crusaderky/tcp_bench.ipynb

## tcp_bench.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b504f018-cd4f-40b7-a6e6-b50d8e15c588",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pickle\n",
    "import numpy\n",
    "from dask.sizeof import sizeof\n",
    "from dask.utils import format_bytes\n",
    "from distributed.comm import connect\n",
    "from distributed.comm.tcp import TCPListener\n",
    "from distributed.protocol.serialize import to_serialize\n",
    "from distributed.shuffle._buffer import _List\n",
    "\n",
    "\n",
    "serializers = {\n",
    "    None: (lambda p: p, lambda p: p),\n",
    "    \"whole\": (pickle.dumps, pickle.loads),\n",
    "    \"elements\": (\n",
    "        lambda p: type(p)(map(pickle.dumps, p)),\n",
    "        lambda p: type(p)(map(pickle.loads, p)),\n",
    "    ),\n",
    "}\n",
    "\n",
    "\n",
    "async def handle_comm(comm):\n",
    "    while True:\n",
    "        msg = await comm.read()\n",
    "        if msg[\"op\"] == \"close\":\n",
    "            await comm.close()\n",
    "            return\n",
    "\n",
    "        s, d = serializers[msg[\"serialize\"]]\n",
    "        data = msg[\"data\"]\n",
    "        data = to_serialize(s(d(data)))\n",
    "        msg = {\"op\": \"pong\", \"data\": data}\n",
    "        await comm.write(msg)\n",
    "\n",
    "\n",
    "listener = await TCPListener(\"127.0.0.1\", handle_comm)\n",
    "comm = await connect(listener.contact_address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "91d5923e-7cba-4fad-aca7-f5be020c24d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def bench(label, payload, serialize):\n",
    "    nb = sum(map(sizeof, payload))\n",
    "\n",
    "    t0 = time.time()\n",
    "    N = 500\n",
    "    for _ in range(N):\n",
    "        s, d = serializers[serialize]\n",
    "        data = to_serialize(s(payload))\n",
    "\n",
    "        await comm.write({\"op\": \"ping\", \"serialize\": serialize, \"data\": data})\n",
    "        msg = await comm.read()\n",
    "        assert msg[\"op\"] == \"pong\"\n",
    "        data = d(msg[\"data\"])\n",
    "\n",
    "    t1 = time.time()\n",
    "    bandwidth = (2 * N * nb) / (t1 - t0) / 2**20\n",
    "    print(label, f\"{bandwidth:4.0f} MiB/s\")\n",
    "\n",
    "    assert len(payload) == len(data)\n",
    "    assert all((a == b).all() for a, b in zip(payload, data))\n",
    "    return bandwidth\n",
    "\n",
    "async def bench_suite(shard_size):\n",
    "    max_message_size = 2 * 2**20\n",
    "\n",
    "    payload = [numpy.random.random(shard_size // 8) for _ in range(max(1, max_message_size // shard_size))]\n",
    "    print(\"=\" * 80)\n",
    "    print(format_bytes(sum(map(sizeof, payload))), \"payload;\", len(payload), \"x\", format_bytes(shard_size), \"shards\")\n",
    "\n",
    "    return [\n",
    "        shard_size,\n",
    "        await bench(\"list[ndarray] \", payload, None),\n",
    "        await bench(\"_List[ndarray]\", _List(payload), None),\n",
    "        await bench(\"bytes         \", payload, \"whole\"),\n",
    "        await bench(\"list[bytes]   \", payload, \"elements\"),\n",
    "        await bench(\"_List[bytes]  \", _List(payload), \"elements\"),\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "083f74ec-abc6-46e3-a9c8-6fdafef28ed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "2.00 MiB payload; 1024 x 2.00 kiB shards\n",
      "list[ndarray]    51 MiB/s\n",
      "_List[ndarray]   70 MiB/s\n",
      "bytes           343 MiB/s\n",
      "list[bytes]      57 MiB/s\n",
      "_List[bytes]    157 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 512 x 4.00 kiB shards\n",
      "list[ndarray]   100 MiB/s\n",
      "_List[ndarray]  135 MiB/s\n",
      "bytes           598 MiB/s\n",
      "list[bytes]     109 MiB/s\n",
      "_List[bytes]    258 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 256 x 8.00 kiB shards\n",
      "list[ndarray]   191 MiB/s\n",
      "_List[ndarray]  264 MiB/s\n",
      "bytes           894 MiB/s\n",
      "list[bytes]     200 MiB/s\n",
      "_List[bytes]    417 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 128 x 16.00 kiB shards\n",
      "list[ndarray]   371 MiB/s\n",
      "_List[ndarray]  493 MiB/s\n",
      "bytes          1101 MiB/s\n",
      "list[bytes]     359 MiB/s\n",
      "_List[bytes]    577 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 64 x 32.00 kiB shards\n",
      "list[ndarray]   670 MiB/s\n",
      "_List[ndarray]  851 MiB/s\n",
      "bytes           819 MiB/s\n",
      "list[bytes]     584 MiB/s\n",
      "_List[bytes]    746 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 32 x 64.00 kiB shards\n",
      "list[ndarray]  1067 MiB/s\n",
      "_List[ndarray] 1282 MiB/s\n",
      "bytes          1339 MiB/s\n",
      "list[bytes]     834 MiB/s\n",
      "_List[bytes]    859 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 16 x 128.00 kiB shards\n",
      "list[ndarray]  1559 MiB/s\n",
      "_List[ndarray] 1777 MiB/s\n",
      "bytes          1414 MiB/s\n",
      "list[bytes]    1073 MiB/s\n",
      "_List[bytes]    872 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 8 x 256.00 kiB shards\n",
      "list[ndarray]  2061 MiB/s\n",
      "_List[ndarray] 2190 MiB/s\n",
      "bytes          1452 MiB/s\n",
      "list[bytes]    1313 MiB/s\n",
      "_List[bytes]    952 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 4 x 512.00 kiB shards\n",
      "list[ndarray]  2508 MiB/s\n",
      "_List[ndarray] 2563 MiB/s\n",
      "bytes          1589 MiB/s\n",
      "list[bytes]    1403 MiB/s\n",
      "_List[bytes]    931 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 2 x 1.00 MiB shards\n",
      "list[ndarray]  2815 MiB/s\n",
      "_List[ndarray] 2810 MiB/s\n",
      "bytes          1487 MiB/s\n",
      "list[bytes]    1549 MiB/s\n",
      "_List[bytes]    924 MiB/s\n",
      "================================================================================\n",
      "2.00 MiB payload; 1 x 2.00 MiB shards\n",
      "list[ndarray]  2953 MiB/s\n",
      "_List[ndarray] 2863 MiB/s\n",
      "bytes          1564 MiB/s\n",
      "list[bytes]    1664 MiB/s\n",
      "_List[bytes]   1012 MiB/s\n",
      "================================================================================\n",
      "4.00 MiB payload; 1 x 4.00 MiB shards\n",
      "list[ndarray]  3232 MiB/s\n",
      "_List[ndarray] 3178 MiB/s\n",
      "bytes           937 MiB/s\n",
      "list[bytes]     943 MiB/s\n",
      "_List[bytes]    600 MiB/s\n",
      "================================================================================\n",
      "8.00 MiB payload; 1 x 8.00 MiB shards\n",
      "list[ndarray]  2997 MiB/s\n",
      "_List[ndarray] 2945 MiB/s\n",
      "bytes           671 MiB/s\n",
      "list[bytes]     658 MiB/s\n",
      "_List[bytes]    439 MiB/s\n",
      "================================================================================\n",
      "16.00 MiB payload; 1 x 16.00 MiB shards\n",
      "list[ndarray]  3207 MiB/s\n",
      "_List[ndarray] 3129 MiB/s\n",
      "bytes           660 MiB/s\n",
      "list[bytes]     660 MiB/s\n",
      "_List[bytes]    433 MiB/s\n"
     ]
    }
   ],
   "source": [
    "shard_size = 2048\n",
    "rows = []\n",
    "while shard_size <= 16 * 2**20:\n",
    "    row = await bench_suite(shard_size)\n",
    "    rows.append(row)\n",
    "    shard_size *= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c4ae109-a76c-4e67-80f2-5385f2da55cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "await comm.write({\"op\": \"close\"})\n",
    "await comm.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5cec89e5-8793-4c5c-889c-4657e01ad16e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>list[ndarray]</th>\n",
       "      <th>opaque list[ndarray]</th>\n",
       "      <th>bytes</th>\n",
       "      <th>list[bytes]</th>\n",
       "      <th>opaque list[bytes]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shard size (kiB)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.903776</td>\n",
       "      <td>69.728695</td>\n",
       "      <td>343.086332</td>\n",
       "      <td>57.066776</td>\n",
       "      <td>157.222938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100.316773</td>\n",
       "      <td>134.653209</td>\n",
       "      <td>598.372030</td>\n",
       "      <td>109.309751</td>\n",
       "      <td>258.107037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>190.839629</td>\n",
       "      <td>263.591943</td>\n",
       "      <td>894.373489</td>\n",
       "      <td>200.283609</td>\n",
       "      <td>417.440357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>371.209654</td>\n",
       "      <td>493.385479</td>\n",
       "      <td>1100.924116</td>\n",
       "      <td>358.662300</td>\n",
       "      <td>576.616526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>670.387683</td>\n",
       "      <td>851.325741</td>\n",
       "      <td>818.691276</td>\n",
       "      <td>583.892921</td>\n",
       "      <td>746.006159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1067.119197</td>\n",
       "      <td>1281.562256</td>\n",
       "      <td>1338.790461</td>\n",
       "      <td>833.731683</td>\n",
       "      <td>858.750140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1558.516878</td>\n",
       "      <td>1777.398838</td>\n",
       "      <td>1413.686997</td>\n",
       "      <td>1073.416605</td>\n",
       "      <td>872.072096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2060.841518</td>\n",
       "      <td>2190.007782</td>\n",
       "      <td>1451.755557</td>\n",
       "      <td>1313.176872</td>\n",
       "      <td>952.193650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>2508.483760</td>\n",
       "      <td>2562.543932</td>\n",
       "      <td>1588.998893</td>\n",
       "      <td>1402.692111</td>\n",
       "      <td>930.712748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>2814.930399</td>\n",
       "      <td>2809.793638</td>\n",
       "      <td>1486.866258</td>\n",
       "      <td>1548.600527</td>\n",
       "      <td>924.047949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>2952.556268</td>\n",
       "      <td>2862.814638</td>\n",
       "      <td>1564.160716</td>\n",
       "      <td>1664.199645</td>\n",
       "      <td>1012.460643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4096</th>\n",
       "      <td>3232.150866</td>\n",
       "      <td>3177.575850</td>\n",
       "      <td>936.658378</td>\n",
       "      <td>943.470656</td>\n",
       "      <td>599.556940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8192</th>\n",
       "      <td>2996.758480</td>\n",
       "      <td>2945.381580</td>\n",
       "      <td>670.755918</td>\n",
       "      <td>658.465733</td>\n",
       "      <td>438.512926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16384</th>\n",
       "      <td>3206.737483</td>\n",
       "      <td>3128.812223</td>\n",
       "      <td>659.806899</td>\n",
       "      <td>659.605696</td>\n",
       "      <td>433.148824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  list[ndarray]  opaque list[ndarray]        bytes  \\\n",
       "shard size (kiB)                                                     \n",
       "2                     50.903776             69.728695   343.086332   \n",
       "4                    100.316773            134.653209   598.372030   \n",
       "8                    190.839629            263.591943   894.373489   \n",
       "16                   371.209654            493.385479  1100.924116   \n",
       "32                   670.387683            851.325741   818.691276   \n",
       "64                  1067.119197           1281.562256  1338.790461   \n",
       "128                 1558.516878           1777.398838  1413.686997   \n",
       "256                 2060.841518           2190.007782  1451.755557   \n",
       "512                 2508.483760           2562.543932  1588.998893   \n",
       "1024                2814.930399           2809.793638  1486.866258   \n",
       "2048                2952.556268           2862.814638  1564.160716   \n",
       "4096                3232.150866           3177.575850   936.658378   \n",
       "8192                2996.758480           2945.381580   670.755918   \n",
       "16384               3206.737483           3128.812223   659.806899   \n",
       "\n",
       "                  list[bytes]  opaque list[bytes]  \n",
       "shard size (kiB)                                   \n",
       "2                   57.066776          157.222938  \n",
       "4                  109.309751          258.107037  \n",
       "8                  200.283609          417.440357  \n",
       "16                 358.662300          576.616526  \n",
       "32                 583.892921          746.006159  \n",
       "64                 833.731683          858.750140  \n",
       "128               1073.416605          872.072096  \n",
       "256               1313.176872          952.193650  \n",
       "512               1402.692111          930.712748  \n",
       "1024              1548.600527          924.047949  \n",
       "2048              1664.199645         1012.460643  \n",
       "4096               943.470656          599.556940  \n",
       "8192               658.465733          438.512926  \n",
       "16384              659.605696          433.148824  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.DataFrame(rows, columns=[\n",
    "    \"shard size (kiB)\",\n",
    "    \"list[ndarray]\",\n",
    "    \"opaque list[ndarray]\",\n",
    "    \"bytes\",\n",
    "    \"list[bytes]\",\n",
    "    \"opaque list[bytes]\",\n",
    "])\n",
    "df[\"shard size (kiB)\"] //= 1024\n",
    "df[\"shard size (kiB)\"] = df[\"shard size (kiB)\"].astype(str)\n",
    "df = df.set_index(\"shard size (kiB)\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "492eb846-7ba4-4496-b3e5-f34991b64120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='shard size (kiB)', ylabel='Bandwidth (MiB/s)'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(grid=True, ylabel=\"Bandwidth (MiB/s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa0f5f20-326a-4d70-9919-15e1532a64f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}