong_persistent_phase1.append(wall_time_1)\n",
      "    # Phase 2 - read all the realizations and compute the mean value\n",
      "    tic = time.time()\n",
      "    res = ensemble.mean(mapper=g)\n",
      "    wall_time_2 = time.time()-tic\n",
      "    wall_time_strong_persistent_phase2.append(wall_time_2)\n",
      "    \n",
      "    print \"Number of engines: %i\" % num_engines\n",
      "    print \"\\tPhase1, wall_time: %f\" % wall_time_1\n",
      "    print \"\\tPhase2, wall_time: %f\" % wall_time_2\n",
      "\n"
     ],
     "language": "python",
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        "Generating 100 realizations of the model (chunk size=100)\n"
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      },
      {
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       "text": [
        "Running reducer on mapped and aggregated results (size=1)\n",
        "Number of engines: 1\n",
        "\tPhase1, wall_time: 103.126317\n",
        "\tPhase2, wall_time: 22.808222\n",
        "Generating 100 realizations of the model (chunk size=33)\n"
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        "Running mapper & aggregator on the result objects (number of results=100, chunk size=33)\n"
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       "text": [
        "Running reducer on mapped and aggregated results (size=4)\n",
        "Number of engines: 3\n",
        "\tPhase1, wall_time: 41.506279\n",
        "\tPhase2, wall_time: 8.733227\n",
        "Generating 100 realizations of the model (chunk size=17)\n"
       ]
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        "Running reducer on mapped and aggregated results (size=6)\n",
        "Number of engines: 6\n",
        "\tPhase1, wall_time: 23.200049\n",
        "\tPhase2, wall_time: 6.791694\n",
        "Generating 100 realizations of the model (chunk size=8)\n"
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       "text": [
        "<IPython.core.display.Javascript at 0x88ea210>"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88ea0d0>"
       ]
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      {
       "javascript": [
        "$('div#37dca5b4-e956-4c70-96c6-322d8fa6cf88').width('98.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88ea090>"
       ]
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      {
       "javascript": [
        "$('div#37dca5b4-e956-4c70-96c6-322d8fa6cf88').width('100.000000%')"
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        "<IPython.core.display.Javascript at 0x88ea9d0>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=50)\n",
        "Number of engines: 48\n",
        "\tPhase1, wall_time: 14.136925\n",
        "\tPhase2, wall_time: 2.779321\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using No-Storage"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "wall_time_no_storage = []\n",
      "for num_engines in engines:\n",
      "    tic = time.time()\n",
      "    ensemble = DistributedEnsemble(model_class=SimpleDiffusion,num_engines=num_engines)\n",
      "    res  = ensemble.mean(mapper=g, number_of_realizations=100,store_realizations=False)\n",
      "    wall_time = time.time()-tic\n",
      "    wall_time_no_storage.append(wall_time)\n",
      "    print \"Number of engines: %i\" % num_engines\n",
      "    print \"\\tPhase1, wall_time: %f\" % wall_time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Generating 100 realizations of the model, running mapper & aggregator (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"791ad2de-24eb-411c-ac97-cda91341e3ac\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x87c5b10>"
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      },
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       ],
       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x87c5b90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=1)\n",
        "Number of engines: 1\n",
        "\tPhase1, wall_time: 84.269283\n",
        "Generating 100 realizations of the model, running mapper & aggregator (chunk size=33)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x87c5950>"
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      },
      {
       "javascript": [
        "$('div#eb188b31-94e6-42fd-b24d-a53504cb01f8').width('50.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x87c5210>"
       ]
      },
      {
       "javascript": [
        "$('div#eb188b31-94e6-42fd-b24d-a53504cb01f8').width('75.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x87c5d50>"
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      },
      {
       "javascript": [
        "$('div#eb188b31-94e6-42fd-b24d-a53504cb01f8').width('100.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x87c5a50>"
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      },
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=4)\n",
        "Number of engines: 3\n",
        "\tPhase1, wall_time: 35.528046\n",
        "Generating 100 realizations of the model, running mapper & aggregator (chunk size=17)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
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      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"6b4e2376-6620-4597-bbff-ccd650c953e7\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
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       "text": [
        "<IPython.core.display.HTML at 0x799d210>"
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      {
       "javascript": [
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x87c5a50>"
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      {
       "javascript": [
        "$('div#6b4e2376-6620-4597-bbff-ccd650c953e7').width('66.666667%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x87c53d0>"
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      {
       "javascript": [
        "$('div#6b4e2376-6620-4597-bbff-ccd650c953e7').width('83.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88d5190>"
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      {
       "javascript": [
        "$('div#6b4e2376-6620-4597-bbff-ccd650c953e7').width('100.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x88ea6d0>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=6)\n",
        "Number of engines: 6\n",
        "\tPhase1, wall_time: 21.598229\n",
        "Generating 100 realizations of the model, running mapper & aggregator (chunk size=8)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=50)\n",
        "Number of engines: 48\n",
        "\tPhase1, wall_time: 13.201192\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print engines\n",
      "print wall_time_strong_persistent_phase1\n",
      "print wall_time_strong_persistent_phase2\n",
      "print wall_time_strong_phase1\n",
      "print wall_time_strong_phase2\n",
      "print wall_time_no_storage\n",
      "#plt.plot(engines, wall_time_strong_persistent_phase1, label=\"Phase1 - run pyurdme\")\n",
      "#plt.plot(engines, wall_time_strong_persistent_phase2, label=\"Phase2 - run postprocessing\")\n",
      "total = numpy.array(wall_time_strong_persistent_phase1)+numpy.array(wall_time_strong_persistent_phase2)\n",
      "plt.plot(engines, total, \".-b\", label=\"PersistentStorage\")\n",
      "#plt.plot(engines, wall_time_strong_phase1, label=\"Phase1 - run pyurdme\")\n",
      "#plt.plot(engines, wall_time_strong_phase2, label=\"Phase2 - run postprocessing\")\n",
      "total = numpy.array(wall_time_strong_phase1)+numpy.array(wall_time_strong_phase2)\n",
      "plt.plot(engines, total, \".-g\", label=\"SharedStorage\")\n",
      "plt.plot(engines, wall_time_no_storage, \".-r\", label=\"No storage\")\n",
      "plt.legend()\n",
      "plt.xlabel(\"Number of engines\")\n",
      "plt.ylabel(\"Wall time (s)\")\n",
      "plt.title(\"Strong Scaling [HP-to-EC2]\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 3, 6, 12, 24, 48]\n",
        "[103.12631702423096, 41.5062792301178, 23.20004892349243, 21.18203902244568, 15.490678071975708, 14.136924982070923]\n",
        "[22.8082218170166, 8.733227014541626, 6.791694164276123, 6.919352054595947, 4.319998025894165, 2.7793209552764893]\n",
        "[540.5449540615082, 225.99220299720764, 314.3098120689392, 241.8712170124054, 182.55944108963013, 60.40703201293945]\n",
        "[173.6486840248108, 78.09844207763672, 41.431931018829346, 44.514524936676025, 26.220016956329346, 15.838594198226929]\n",
        "[84.26928305625916, 35.52804613113403, 21.598228931427002, 19.38456892967224, 14.032283067703247, 13.201192140579224]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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kn9e6usCNG0BDPWPxvueTJDpTJUrQrly5gr59+6KgoAAeHh4YPnw4njx5goiI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\nJNU+Z0JCg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       "text": [
        "<matplotlib.figure.Figure at 0x90f6f10>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Clean up\n",
      "from molnsutil import PersistentStorage\n",
      "ps = PersistentStorage()\n",
      "ps.delete_all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'content-length': '0', 'x-container-object-count': '0', 'accept-ranges': 'bytes', 'date': 'Sun, 28 Dec 2014 08:21:52 GMT', 'connection': 'keep-alive', 'x-timestamp': '1419751607.83834', 'x-trans-id': 'txf708a05e6e484bf892611-00549fbda0', 'x-container-bytes-used': '0', 'content-type': 'text/plain; charset=utf-8'}\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Weak scaling - The work per engine stays constant as the number of engines increase"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "PersistentStorage"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "wall_time_weak_persistent_phase1 = []\n",
      "wall_time_weak_persistent_phase2 = []\n",
      "for num_engines in engines:\n",
      "    # Phase1 - generate all the realizations and write them to disk\n",
      "    ensemble = DistributedEnsemble(model_class=SimpleDiffusion,num_engines=num_engines)\n",
      "    tic = time.time()\n",
      "    res  = ensemble.add_realizations(number_of_realizations=100*num_engines, storage_mode=\"Persistent\")\n",
      "    wall_time_1 = time.time()-tic\n",
      "    wall_time_weak_persistent_phase1.append(wall_time_1)\n",
      "    # Phase 2 - read all the realizations and compute the mean value\n",
      "    tic = time.time()\n",
      "    res = ensemble.mean(mapper=g)\n",
      "    wall_time_2 = time.time()-tic\n",
      "    wall_time_weak_persistent_phase2.append(wall_time_2)\n",
      "    \n",
      "    print \"Number of engines: %i\" % num_engines\n",
      "    print \"\\tPhase1, wall_time: %f\" % wall_time_1\n",
      "    print \"\\tPhase2, wall_time: %f\" % wall_time_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Generating 100 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"67cbec99-2344-4deb-8224-2a60fdb6d46e\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x90ed290>"
       ]
      },
      {
       "javascript": [
        "$('div#67cbec99-2344-4deb-8224-2a60fdb6d46e').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90ed7d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=100, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"29fe1d0a-d76c-48d9-a0dd-7a210154e146\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x90edb50>"
       ]
      },
      {
       "javascript": [
        "$('div#29fe1d0a-d76c-48d9-a0dd-7a210154e146').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90ed710>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=1)\n",
        "Number of engines: 1\n",
        "\tPhase1, wall_time: 104.083920\n",
        "\tPhase2, wall_time: 22.586556\n",
        "Generating 300 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"b839242e-4180-4eb5-ba90-3dbbe6cde843\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x785b650>"
       ]
      },
      {
       "javascript": [
        "$('div#b839242e-4180-4eb5-ba90-3dbbe6cde843').width('33.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90ede90>"
       ]
      },
      {
       "javascript": [
        "$('div#b839242e-4180-4eb5-ba90-3dbbe6cde843').width('66.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90edb10>"
       ]
      },
      {
       "javascript": [
        "$('div#b839242e-4180-4eb5-ba90-3dbbe6cde843').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90ed090>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=300, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"f85b0947-479d-4891-86e8-05340d684e23\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x90ed950>"
       ]
      },
      {
       "javascript": [
        "$('div#f85b0947-479d-4891-86e8-05340d684e23').width('33.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90ed110>"
       ]
      },
      {
       "javascript": [
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       "text": [
        "<IPython.core.display.Javascript at 0x90edf10>"
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      },
      {
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        "$('div#f85b0947-479d-4891-86e8-05340d684e23').width('100.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90ed050>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=3)\n",
        "Number of engines: 3\n",
        "\tPhase1, wall_time: 106.777289\n",
        "\tPhase2, wall_time: 26.093824\n",
        "Generating 600 realizations of the model (chunk size=100)"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
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        "<IPython.core.display.Javascript at 0x90edb50>"
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        "<IPython.core.display.Javascript at 0x90ed950>"
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       "text": [
        "<IPython.core.display.Javascript at 0x90edb10>"
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      {
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        "$('div#91147caa-5315-4c1c-9964-7c24b7f3a7c0').width('100.000000%')"
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       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90ed710>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=600, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "<IPython.core.display.Javascript at 0x90ed590>"
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      {
       "javascript": [
        "$('div#823a686e-7b93-41b8-b22d-23f92f02d0ee').width('66.666667%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x90ed110>"
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      },
      {
       "javascript": [
        "$('div#823a686e-7b93-41b8-b22d-23f92f02d0ee').width('83.333333%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90edad0>"
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      {
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        "<IPython.core.display.Javascript at 0x90eddd0>"
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=6)\n",
        "Number of engines: 6\n",
        "\tPhase1, wall_time: 111.314981\n",
        "\tPhase2, wall_time: 41.577717\n",
        "Generating 1200 realizations of the model (chunk size=100)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "\n",
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        "                          </div>\n",
        "                          "
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       "text": [
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      {
       "javascript": [
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       "metadata": {},
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      {
       "javascript": [
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90ed090>"
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      },
      {
       "javascript": [
        "$('div#3883161a-6529-4305-ab55-3085bff953cf').width('25.000000%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90edf10>"
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      },
      {
       "javascript": [
        "$('div#3883161a-6529-4305-ab55-3085bff953cf').width('33.333333%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x90edb10>"
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      {
       "javascript": [
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90ed950>"
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      },
      {
       "javascript": [
        "$('div#3883161a-6529-4305-ab55-3085bff953cf').width('50.000000%')"
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       "metadata": {},
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       "text": [
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      {
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       "javascript": [
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      {
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       "metadata": {},
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      {
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       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x87c5210>"
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      {
       "javascript": [
        "$('div#3883161a-6529-4305-ab55-3085bff953cf').width('91.666667%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x87c5390>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=1200, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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      {
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        "<IPython.core.display.Javascript at 0x79cd8d0>"
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      {
       "javascript": [
        "$('div#95d2c675-61d3-4030-a2c5-b30dc5aec776').width('41.666667%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x79cd650>"
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      {
       "javascript": [
        "$('div#95d2c675-61d3-4030-a2c5-b30dc5aec776').width('50.000000%')"
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        "<IPython.core.display.Javascript at 0x90f6350>"
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      {
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6e50>"
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      },
      {
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        "$('div#95d2c675-61d3-4030-a2c5-b30dc5aec776').width('66.666667%')"
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       "metadata": {},
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        "<IPython.core.display.Javascript at 0x90f6d10>"
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      },
      {
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6910>"
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      },
      {
       "javascript": [
        "$('div#95d2c675-61d3-4030-a2c5-b30dc5aec776').width('83.333333%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6990>"
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      },
      {
       "javascript": [
        "$('div#95d2c675-61d3-4030-a2c5-b30dc5aec776').width('91.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6a90>"
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      },
      {
       "javascript": [
        "$('div#95d2c675-61d3-4030-a2c5-b30dc5aec776').width('100.000000%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6f10>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=12)\n",
        "Number of engines: 12\n",
        "\tPhase1, wall_time: 132.278606\n",
        "\tPhase2, wall_time: 78.499103\n",
        "Generating 2400 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "Running reducer on mapped and aggregated results (size=24)\n",
        "Number of engines: 24\n",
        "\tPhase1, wall_time: 144.492300\n",
        "\tPhase2, wall_time: 105.528640\n",
        "Generating 4800 realizations of the model (chunk size=100)\n"
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        "Running reducer on mapped and aggregated results (size=48)\n",
        "Number of engines: 48\n",
        "\tPhase1, wall_time: 186.581307\n",
        "\tPhase2, wall_time: 105.526238\n"
       ]
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      "import time\n",
      "wall_time_weak_shared_phase1 = []\n",
      "wall_time_weak_shared_phase2 = []\n",
      "for num_engines in engines:\n",
      "    # Phase1 - generate all the realizations and write them to disk\n",
      "    ensemble = DistributedEnsemble(model_class=SimpleDiffusion, num_engines=num_engines)\n",
      "    tic = time.time()\n",
      "    res  = ensemble.add_realizations(number_of_realizations=100*num_engines)\n",
      "    wall_time_1 = time.time()-tic\n",
      "    wall_time_weak_shared_phase1.append(wall_time_1)\n",
      "    # Phase 2 - read all the realizations and compute the mean value\n",
      "    tic = time.time()\n",
      "    res = ensemble.mean(mapper=g)\n",
      "    wall_time_2 = time.time()-tic\n",
      "    wall_time_weak_shared_phase2.append(wall_time_2)\n",
      "    \n",
      "    print \"Number of engines: %i\" % num_engines\n",
      "    print \"\\tPhase1, wall_time: %f\" % wall_time_1\n",
      "    print \"\\tPhase2, wall_time: %f\" % wall_time_2"
     ],
     "language": "python",
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        "Generating 100 realizations of the model (chunk size=100)\n"
       ]
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        "<IPython.core.display.Javascript at 0x90f6390>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=1)\n",
        "Number of engines: 1\n",
        "\tPhase1, wall_time: 467.490409\n",
        "\tPhase2, wall_time: 156.227194\n",
        "Generating 300 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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        "<IPython.core.display.Javascript at 0x90f6350>"
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        "<IPython.core.display.Javascript at 0x90f6750>"
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      {
       "javascript": [
        "$('div#73469e67-6ee0-4259-9fe3-20c215a7a14b').width('100.000000%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f60d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=300, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
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       "text": [
        "<IPython.core.display.HTML at 0x90f6e10>"
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      },
      {
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        "$('div#927f2568-abba-4a5d-8800-def34f4ee92a').width('33.333333%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6bd0>"
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      },
      {
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        "$('div#927f2568-abba-4a5d-8800-def34f4ee92a').width('66.666667%')"
       ],
       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6f10>"
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      },
      {
       "javascript": [
        "$('div#927f2568-abba-4a5d-8800-def34f4ee92a').width('100.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6fd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=3)\n",
        "Number of engines: 3\n",
        "\tPhase1, wall_time: 843.858171\n",
        "\tPhase2, wall_time: 176.683970\n",
        "Generating 600 realizations of the model (chunk size=100)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "\n",
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        "                          </div>\n",
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6f90>"
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      },
      {
       "javascript": [
        "$('div#90bb2e38-217e-4861-9fa1-d0386827a2ff').width('66.666667%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6e10>"
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      },
      {
       "javascript": [
        "$('div#90bb2e38-217e-4861-9fa1-d0386827a2ff').width('83.333333%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6350>"
       ]
      },
      {
       "javascript": [
        "$('div#90bb2e38-217e-4861-9fa1-d0386827a2ff').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6390>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=600, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"dc2bc98d-1dc6-4930-97db-4e329c0e4df8\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
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       "metadata": {},
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       "text": [
        "<IPython.core.display.HTML at 0x90f6250>"
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      },
      {
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       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6910>"
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      },
      {
       "javascript": [
        "$('div#dc2bc98d-1dc6-4930-97db-4e329c0e4df8').width('33.333333%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6c50>"
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      },
      {
       "javascript": [
        "$('div#dc2bc98d-1dc6-4930-97db-4e329c0e4df8').width('50.000000%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6f10>"
       ]
      },
      {
       "javascript": [
        "$('div#dc2bc98d-1dc6-4930-97db-4e329c0e4df8').width('66.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6e50>"
       ]
      },
      {
       "javascript": [
        "$('div#dc2bc98d-1dc6-4930-97db-4e329c0e4df8').width('83.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6710>"
       ]
      },
      {
       "javascript": [
        "$('div#dc2bc98d-1dc6-4930-97db-4e329c0e4df8').width('100.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6150>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=6)\n",
        "Number of engines: 6\n",
        "\tPhase1, wall_time: 1480.690241\n",
        "\tPhase2, wall_time: 221.874367\n",
        "Generating 1200 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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      },
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6d10>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('16.666667%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x90f60d0>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('25.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6710>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('33.333333%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6e50>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('41.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6f10>"
       ]
      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('50.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6250>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('58.333333%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6390>"
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      {
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6e10>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('75.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6f90>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('83.333333%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6510>"
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      },
      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('91.666667%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6d90>"
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      {
       "javascript": [
        "$('div#5c8a6801-b01a-4443-8a3d-90f444363c6e').width('100.000000%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f63d0>"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running mapper & aggregator on the result objects (number of results=1200, chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "<IPython.core.display.Javascript at 0x90f6910>"
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6f50>"
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      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('41.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6550>"
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      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('50.000000%')"
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       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6310>"
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      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('58.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f67d0>"
       ]
      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('66.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f66d0>"
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      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('75.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6850>"
       ]
      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('83.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6410>"
       ]
      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('91.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f6290>"
       ]
      },
      {
       "javascript": [
        "$('div#9564b6bb-4745-42be-8f7a-fe59e8f0dd93').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x90f9fd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=12)\n",
        "Number of engines: 12\n",
        "\tPhase1, wall_time: 2531.417186\n",
        "\tPhase2, wall_time: 430.246002\n",
        "Generating 2400 realizations of the model (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
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        "                          </div>\n",
        "                          "
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      {
       "javascript": [
        "$('div#886f54f3-101c-4819-9ccf-f69e31f1fcfa').width('12.500000%')"
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        "<IPython.core.display.Javascript at 0x90f6950>"
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      {
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        "<IPython.core.display.Javascript at 0x90f6350>"
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      },
      {
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       "text": [
        "<IPython.core.display.Javascript at 0x90f6210>"
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      {
       "javascript": [
        "$('div#886f54f3-101c-4819-9ccf-f69e31f1fcfa').width('29.166667%')"
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       "metadata": {},
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        "<IPython.core.display.Javascript at 0x90f63d0>"
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      {
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        "<IPython.core.display.Javascript at 0x90f6510>"
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      {
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      {
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        "<IPython.core.display.Javascript at 0x90f6e10>"
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      {
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    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Running without storage"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import time\n",
      "wall_time_weak_no_storage = []\n",
      "for num_engines in engines:\n",
      "    tic = time.time()\n",
      "    ensemble = DistributedEnsemble(model_class=SimpleDiffusion,num_engines=num_engines)\n",
      "    res  = ensemble.mean(mapper=g, number_of_realizations=100*num_engines, store_realizations=False)\n",
      "    wall_time = time.time()-tic\n",
      "    wall_time_weak_no_storage.append(wall_time)\n",
      "    print \"Number of engines: %i\" % num_engines\n",
      "    print \"\\tPhase1, wall_time: %f\" % wall_time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Generating 100 realizations of the model, running mapper & aggregator (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"07d5beed-a21d-48e5-b1c2-eef825a136f3\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x88ea1d0>"
       ]
      },
      {
       "javascript": [
        "$('div#07d5beed-a21d-48e5-b1c2-eef825a136f3').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eabd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=1)\n",
        "Number of engines: 1\n",
        "\tPhase1, wall_time: 83.823786\n",
        "Generating 300 realizations of the model, running mapper & aggregator (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"1e339d7c-e86b-479e-903d-853f77fd1bf1\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x90eac90>"
       ]
      },
      {
       "javascript": [
        "$('div#1e339d7c-e86b-479e-903d-853f77fd1bf1').width('33.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88ea490>"
       ]
      },
      {
       "javascript": [
        "$('div#1e339d7c-e86b-479e-903d-853f77fd1bf1').width('66.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eaa50>"
       ]
      },
      {
       "javascript": [
        "$('div#1e339d7c-e86b-479e-903d-853f77fd1bf1').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eac10>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=3)\n",
        "Number of engines: 3\n",
        "\tPhase1, wall_time: 92.160085\n",
        "Generating 600 realizations of the model, running mapper & aggregator (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x88ea310>"
       ]
      },
      {
       "javascript": [
        "$('div#24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e').width('16.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88ea810>"
       ]
      },
      {
       "javascript": [
        "$('div#24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e').width('33.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eaa50>"
       ]
      },
      {
       "javascript": [
        "$('div#24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e').width('50.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eac10>"
       ]
      },
      {
       "javascript": [
        "$('div#24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e').width('66.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88ea6d0>"
       ]
      },
      {
       "javascript": [
        "$('div#24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e').width('83.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a590>"
       ]
      },
      {
       "javascript": [
        "$('div#24eb9f48-bcc7-4dc6-a2ab-d6ef7c4dd02e').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a5d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=6)\n",
        "Number of engines: 6\n",
        "\tPhase1, wall_time: 102.851588\n",
        "Generating 1200 realizations of the model, running mapper & aggregator (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"e7093eb7-adf6-4b3d-bb76-cd8f01bb7970\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x90eac90>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('8.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a610>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('16.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996ab50>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('25.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eaa50>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('33.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88ea810>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('41.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x88eac10>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('50.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a0d0>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('58.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996ab50>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('66.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a690>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('75.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a890>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('83.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a450>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('91.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a150>"
       ]
      },
      {
       "javascript": [
        "$('div#e7093eb7-adf6-4b3d-bb76-cd8f01bb7970').width('100.000000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a910>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=12)\n",
        "Number of engines: 12\n",
        "\tPhase1, wall_time: 117.126704\n",
        "Generating 2400 realizations of the model, running mapper & aggregator (chunk size=100)\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"24f4a68d-c457-4612-823e-37287e8e4908\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
        "                          "
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x88ea650>"
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      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('4.166667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996af10>"
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      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('8.333333%')"
       ],
       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x996a390>"
       ]
      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('12.500000%')"
       ],
       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x996ae50>"
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      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('16.666667%')"
       ],
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      {
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      {
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       "text": [
        "<IPython.core.display.Javascript at 0x996aed0>"
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      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('29.166667%')"
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        "<IPython.core.display.Javascript at 0x996a4d0>"
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      {
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        "<IPython.core.display.Javascript at 0x996a490>"
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      },
      {
       "javascript": [
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      {
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        "<IPython.core.display.Javascript at 0x996af50>"
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      {
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       "text": [
        "<IPython.core.display.Javascript at 0x996af90>"
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      {
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       "text": [
        "<IPython.core.display.Javascript at 0x996a310>"
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      {
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      {
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x996a050>"
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      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('70.833333%')"
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       "text": [
        "<IPython.core.display.Javascript at 0x996a950>"
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      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('75.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x996a990>"
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      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('79.166667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a8d0>"
       ]
      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('83.333333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a810>"
       ]
      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('87.500000%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a7d0>"
       ]
      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('91.666667%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a410>"
       ]
      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('95.833333%')"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x996a490>"
       ]
      },
      {
       "javascript": [
        "$('div#24f4a68d-c457-4612-823e-37287e8e4908').width('100.000000%')"
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       "metadata": {},
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       "text": [
        "<IPython.core.display.Javascript at 0x996a4d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Running reducer on mapped and aggregated results (size=24)\n",
        "Number of engines: 24\n",
        "\tPhase1, wall_time: 131.171291\n",
        "Generating 4800 realizations of the model, running mapper & aggregator (chunk size=100)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "\n",
        "                          <div style=\"border: 1px solid black; width:500px\">\n",
        "                          <div id=\"a407f712-2a78-4e6d-9bdc-64af797ce721\" style=\"background-color:blue; width:0%\">&nbsp;</div>\n",
        "                          </div>\n",
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      {
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       "text": [
        "Running reducer on mapped and aggregated results (size=48)\n",
        "Number of engines: 48\n",
        "\tPhase1, wall_time: 131.968230\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print engines\n",
      "print wall_time_weak_persistent_phase1\n",
      "print wall_time_weak_persistent_phase2\n",
      "print wall_time_weak_shared_phase1\n",
      "print wall_time_weak_shared_phase2\n",
      "print wall_time_weak_no_storage\n",
      "\n",
      "total = numpy.array(wall_time_weak_shared_phase1) + numpy.array(wall_time_weak_shared_phase2)\n",
      "plt.plot(engines, total, label=\"SharedStorage\")\n",
      "total = numpy.array(wall_time_weak_persistent_phase1) + numpy.array(wall_time_weak_persistent_phase2)\n",
      "plt.plot(engines, total, label=\"PersistentStorage\")\n",
      "plt.plot(engines, wall_time_weak_no_storage, label=\"No storage\")\n",
      "plt.legend(loc='best')\n",
      "plt.xlabel(\"Number of engines\")\n",
      "plt.ylabel(\"Execution time\")\n",
      "plt.title(\"Weak Scaling [HP-to-EC2]\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 3, 6, 12, 24, 48]\n",
        "[104.08392000198364, 106.7772889137268, 111.31498122215271, 132.27860593795776, 144.49229979515076, 186.58130717277527]\n",
        "[22.58655595779419, 26.09382390975952, 41.57771682739258, 78.49910306930542, 105.52864003181458, 105.52623796463013]\n",
        "[467.4904088973999, 843.8581709861755, 1480.690241098404, 2531.417186021805, 3480.651920080185, 3179.606852054596]\n",
        "[156.22719407081604, 176.68396997451782, 221.87436699867249, 430.24600195884705, 586.5869789123535, 1073.220731973648]\n",
        "[83.82378602027893, 92.16008496284485, 102.85158777236938, 117.12670421600342, 131.17129111289978, 131.96823000907898]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.text.Text at 0x90f0450>"
       ]
      },
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       "metadata": {},
       "output_type": "display_data",
       "png": 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5qurl55k0iUhD491c05tAIgkAAADNQk3Nf8lcfZPAsjIiiaTu52QYohYtiAwN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\nAAAAaJf/BwKWdcHrepxQAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x996a950>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}