{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More efficient data movement with MPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just like [we did](memmap%20Broadcast.ipynb) manually with memmap,\n",
    "you can move data more efficiently with MPI by sending it to just one engine,\n",
    "and using MPI to broadcast it to the rest of the engines.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import socket\n",
    "import os, sys, re\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import ipyparallel as ipp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demo, I will connect to a cluster with engines started with MPI.\n",
    "\n",
    "One way to do so would be:\n",
    "\n",
    "    ipcluster start -n 64 --engines=MPI --profile mpi\n",
    "    \n",
    "In this directory is a docker-compose file to simulate multiple engine sets launched with MPI.\n",
    "\n",
    "I ran this example with a cluster on a 64-core remote VM,\n",
    "so communication between the client and controller is over the public internet,\n",
    "while communication between the controller and engines is local."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rc = ipp.Client(profile=\"mpi\")\n",
    "rc.wait_for_engines(64)\n",
    "eall = rc.broadcast_view(coalescing=True)\n",
    "root = rc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "root['a'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%px from mpi4py.MPI import COMM_WORLD as MPI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cn get a mapping of IPP rank to MPI rank, in case they mismatch.\n",
    "\n",
    "In recent-enough IPython Parallel,\n",
    "they usually don't because IPython engines request their MPI rank as their engine id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0,\n",
       " 1: 1,\n",
       " 2: 2,\n",
       " 3: 3,\n",
       " 4: 4,\n",
       " 5: 5,\n",
       " 6: 6,\n",
       " 7: 7,\n",
       " 8: 8,\n",
       " 9: 9,\n",
       " 10: 10,\n",
       " 11: 11,\n",
       " 12: 12,\n",
       " 13: 13,\n",
       " 14: 14,\n",
       " 15: 15,\n",
       " 16: 16,\n",
       " 17: 17,\n",
       " 18: 18,\n",
       " 19: 19,\n",
       " 20: 20,\n",
       " 21: 21,\n",
       " 22: 22,\n",
       " 23: 23,\n",
       " 24: 24,\n",
       " 25: 25,\n",
       " 26: 26,\n",
       " 27: 27,\n",
       " 28: 28,\n",
       " 29: 29,\n",
       " 30: 30,\n",
       " 31: 31,\n",
       " 32: 32,\n",
       " 33: 33,\n",
       " 34: 34,\n",
       " 35: 35,\n",
       " 36: 36,\n",
       " 37: 37,\n",
       " 38: 38,\n",
       " 39: 39,\n",
       " 40: 40,\n",
       " 41: 41,\n",
       " 42: 42,\n",
       " 43: 43,\n",
       " 44: 44,\n",
       " 45: 45,\n",
       " 46: 46,\n",
       " 47: 47,\n",
       " 48: 48,\n",
       " 49: 49,\n",
       " 50: 50,\n",
       " 51: 51,\n",
       " 52: 52,\n",
       " 53: 53,\n",
       " 54: 54,\n",
       " 55: 55,\n",
       " 56: 56,\n",
       " 57: 57,\n",
       " 58: 58,\n",
       " 59: 59,\n",
       " 60: 60,\n",
       " 61: 61,\n",
       " 62: 62,\n",
       " 63: 63}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpi_ranks = eall.apply_async(lambda : MPI.Get_rank()).get_dict()\n",
    "root_rank = root.apply_sync(lambda : MPI.Get_rank())\n",
    "mpi_ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sz = 512\n",
    "data = np.random.random((sz, sz))\n",
    "megabytes = data.nbytes // (1024 * 1024)\n",
    "megabytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3943864933941bbb65e6ad17e3c589f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_push:   0%|          | 0/64 [00:00<?, ?tasks/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 285 ms, sys: 94.4 ms, total: 379 ms\n",
      "Wall time: 4.49 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "ar = eall.push({'data': data}, block=False)\n",
    "ar.wait_interactive()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "@ipp.interactive\n",
    "def _bcast(key, root_rank):\n",
    "    \"\"\"function to run on engines as part of broadcast\"\"\"\n",
    "    g = globals()\n",
    "    obj = g.get(key, None)\n",
    "    obj = MPI.bcast(obj, root_rank)\n",
    "    g[key] = obj\n",
    "\n",
    "def broadcast(key, obj, dv, root, root_rank):\n",
    "    \"\"\"More efficient broadcast by doing push to root,\n",
    "    and MPI broadcast to other engines.\n",
    "    \n",
    "    Still O(N) messages, but all but one message is always small.\n",
    "    \"\"\"\n",
    "    root.push({key : obj}, block=False)\n",
    "    return dv.apply_async(_bcast, key, root_rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b668be04b0f84dec9ee390cd03d6c677",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "_bcast:   0%|          | 0/64 [00:00<?, ?tasks/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 252 ms, sys: 58.3 ms, total: 310 ms\n",
      "Wall time: 939 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ar = broadcast('data', data, eall, root, root_rank)\n",
    "ar.wait_interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can quickly check that everyone got the same data by computing its norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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