From c471525f720cc9c660eae246da14491576fbd038 Mon Sep 17 00:00:00 2001
From: Min RK <benjaminrk@gmail.com>
Date: Fri, 6 Aug 2021 15:59:31 +0200
Subject: [PATCH] example doc visualizing an AsyncResult's timestamps

---
 docs/source/examples/Index.ipynb             |   3 +-
 docs/source/examples/visualizing-tasks.ipynb | 848 +++++++++++++++++++
 2 files changed, 850 insertions(+), 1 deletion(-)
 create mode 100644 docs/source/examples/visualizing-tasks.ipynb

diff --git a/docs/source/examples/Index.ipynb b/docs/source/examples/Index.ipynb
index e6e3beadc..8b9d43252 100644
--- a/docs/source/examples/Index.ipynb
+++ b/docs/source/examples/Index.ipynb
@@ -25,6 +25,7 @@
     "* [Data Publication API](Data%20Publication%20API.ipynb)\n",
     "* [Parallel Magics](Parallel%20Magics.ipynb)\n",
     "* [Broadcast View](broadcast/Broadcast%20View.ipynb)\n",
+    "* [Visualizing AsyncResults](visualizing-tasks.ipynb)\n",
     "* [Broadcasting arrays with memmap](broadcast/memmap%20Broadcast.ipynb)\n",
     "* [Broadcasting arrays with MPI](broadcast/MPI%20Broadcast.ipynb)"
    ]
@@ -325,7 +326,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.6"
   },
   "nbsphinx": {
    "execute": "always"
diff --git a/docs/source/examples/visualizing-tasks.ipynb b/docs/source/examples/visualizing-tasks.ipynb
new file mode 100644
index 000000000..b265afc92
--- /dev/null
+++ b/docs/source/examples/visualizing-tasks.ipynb
@@ -0,0 +1,848 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c12b2b6a-f643-4cb9-b5b4-dca60086a8e7",
+   "metadata": {},
+   "source": [
+    "# Visualizing an AsyncResult in progress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91255a04-00e0-44d5-8817-e5346226b4ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipyparallel as ipp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4bd7ec9e-285a-4e40-bfdf-89290a5e0e35",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using existing profile dir: '/Users/minrk/.ipython/profile_default'\n",
+      "Sending output for ipcontroller-touchy-1628258116-xwps-77061 to /Users/minrk/.ipython/profile_default/log/ipcontroller-touchy-1628258116-xwps-77061.log\n",
+      "Starting 8 engines with <class 'ipyparallel.cluster.launcher.LocalEngineSetLauncher'>\n",
+      "Sending output for 0 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-0.log\n",
+      "Sending output for 1 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-1.log\n",
+      "Sending output for 2 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-2.log\n",
+      "Sending output for 3 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-3.log\n",
+      "Sending output for 4 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-4.log\n",
+      "Sending output for 5 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-5.log\n",
+      "Sending output for 6 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-6.log\n",
+      "Sending output for 7 to /Users/minrk/.ipython/profile_default/log/ipengine-touchy-1628258116-xwps-1628258117-mpma-7.log\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be8ded73187d47abb7855d4e883a5b3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/8 [00:00<?, ?engine/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cluster = ipp.Cluster()\n",
+    "n = 8\n",
+    "await cluster.start_cluster(8)\n",
+    "rc = await cluster.connect_client()\n",
+    "rc.wait_for_engines(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0ffe757-cd80-4876-ad30-1814b2f41789",
+   "metadata": {},
+   "source": [
+    "IPython Parallel tracks states of engines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6c6c0b3d-5168-4ed7-9d7f-b6eb024a705f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91bd409d9bd448e2bc23cd8b96c993e8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "work:   0%|          | 0/256 [00:00<?, ?tasks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "import random\n",
+    "\n",
+    "\n",
+    "v = rc.load_balanced_view()\n",
+    "\n",
+    "\n",
+    "def work(t):\n",
+    "    import time\n",
+    "\n",
+    "    time.sleep(t)\n",
+    "    return t\n",
+    "\n",
+    "\n",
+    "random.seed(1)\n",
+    "times = [0.1 + 0.1 * random.random() for i in range(256)]\n",
+    "\n",
+    "start_time = datetime.datetime.now(datetime.timezone.utc)\n",
+    "ar = v.map_async(work, times)\n",
+    "ar.wait_interactive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a868966-d6ce-4709-87c5-3cb178458caf",
+   "metadata": {},
+   "source": [
+    "We can view a summary of our performance efficiency\n",
+    "by comparing the 'wall time' (total time it took to compute the whole task and retrieve all results)\n",
+    "to the 'serial time' (total work time of all tasks on the engines)\n",
+    "\n",
+    "The parallel 'efficiency' is the ratio of the speedup to the number of engines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e6b4e32b-14b5-4b7d-8826-e8d81ffba2ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wall time  :    5.6s\n",
+      "Serial time:   39.7s\n",
+      "Speedup    :    7.1x / 8\n",
+      "Efficiency :   88.9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "wall_time = ar.wall_time\n",
+    "serial_time = ar.serial_time\n",
+    "speedup = serial_time / wall_time\n",
+    "n_engines = len(set(ar.engine_id))\n",
+    "efficiency = speedup / n_engines\n",
+    "\n",
+    "print(f\"wall time  : {wall_time:6.1f}s\")\n",
+    "print(f\"Serial time: {serial_time:6.1f}s\")\n",
+    "print(f\"Speedup    : {speedup:6.1f}x / {n_engines}\")\n",
+    "print(f\"Efficiency : {efficiency:7.1%}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0ef79eb-532d-4dbf-b29b-27b5d0f88e15",
+   "metadata": {},
+   "source": [
+    "We can collect the four timestamps and convert them to seconds since first submission,\n",
+    "and store these in a DataFrame for easier analysis\n",
+    "\n",
+    "- 'submitted' is when the message is created on the client to be sent to the engines\n",
+    "- 'started' is when it arrived on the engine and began processing\n",
+    "- 'completed' is when it completed on the engine\n",
+    "- 'received' is when the result arrived back on the client\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a6e82a95-fd8f-48c2-818e-99acc7acbee0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import datetime\n",
+    "\n",
+    "data = {\n",
+    "    \"engine_id\": ar.engine_id,\n",
+    "}\n",
+    "\n",
+    "for name in (\"submitted\", \"started\", \"completed\", \"received\"):\n",
+    "    data[name] = [(dt - start_time).total_seconds() for dt in ar[name]]\n",
+    "\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "df['prepare'] = df['submitted'] - df['submitted'].min()\n",
+    "df['schedule'] = df['started'] - df['submitted']\n",
+    "df['compute'] = df['completed'] - df['started']\n",
+    "df['reply'] = df['received'] - df['completed']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3de2696e-d561-4bac-8ea6-6067abbd93c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'tasks')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x444.984 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 384,
+       "width": 612
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "df.plot.barh(y=['prepare', 'schedule', 'compute', 'reply'], stacked=True, width=1)\n",
+    "plt.grid((True, False))\n",
+    "yticks = [0, len(df) // 2, len(df)]\n",
+    "plt.yticks(yticks, yticks);\n",
+    "plt.xlabel(\"seconds\")\n",
+    "plt.ylabel(\"tasks\")\n",
+    "# plt.ylim([0, len(df)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e41ad4e-9af8-4a46-a745-2f0aa4af87eb",
+   "metadata": {},
+   "source": [
+    "In this visualization, the blue indicates the time spent in the client preparing the tasks to be submitted.\n",
+    "It takes 200ms to prepare and send all of these mesages.\n",
+    "\n",
+    "The blue area is mostly pure overhead of IPython parallel,\n",
+    "and should be minimized. The same goes for red, which is the same thing, just in the other direction - delivering completed results from engines to the client.\n",
+    "\n",
+    "The orange indicates scheduling time - the time tasks are waiting in queues before starting.\n",
+    "There are two contributors to this:\n",
+    "\n",
+    "1. available compute (if there are more tasks than engines, tasks need to wait). Allocating more engines can help with this.\n",
+    "   The optimal scenario here is one engine per task, which would mean no task ever has to wait for busy engines.\n",
+    "   On the other hand, your engines will probably spend a lot of time idle, so that's lost of wasted resources.\n",
+    "2. Scheduler overhead - There's always a cost to delivering messages.\n",
+    "   There is currently no separate measure of when a task has been *assigned* to an engine,\n",
+    "   so it is difficult to measure this one except by comparing the time between completing one task and starting the next\n",
+    "   \n",
+    "The green is time spent actually working on tasks. This is all the 'real' work. In a perfect world, the graph would be all green, but that's never going to happen.\n",
+    "\n",
+    "The little bits of red are the overhead of delivering results back to the client.\n",
+    "That cost is usually very low."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca4b6af-36a4-4aca-8e45-7536bf43f2f9",
+   "metadata": {},
+   "source": [
+    "We can also visualize the work on the engines,\n",
+    "which should look like a solid block of color if there's no wasted time.\n",
+    "\n",
+    "The left and right edges are the times when the first task was created and the last result was received at the client (the total effective computation time).\n",
+    "\n",
+    "The y axis is the engine id, and the x axis is time since the beginning of the task.\n",
+    "Space in the graph indicates idle engines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d1d7a1ef-a541-4f17-b6ef-9bb9bd3a0138",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'engine id')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 720x444.984 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 401,
+       "width": 632
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y = df.engine_id\n",
+    "x = df.started\n",
+    "width = df.completed\n",
+    "height = np.ones(len(df))\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "from matplotlib.collections import PatchCollection\n",
+    "from matplotlib.patches import Rectangle\n",
+    "from matplotlib import cm\n",
+    "\n",
+    "color_map = mpl.cm.ScalarMappable(\n",
+    "    norm=mpl.colors.Normalize(vmin=0, vmax=df.compute.max()),\n",
+    "    cmap=cm.viridis,\n",
+    ")\n",
+    "\n",
+    "plt.colorbar(color_map, label=\"duration (s)\")\n",
+    "\n",
+    "\n",
+    "def working_rect(row):\n",
+    "    \"\"\"Create a rectangle representing one task\n",
+    "\n",
+    "    x = start time\n",
+    "    y = engine id\n",
+    "    width = duration\n",
+    "    height = 1\n",
+    "    color = duration\n",
+    "    \"\"\"\n",
+    "    duration = row.completed - row.started\n",
+    "    return Rectangle(\n",
+    "        (row.started, row.engine_id),\n",
+    "        width=duration,\n",
+    "        height=1,\n",
+    "        color=color_map.to_rgba(duration),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "rects = df.apply(working_rect, axis=1)\n",
+    "pc = PatchCollection(rects, match_original=True)\n",
+    "\n",
+    "ax = plt.gca()\n",
+    "ax.set_ylim(0, df.engine_id.max() + 1)\n",
+    "ax.set_xlim(0, df.received.max())\n",
+    "ax.add_collection(pc)\n",
+    "\n",
+    "last_send = df.submitted.max()\n",
+    "finished = df.received.max()\n",
+    "first_result = df.received.min()\n",
+    "half_done = df.received.quantile(0.5)\n",
+    "\n",
+    "ymin, ymax = ax.get_ylim()\n",
+    "\n",
+    "\n",
+    "def annotate_milestone(ax, t, label, color=\"red\", dark=False, top=True):\n",
+    "    \"\"\"Add a vertical line annotation for a milestone time\"\"\"\n",
+    "\n",
+    "    patch = plt.vlines(\n",
+    "        [t],\n",
+    "        *ax.get_ylim(),\n",
+    "        linewidth=4,\n",
+    "        color=color,\n",
+    "    )\n",
+    "    if top:\n",
+    "        xy = (t, ymax)\n",
+    "        xytext = (10, 10)\n",
+    "    else:\n",
+    "        xy = (t, ymin)\n",
+    "        xytext = (10, -30)\n",
+    "    full_label = f\"{label} ({t:.1f}s)\"\n",
+    "    ax.annotate(\n",
+    "        full_label,\n",
+    "        xy=xy,\n",
+    "        xytext=xytext,\n",
+    "        textcoords=\"offset points\",\n",
+    "        color=\"white\" if dark else \"black\",\n",
+    "        bbox=dict(\n",
+    "            boxstyle=\"round\",\n",
+    "            fc=color,\n",
+    "        ),\n",
+    "        arrowprops=dict(\n",
+    "            arrowstyle=\"wedge,tail_width=1.\",\n",
+    "            fc=color,\n",
+    "            patchA=None,\n",
+    "            patchB=patch,\n",
+    "            relpos=(0.2, 0.8),\n",
+    "            #         connectionstyle=\"arc3,rad=-0.1\",\n",
+    "        ),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "annotate_milestone(\n",
+    "    ax,\n",
+    "    df.submitted.max(),\n",
+    "    \"last task send\",\n",
+    "    color=(0.4, 0, 0.4),\n",
+    "    dark=True,\n",
+    ")\n",
+    "annotate_milestone(\n",
+    "    ax,\n",
+    "    df.completed.quantile(0.5),\n",
+    "    \"50% results computed\",\n",
+    "    color=\"black\",\n",
+    "    dark=True,\n",
+    ")\n",
+    "annotate_milestone(\n",
+    "    ax,\n",
+    "    df.completed.max(),\n",
+    "    \"All results computed\",\n",
+    "    color=\"black\",\n",
+    "    dark=True,\n",
+    ")\n",
+    "\n",
+    "annotate_milestone(\n",
+    "    ax,\n",
+    "    df.received.min(),\n",
+    "    \"first result arrives\",\n",
+    "    color=(1, 0.5, 0.5),\n",
+    "    top=False,\n",
+    ")\n",
+    "annotate_milestone(\n",
+    "    ax,\n",
+    "    df.received.quantile(0.5),\n",
+    "    \"50% results arrived\",\n",
+    "    color=(1, 0.5, 0.5),\n",
+    "    top=False,\n",
+    ")\n",
+    "annotate_milestone(\n",
+    "    ax,\n",
+    "    df.received.max(),\n",
+    "    \"All done\",\n",
+    "    color=(1, 0.5, 0.5),\n",
+    "    top=False,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# plt.vlines(\n",
+    "#     [first_result, half_done, finished],\n",
+    "#     *ax.get_ylim(),\n",
+    "#     linewidth=4,\n",
+    "#     color=\"red\",\n",
+    "# )\n",
+    "# plt.legend(loc=0)\n",
+    "plt.xlabel(\"seconds\")\n",
+    "plt.ylabel(\"engine id\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4ecbaf9-d541-482e-8749-62aaa8a2192d",
+   "metadata": {},
+   "source": [
+    "Sometimes in this example, a bubble of idle engines can be produced around when the last task is sent.\n",
+    "\n",
+    "This is caused when lots of results arrive in the client while the client is still trying to submit tasks.\n",
+    "Because of IPython Parallel's threaded implementation,\n",
+    "this can cause lots of contention,\n",
+    "and receiving lots of results can significantly affect overal performance if the client is still trying to assemble tasks to submit.\n",
+    "\n",
+    "This is an opportunity for [optimization in the client](https://github.com/ipython/ipyparallel/pull/534)!\n",
+    "\n",
+    "A similar visualization can be done with altair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a5ce43d6-2202-4fd6-a1d9-251d8572d380",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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0.13006899999999977, \"reply\": 0.0031449999999999534}, {\"engine_id\": 7, \"submitted\": 0.412532, \"started\": 5.397896, \"completed\": 5.577948, \"received\": 5.582566, \"prepare\": 0.409127, \"schedule\": 4.985364000000001, \"compute\": 0.18005199999999988, \"reply\": 0.004617999999999789}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import altair as alt\n",
+    "\n",
+    "alt.Chart(df).mark_rect().encode(\n",
+    "    x=\"started\",\n",
+    "    x2=\"completed\",\n",
+    "    y=\"engine_id\",\n",
+    "    y2=\"next_engine_id\",\n",
+    "    color=\"compute\",\n",
+    ").transform_calculate(\n",
+    "    next_engine_id=\"datum.engine_id + 1\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d5d9bda-a58b-43b9-9839-59811956a6f8",
+   "metadata": {},
+   "source": [
+    "We can also visualize 'load' as a measure of how busy the cluster is.\n",
+    "It's essentially collecting samples of the above plot by drawing lots of vertical lines,\n",
+    "and counting how many boxes are intersected instead of empty space.\n",
+    "\n",
+    "That is, for any given point in time, how many engines are working,\n",
+    "vs how many are waiting for their next task.\n",
+    "\n",
+    "We do this by sampling points in time and counting the number of 'active' tasks.\n",
+    "This is computed as the average value across a given window (default: sample every 10ms, plot the average load over 100ms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "bdabe160-5548-492d-910c-d1af39e88739",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 720x444.984 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 384,
+       "width": 599
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_load(asyncresult_df, samples=None, window=10):\n",
+    "    if samples is None:\n",
+    "        samples = min(int(1e2 * df['received'].max()), 1001)\n",
+    "\n",
+    "    timeline = np.linspace(0, df['received'].max(), samples)\n",
+    "    remaining = df\n",
+    "    working = []\n",
+    "    for t in timeline:\n",
+    "\n",
+    "        remaining = remaining[remaining.completed >= t]\n",
+    "        active = remaining[remaining.started < t]\n",
+    "        working.append(len(active))\n",
+    "\n",
+    "    s = pd.Series(working, index=timeline, name=\"load\")\n",
+    "    if window > 1:\n",
+    "        windowed = s.rolling(window).mean()\n",
+    "    else:\n",
+    "        windowed = s\n",
+    "    windowed.plot()\n",
+    "    plt.ylim(0, len(df.engine_id.unique()))\n",
+    "    plt.xlabel(\"seconds\")\n",
+    "    plt.ylabel(\"busy engines\")\n",
+    "    plt.xlim(0, timeline[-1])\n",
+    "    \n",
+    "plot_load(df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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