Benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "face0b17",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import TqdmExperimentalWarning\n",
    "import warnings\n",
    "warnings.filterwarnings(action='once')\n",
    "warnings.filterwarnings(\"ignore\", category=TqdmExperimentalWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "import torch\n",
    "\n",
    "import numpy as np\n",
    "import torchCompactRadius as tcr\n",
    "from torchCompactRadius import radiusSearch, volumeToSupport\n",
    "from torchCompactRadius.util import countUniqueEntries\n",
    "from torchCompactRadius.radiusNaive import radiusNaive, radiusNaiveFixed\n",
    "from torchCompactRadius.cppWrapper import neighborSearchSmall, neighborSearchSmallFixed\n",
    "import platform\n",
    "import pandas as pd\n",
    "import time\n",
    "from tqdm.autonotebook import tqdm\n",
    "import copy\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9eb631f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import os\n",
    "import platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfc09466",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ddbdcb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "py310_torch201_cu117\n"
     ]
    }
   ],
   "source": [
    "version = platform.python_version()\n",
    "\n",
    "variant = 'py' + ''.join(version.split(\".\")[:-1]) + '_torch' + torch.__version__.split(\"+\")[0].replace(\".\", \"\") + '_cu' + torch.version.cuda.replace(\".\", \"\")\n",
    "\n",
    "print(variant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "38c241e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.1\n",
      "11.7\n"
     ]
    }
   ],
   "source": [
    "print(torch.__version__)\n",
    "print(torch.version.cuda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f8216da",
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = '/home/winchenbach/dev/torchCompactRadius/src/torchCompactRadius/prebuilt'\n",
    "variant = 'py' + ''.join(version.split(\".\")[:-1]) + '_torch' + torch.__version__.split(\"+\")[0].replace(\".\", \"\") + '_cpu'\n",
    "file = os.path.join(directory, 'prebuilt') + variant + '.so'\n",
    "if os.path.exists(file):\n",
    "    torch.ops.load_library(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "83dd7cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'torch.ops.countNeighbors' from 'torch.ops'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ops.countNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "160fabc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateNeighborTestData(nx, targetNumNeighbors, dim, maxDomain_0, periodic, device):\n",
    "\n",
    "\n",
    "    minDomain = torch.tensor([-1] * dim, dtype = torch.float32, device = device)\n",
    "    maxDomain = torch.tensor([ 1] * dim, dtype = torch.float32, device = device)\n",
    "    maxDomain[0] = maxDomain_0\n",
    "    periodicity = [periodic] * dim\n",
    "\n",
    "    extent = maxDomain - minDomain\n",
    "    shortExtent = torch.min(extent, dim = 0)[0].item()\n",
    "    dx = (shortExtent / nx)\n",
    "    ny = int(1 // dx)\n",
    "    h = volumeToSupport(dx**dim, targetNumNeighbors, dim)\n",
    "    dy = dx\n",
    "\n",
    "    # print(f\"dx = {dx}, dy = {dy}, h = {h}\")\n",
    "    # print(f\"nx = {nx}, ny = {ny}\")\n",
    "    # print(f\"minDomain = {minDomain}, maxDomain = {maxDomain}\")\n",
    "    # print(f\"periodicity = {periodicity}\")\n",
    "    # print(f\"dim = {dim}\")\n",
    "    # print(f\"device = {device}\")\n",
    "    # print(f\"maxDomain_0 = {maxDomain_0}\")\n",
    "    # print(f\"targetNumNeighbors = {targetNumNeighbors}\")\n",
    "    \n",
    "\n",
    "    positions = []\n",
    "    for d in range(dim):\n",
    "        positions.append(torch.linspace(minDomain[d] + dx / 2, maxDomain[d] - dx / 2, int((extent[d] - dx) / dx) + 1, device = device))\n",
    "    grid = torch.meshgrid(*positions, indexing = 'xy')\n",
    "    positions = torch.stack(grid, dim = -1).reshape(-1,dim).to(device)\n",
    "    supports = torch.ones(positions.shape[0], device = device) * h\n",
    "    ypositions = []\n",
    "    for d in range(dim):\n",
    "        ypositions.append(torch.linspace(-0.5 + dy / 2, 0.5 - dy / 2, ny, device = device))\n",
    "    grid = torch.meshgrid(*ypositions, indexing = 'xy')\n",
    "    y = torch.stack(grid, dim = -1).reshape(-1,dim).to(device)\n",
    "    ySupport = torch.ones(y.shape[0], device = device) * supports[0]\n",
    "    return (y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, positions.shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82c62871",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "if platform.system() == 'Darwin':\n",
    "    device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')\n",
    "device = torch.device('cpu')\n",
    "targetNumNeighbors = 50\n",
    "hashMapLength = 4096\n",
    "nx = 32\n",
    "dim = 2\n",
    "periodic = False\n",
    "\n",
    "(y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength = generateNeighborTestData(nx, targetNumNeighbors, dim, 1.0, periodic, device)\n",
    "for i in range(256):\n",
    "    (i_cpu, j_cpu), neighborDict = neighborSearch((y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength, 'scatter', 'cpp')\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_cluster import radius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d701f409",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62eea36c",
   "metadata": {},
   "outputs": [],
   "source": [
    "(y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength = generateNeighborTestData(nx, targetNumNeighbors, dim, 1.0, False, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9d987223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([False, False])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.tensor(periodicity).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bc16d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def callRadiusNaive(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials):\n",
    "    for i in range(numTrials):\n",
    "        r = radiusNaive(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, mode)\n",
    "    return r\n",
    "\n",
    "    # i, j = neighborSearchSmall(y, ySupport, positions, supports, minDomain, maxDomain, periodicTensor, 'symmetric')\n",
    "\n",
    "def callRadiusSmall(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials):\n",
    "    for i in range(numTrials):\n",
    "        r = neighborSearchSmall(y, ySupport, positions, supports, minDomain, maxDomain, torch.tensor(periodicity).to(device), mode)\n",
    "    return r\n",
    "\n",
    "def callRadiusCompact(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials):\n",
    "    for i in range(numTrials):\n",
    "        r = neighborSearch((y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength, mode, 'cpp')\n",
    "    return r\n",
    "\n",
    "def callRadius(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials):\n",
    "    for i in range(numTrials):\n",
    "        (i,j) = radius(y, positions, supports[0], max_num_neighbors = 384)\n",
    "    return (j,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79bc5893",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Union, Tuple, Optional, List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "515d7e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "def radiusSearch( \n",
    "        queryPositions : torch.Tensor,\n",
    "        referencePositions : Optional[torch.Tensor],\n",
    "        support : Union[float, torch.Tensor,Tuple[torch.Tensor, torch.Tensor]],\n",
    "        mode : str = 'symmetric',\n",
    "        domainMin : Optional[torch.Tensor] = None,\n",
    "        domainMax : Optional[torch.Tensor] = None,\n",
    "        periodicity : Optional[Union[bool, List[bool]]] = False,\n",
    "        hashMapLength = 4096,\n",
    "        algorithm: str = 'naive',\n",
    "        verbose: bool = True\n",
    "        ):\n",
    "    assert algorithm in ['naive', 'small', 'compact'], f'algorithm = {algorithm} not supported'\n",
    "    assert mode in ['symmetric', 'scatter', 'gather'], f'mode = {mode} not supported'\n",
    "    assert queryPositions.shape[1] == referencePositions.shape[1] if referencePositions is not None else True, f'queryPositions.shape[1] = {queryPositions.shape[1]} != referencePositions.shape[1] = {referencePositions.shape[1]}'\n",
    "    assert hashMapLength > 0, f'hashMapLength = {hashMapLength} <= 0'\n",
    "    assert len(periodicity) == queryPositions.shape[1] if isinstance(periodicity, list) else True, f'len(periodicity) = {len(periodicity)} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "    assert domainMin.shape[0] == queryPositions.shape[1] if domainMin is not None else True, f'domainMin.shape[0] = {domainMin.shape[0]} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "    assert domainMax.shape[0] == queryPositions.shape[1] if domainMax is not None else True, f'domainMax.shape[0] = {domainMax.shape[0]} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "    assert isinstance(support, float) or support.shape[0] == queryPositions.shape[0] if isinstance(support, torch.Tensor) else True, f'support.shape[0] = {support.shape[0]} != queryPositions.shape[0] = {queryPositions.shape[0]}'\n",
    "    assert support[0].shape[0] == queryPositions.shape[0] if isinstance(support, tuple) else True, f'support[0].shape[0] = {support[0].shape[0]} != queryPositions.shape[0] = {queryPositions.shape[0]}'\n",
    "    assert support[1].shape[0] == referencePositions.shape[0] if isinstance(support, tuple) else True, f'support[1].shape[0] = {support[1].shape[0]} != referencePositions.shape[0] = {referencePositions.shape[0]}'\n",
    "\n",
    "\n",
    "\n",
    "    if referencePositions is None:\n",
    "        referencePositions = queryPositions\n",
    "\n",
    "    if isinstance(support, float):\n",
    "        supportRadius = support\n",
    "        querySupport = None\n",
    "        referenceSupport = None\n",
    "    elif isinstance(support, torch.Tensor):\n",
    "        supportRadius = None\n",
    "        querySupport = support\n",
    "        if mode == 'gather':\n",
    "            referenceSupport = torch.zeros(referencePositions.shape[0], device = referencePositions.device)\n",
    "        assert mode == 'gather', f'mode = {mode} != gather'\n",
    "        assert querySupport.shape[0] == queryPositions.shape[0], f'querySupport.shape[0] = {querySupport.shape[0]} != queryPositions.shape[0] = {queryPositions.shape[0]}'\n",
    "    elif isinstance(support, tuple):\n",
    "        supportRadius = None\n",
    "        querySupport = support[0]\n",
    "        referenceSupport = support[1]\n",
    "        assert querySupport.shape[0] == queryPositions.shape[0], f'querySupport.shape[0] = {querySupport.shape[0]} != queryPositions.shape[0] = {queryPositions.shape[0]}'\n",
    "        assert referenceSupport.shape[0] == referencePositions.shape[0], f'referenceSupport.shape[0] = {referenceSupport.shape[0]} != referencePositions.shape[0] = {referencePositions.shape[0]}'\n",
    "    if periodicity is not None:\n",
    "        if isinstance(periodicity, bool):\n",
    "            periodicTensor = [periodicity] * queryPositions.shape[1]\n",
    "            if periodicity:\n",
    "                assert domainMin is not None, f'domainMin = {domainMin} is None'\n",
    "                assert domainMax is not None, f'domainMax = {domainMax} is None'\n",
    "                assert domainMin.shape[0] == queryPositions.shape[1], f'domainMin.shape[0] = {domainMin.shape[0]} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "                assert domainMax.shape[0] == queryPositions.shape[1], f'domainMax.shape[0] = {domainMax.shape[0]} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "        else:\n",
    "            periodicTensor = periodicity\n",
    "            assert len(periodicTensor) == queryPositions.shape[1], f'len(periodicTensor) = {len(periodicTensor)} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "            if np.any(periodicTensor):\n",
    "                assert domainMin is not None, f'domainMin = {domainMin} is None'\n",
    "                assert domainMax is not None, f'domainMax = {domainMax} is None'\n",
    "                assert domainMin.shape[0] == queryPositions.shape[1], f'domainMin.shape[0] = {domainMin.shape[0]} != queryPositions.shape[1] = {queryPositions.shape[1]}'\n",
    "                assert domainMax.shape[0] == queryPositions.shape[1], f'domainMax.shape[0] = {domainMax.shape[0]} != queryPositions.shape[1] = {queryPositions.shape[1]}'    \n",
    "    if supportRadius is not None:\n",
    "        if algorithm == 'naive':\n",
    "            if verbose:\n",
    "                print('Calling radiusNaiveFixed, arguments:')\n",
    "                print(f'queryPositions = {queryPositions.shape} on {queryPositions.device}')\n",
    "                print(f'referencePositions = {referencePositions.shape} on {referencePositions.device}')\n",
    "                print(f'supportRadius = {supportRadius}')\n",
    "                print(f'periodicTensor = {periodicTensor}')\n",
    "                print(f'minDomain = {minDomain.shape} on {minDomain.device}')\n",
    "                print(f'maxDomain = {maxDomain.shape} on {maxDomain.device}')\n",
    "            return radiusNaiveFixed(queryPositions, referencePositions, supportRadius, periodicTensor, minDomain, maxDomain)\n",
    "        elif algorithm == 'small':\n",
    "            if verbose:\n",
    "                print('Calling neighborSearchSmallFixed, arguments:')\n",
    "                print(f'queryPositions = {queryPositions.shape} on {queryPositions.device}')\n",
    "                print(f'referencePositions = {referencePositions.shape} on {referencePositions.device}')\n",
    "                print(f'supportRadius = {supportRadius}')\n",
    "                print(f'minDomain = {minDomain.shape} on {minDomain.device}')\n",
    "                print(f'maxDomain = {maxDomain.shape} on {maxDomain.device}')\n",
    "                print(f'periodicTensor = {periodicTensor}')\n",
    "            return neighborSearchSmallFixed(queryPositions, referencePositions, supportRadius, minDomain, maxDomain, torch.tensor(periodicTensor).to(queryPositions.device))\n",
    "        elif algorithm == 'compact':\n",
    "            if verbose:\n",
    "                print('Calling neighborSearch, arguments:')\n",
    "                print(f'queryPositions = {queryPositions.shape} on {queryPositions.device}')\n",
    "                print(f'referencePositions = {referencePositions.shape} on {referencePositions.device}')\n",
    "                print(f'support = {support}')\n",
    "                print(f'minDomain = {minDomain.shape} on {minDomain.device}')\n",
    "                print(f'maxDomain = {maxDomain.shape} on {maxDomain.device}')\n",
    "                print(f'periodicity = {periodicity}')\n",
    "                print(f'hashMapLength = {hashMapLength}')\n",
    "                print(f'mode = {mode}')\n",
    "            return neighborSearch((queryPositions, referencePositions), support, (minDomain, maxDomain), periodicity, hashMapLength, mode, 'cpp')\n",
    "        else:\n",
    "            raise ValueError(f'algorithm = {algorithm} not supported')\n",
    "    else:\n",
    "        if algorithm == 'naive':\n",
    "            if verbose:\n",
    "                print('Calling radiusNaive, arguments:')\n",
    "                print(f'queryPositions = {queryPositions.shape} on {queryPositions.device}')\n",
    "                print(f'querySupport = {querySupport.shape} on {querySupport.device}')\n",
    "                print(f'referencePositions = {referencePositions.shape} on {referencePositions.device}')\n",
    "                print(f'referenceSupport = {referenceSupport.shape} on {referenceSupport.device}')\n",
    "                print(f'periodicTensor = {periodicTensor}')\n",
    "                print(f'minDomain = {minDomain.shape} on {minDomain.device}')\n",
    "                print(f'maxDomain = {maxDomain.shape} on {maxDomain.device}')\n",
    "            return radiusNaive(queryPositions, referencePositions, querySupport, referenceSupport, periodicTensor, minDomain, maxDomain, mode)\n",
    "        elif algorithm == 'small':\n",
    "            if verbose:\n",
    "                print('Calling neighborSearchSmall, arguments:')\n",
    "                print(f'queryPositions = {queryPositions.shape} on {queryPositions.device}')\n",
    "                print(f'querySupport = {querySupport.shape} on {querySupport.device}')\n",
    "                print(f'referencePositions = {referencePositions.shape} on {referencePositions.device}')\n",
    "                print(f'referenceSupport = {referenceSupport.shape} on {referenceSupport.device}' if referenceSupport is not None else None)\n",
    "                print(f'minDomain = {minDomain.shape} on {minDomain.device}')\n",
    "                print(f'maxDomain = {maxDomain.shape} on {maxDomain.device}')\n",
    "                print(f'periodicTensor = {periodicTensor}')\n",
    "                print(f'mode = {mode}')\n",
    "            return neighborSearchSmall(queryPositions, querySupport, referencePositions, querySupport if referenceSupport is None else referenceSupport, minDomain, maxDomain, torch.tensor(periodicTensor).to(queryPositions.device), mode)\n",
    "        elif algorithm == 'compact':\n",
    "            if verbose:\n",
    "                print('Calling neighborSearch, arguments:')\n",
    "                print(f'queryPositions = {queryPositions.shape} on {queryPositions.device}')\n",
    "                print(f'querySupport = {querySupport.shape} on {querySupport.device}')\n",
    "                print(f'referencePositions = {referencePositions.shape} on {referencePositions.device}')\n",
    "                print(f'referenceSupport = {referenceSupport.shape} on {referenceSupport.device}')\n",
    "                print(f'periodicity = {periodicity}')\n",
    "                print(f'hashMapLength = {hashMapLength}')\n",
    "                print(f'mode = {mode}')\n",
    "            return neighborSearch((queryPositions, referencePositions), (querySupport, referenceSupport), (minDomain, maxDomain), periodicity, hashMapLength, mode, 'cpp')\n",
    "        else:\n",
    "            raise ValueError(f'algorithm = {algorithm} not supported')\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ecbea745",
   "metadata": {},
   "outputs": [],
   "source": [
    "(y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength = generateNeighborTestData(nx, targetNumNeighbors, dim, 1.0, False, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce1fa7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_ij(i, j, y, positions):\n",
    "    assert i.shape[0] == j.shape[0], f'i.shape[0] = {i.shape[0]} != j.shape[0] = {j.shape[0]}'\n",
    "    assert i.shape[0] == 11520, f'i.shape[0] = {i.shape[0]} != 11520'\n",
    "    assert j.shape[0] == 11520, f'i.shape[0] = {j.shape[0]} != 11520'\n",
    "    ii, ni = countUniqueEntries(i, y)\n",
    "    jj, nj = countUniqueEntries(j, positions)\n",
    "    assert ni.min() == ni.max(), f'ni.min() = {ni.min()} != ni.max() = {ni.max()}'\n",
    "    assert ni.min() == 45, f'ni.min() = {ni.min()} != 45'\n",
    "    print('Passed')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f8f80c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22983cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passed\n",
      "Passed\n",
      "Passed\n"
     ]
    }
   ],
   "source": [
    "i, j = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'naive', False)\n",
    "test_ij(i, j, y, positions)\n",
    "i, j = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'small', False)\n",
    "test_ij(i, j, y, positions)\n",
    "(i, j), nl = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'compact', False)\n",
    "test_ij(i, j, y, positions)\n",
    "i, j = radiusSearch(y, positions, (ySupport, supports), 'scatter', minDomain, maxDomain, periodicity, hashMapLength, 'naive', False)\n",
    "test_ij(i, j, y, positions)\n",
    "i, j = radiusSearch(y, positions, (ySupport, supports), 'scatter', minDomain, maxDomain, periodicity, hashMapLength, 'small', False)\n",
    "test_ij(i, j, y, positions)\n",
    "(i, j), nl = radiusSearch(y, positions, (ySupport, supports), 'scatter', minDomain, maxDomain, periodicity, hashMapLength, 'compact', False)\n",
    "test_ij(i, j, y, positions)\n",
    "i, j = radiusSearch(y, positions, ySupport, 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'naive', False)\n",
    "test_ij(i, j, y, positions)\n",
    "i, j = radiusSearch(y, positions, ySupport, 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'small', False)\n",
    "test_ij(i, j, y, positions)\n",
    "(i, j), nl = radiusSearch(y, positions, ySupport, 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'compact', False)\n",
    "test_ij(i, j, y, positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6011c476",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passed\n",
      "Passed\n",
      "Passed\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6c9e1ae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passed\n",
      "Passed\n",
      "Passed\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3fb48dec",
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "i.shape[0] = 9324 != 11520",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m i, j \u001b[38;5;241m=\u001b[39m radiusSearch(y, \u001b[38;5;28;01mNone\u001b[39;00m, ySupport[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mitem(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgather\u001b[39m\u001b[38;5;124m'\u001b[39m, minDomain, maxDomain, periodicity, hashMapLength, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnaive\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m----> 2\u001b[0m \u001b[43mtest_ij\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpositions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m i, j \u001b[38;5;241m=\u001b[39m radiusSearch(y, \u001b[38;5;28;01mNone\u001b[39;00m, ySupport[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mitem(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgather\u001b[39m\u001b[38;5;124m'\u001b[39m, minDomain, maxDomain, periodicity, hashMapLength, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msmall\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      4\u001b[0m test_ij(i, j, y, positions)\n",
      "Cell \u001b[0;32mIn[11], line 3\u001b[0m, in \u001b[0;36mtest_ij\u001b[0;34m(i, j, y, positions)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtest_ij\u001b[39m(i, j, y, positions):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m i\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m j\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi.shape[0] = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m != j.shape[0] = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mj\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 3\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m i\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m11520\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi.shape[0] = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m != 11520\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m j\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m11520\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi.shape[0] = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mj\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m != 11520\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      5\u001b[0m     ii, ni \u001b[38;5;241m=\u001b[39m countUniqueEntries(i, y)\n",
      "\u001b[0;31mAssertionError\u001b[0m: i.shape[0] = 9324 != 11520"
     ]
    }
   ],
   "source": [
    "i, j = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'naive', False)\n",
    "test_ij(i, j, y, positions)\n",
    "i, j = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'small', False)\n",
    "test_ij(i, j, y, positions)\n",
    "(i, j), nl = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'compact', False)\n",
    "test_ij(i, j, y, positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cced811e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling neighborSearchSmallFixed, arguments:\n",
      "queryPositions = torch.Size([256, 2]) on cuda:0\n",
      "referencePositions = torch.Size([1024, 2]) on cuda:0\n",
      "supportRadius = 0.24933892488479614\n",
      "minDomain = torch.Size([2]) on cuda:0\n",
      "maxDomain = torch.Size([2]) on cuda:0\n",
      "periodicTensor = [False, False]\n",
      "ni = (torch.Size([256]), tensor([45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45], device='cuda:0')) (torch.Size([11520])), nj = (torch.Size([1024]), tensor([0, 0, 0,  ..., 0, 0, 0], device='cuda:0')) (torch.Size([11520])) targetNumNeighbors = 50\n"
     ]
    }
   ],
   "source": [
    "i, j = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'small', True)\n",
    "ii, ni = countUniqueEntries(i, y)\n",
    "jj, nj = countUniqueEntries(j, positions)\n",
    "print(f'ni = {ni.shape, ni} ({i.shape}), nj = {nj.shape, nj} ({j.shape}) targetNumNeighbors = {targetNumNeighbors}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "930857a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling neighborSearch, arguments:\n",
      "queryPositions = torch.Size([256, 2]) on cuda:0\n",
      "referencePositions = torch.Size([1024, 2]) on cuda:0\n",
      "support = 0.24933892488479614\n",
      "minDomain = torch.Size([2]) on cuda:0\n",
      "maxDomain = torch.Size([2]) on cuda:0\n",
      "periodicity = [False, False]\n",
      "hashMapLength = 1024\n",
      "mode = gather\n",
      "ni = (torch.Size([256]), tensor([45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,\n",
      "        45, 45, 45, 45], device='cuda:0')) (torch.Size([11520])), nj = (torch.Size([1024]), tensor([0, 0, 0,  ..., 0, 0, 0], device='cuda:0')) (torch.Size([11520])) targetNumNeighbors = 50\n"
     ]
    }
   ],
   "source": [
    "(i, j), nl = radiusSearch(y, positions, ySupport[0].cpu().item(), 'gather', minDomain, maxDomain, periodicity, hashMapLength, 'compact', True)\n",
    "ii, ni = countUniqueEntries(i, y)\n",
    "jj, nj = countUniqueEntries(j, positions)\n",
    "print(f'ni = {ni.shape, ni} ({i.shape}), nj = {nj.shape, nj} ({j.shape}) targetNumNeighbors = {targetNumNeighbors}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57d2fd8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4526c12",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx in range(len(j)):\n",
    "    print(f'i = {idx}, i[i] = {i[idx]} j[i] = {j[idx]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ee5f19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def callNeighborSearch(algorithm, y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials):\n",
    "    if algorithm == 'naive':\n",
    "        return callRadiusNaive(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials)\n",
    "    elif algorithm == 'small':\n",
    "        return callRadiusSmall(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials)\n",
    "    elif algorithm == 'compact':\n",
    "        return callRadiusCompact(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials)\n",
    "    elif algorithm == 'torch':\n",
    "        return callRadius(y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, mode, device, numTrials)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5be7cf1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee8e62fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame()\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "if platform.system() == 'Darwin':\n",
    "    device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')\n",
    "# device = torch.device('cpu')\n",
    "targetNumNeighbors = 50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cddee916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e69d50833b0482fa2ddeb22675e4124",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa71dd3567464eb1b48a792e1774c128",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/64 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6118a5871260461ea93e50514f1e5e39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1e496a0e462468e92f49f2fa43673d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ptcls = np.logspace(8, 22, 64, base = 2).astype(int)\n",
    "dims = [1,2,3]\n",
    "periodics = ['small', 'naive', 'compact', 'torch']\n",
    "iters = 8\n",
    "\n",
    "t_periodic = tqdm(periodics)\n",
    "t_nx = tqdm(ptcls)\n",
    "t_dim = tqdm(dims)\n",
    "t_iter = tqdm(range(iters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ba55aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "262144"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f95eb990",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_periodic.reset()\n",
    "for periodic in periodics:\n",
    "    t_periodic.set_description(\"periodic = %s\" % periodic)\n",
    "    t_nx.reset()\n",
    "    for ptcl in ptcls:\n",
    "        t_nx.set_description(\"ptcls = %d\" % ptcl)\n",
    "        t_dim.reset()\n",
    "        for dim in dims:\n",
    "            if ptcl > 2**19 and periodic == 'torch':\n",
    "                break\n",
    "            if ptcl > 2**15 and periodic == 'naive':\n",
    "                break\n",
    "            if ptcl > 2**17 and periodic == 'small':\n",
    "                break\n",
    "            \n",
    "            nx = int(ptcl ** (1 / dim))\n",
    "            t_dim.set_description(\"dim = %d, nx = %d\" % (dim, nx))\n",
    "            (y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength = generateNeighborTestData(nx, targetNumNeighbors, dim, 1.0, False, device)\n",
    "            # print(y.shape, positions.shape, ySupport.shape, supports.shape, minDomain, maxDomain, periodicity, hashMapLength)\n",
    "\n",
    "            callNeighborSearch(periodic, y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, 'scatter', device, 1)\n",
    "\n",
    "            # (i_cpu, j_cpu), neighborDict = neighborSearch((y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength, 'scatter', 'cpp')\n",
    "            # del i_cpu, j_cpu, neighborDict\n",
    "            t_iter.reset()\n",
    "            for i in range(iters):\n",
    "                t_iter.set_description(\"i = %d\" % i)\n",
    "                start_time = time.time()\n",
    "                # for i in range(8):\n",
    "                callNeighborSearch(periodic, y, positions, ySupport, supports, periodicity, minDomain, maxDomain, hashMapLength, 'scatter', device, 1)\n",
    "                # (i_cpu, j_cpu), neighborDict = neighborSearch((y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength, 'scatter', 'cpp')\n",
    "                end_time = time.time()\n",
    "                torch.cuda.empty_cache()        \n",
    "\n",
    "                df = pd.DataFrame({\n",
    "                    'ptcls': nx**dim, 'nx': nx, 'dim': dim, 'targetNumNeighbors': periodic, \n",
    "                    'time': end_time - start_time, 'device': device.type, 'algorithm': periodic\n",
    "                    # 'ni_cpu.min()': ni_cpu.min().item(), 'ni_cpu.max()': ni_cpu.max().item(), 'nj_cpu.min()': nj_cpu.min().item(), 'nj_cpu.max()': nj_cpu.max().item()\n",
    "                    }, index = [0])\n",
    "                if i > 0:\n",
    "                    dataset = pd.concat([dataset, df], ignore_index = True)\n",
    "                t_iter.update()\n",
    "            t_dim.update()\n",
    "        t_nx.update()\n",
    "    t_periodic.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c5541a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>ptcls</th>\n",
       "      <th>nx</th>\n",
       "      <th>dim</th>\n",
       "      <th>targetNumNeighbors</th>\n",
       "      <th>time</th>\n",
       "      <th>device</th>\n",
       "      <th>algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>small</td>\n",
       "      <td>0.000662</td>\n",
       "      <td>cuda</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>small</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>cuda</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>small</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>cuda</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>small</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>cuda</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>small</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>cuda</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3922</th>\n",
       "      <td>474552</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>torch</td>\n",
       "      <td>0.171056</td>\n",
       "      <td>cuda</td>\n",
       "      <td>torch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3923</th>\n",
       "      <td>474552</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>torch</td>\n",
       "      <td>0.167784</td>\n",
       "      <td>cuda</td>\n",
       "      <td>torch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3924</th>\n",
       "      <td>474552</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>torch</td>\n",
       "      <td>0.269015</td>\n",
       "      <td>cuda</td>\n",
       "      <td>torch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3925</th>\n",
       "      <td>474552</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>torch</td>\n",
       "      <td>0.171106</td>\n",
       "      <td>cuda</td>\n",
       "      <td>torch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3926</th>\n",
       "      <td>474552</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>torch</td>\n",
       "      <td>0.174135</td>\n",
       "      <td>cuda</td>\n",
       "      <td>torch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3927 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ptcls   nx  dim targetNumNeighbors      time device algorithm\n",
       "0        256  256    1              small  0.000662   cuda     small\n",
       "1        256  256    1              small  0.000553   cuda     small\n",
       "2        256  256    1              small  0.000470   cuda     small\n",
       "3        256  256    1              small  0.000304   cuda     small\n",
       "4        256  256    1              small  0.000446   cuda     small\n",
       "...      ...  ...  ...                ...       ...    ...       ...\n",
       "3922  474552   78    3              torch  0.171056   cuda     torch\n",
       "3923  474552   78    3              torch  0.167784   cuda     torch\n",
       "3924  474552   78    3              torch  0.269015   cuda     torch\n",
       "3925  474552   78    3              torch  0.171106   cuda     torch\n",
       "3926  474552   78    3              torch  0.174135   cuda     torch\n",
       "\n",
       "[3927 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "09bcc916",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5af339655281473fac785aea9ae73c82",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=1610.25/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = copy.deepcopy(dataset)\n",
    "data = data[data['ptcls'] > 300]\n",
    "data['dim'] = data['dim'].astype('category')\n",
    "data['targetNumNeighbors'] = data['targetNumNeighbors'].astype('category')\n",
    "\n",
    "g = sns.relplot(data = data, x = 'ptcls', y = 'time', hue = 'algorithm', kind = 'line', col = 'dim')\n",
    "\n",
    "for ax in g.axes.flat:\n",
    "    ax.set_xlabel('Number of particles')\n",
    "    ax.set_ylabel('Time (s)')\n",
    "    ax.grid(True, which='both')\n",
    "\n",
    "g.figure.suptitle('Time to find neighbors')\n",
    "\n",
    "sns.move_legend(g, 'center left', bbox_to_anchor=[.5, 0.05], ncols=4, title='Number of neighbors')\n",
    "g.axes[0,0].set_xscale('log')\n",
    "g.axes[0,0].set_yscale('log')\n",
    "g.figure.tight_layout()\n",
    "plt.subplots_adjust(left=0.05, right=0.99, top=0.9, bottom=0.225)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5b249a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:2024-02-13 00:11:28 864:864 init.cpp:146] function cbapi->getCuptiStatus() failed with error CUPTI_ERROR_NOT_INITIALIZED (15)\n",
      "WARNING:2024-02-13 00:11:28 864:864 init.cpp:147] CUPTI initialization failed - CUDA profiler activities will be missing\n",
      "INFO:2024-02-13 00:11:28 864:864 init.cpp:149] If you see CUPTI_ERROR_INSUFFICIENT_PRIVILEGES, refer to https://developer.nvidia.com/nvidia-development-tools-solutions-err-nvgpuctrperm-cupti\n",
      "STAGE:2024-02-13 00:11:28 864:864 ActivityProfilerController.cpp:311] Completed Stage: Warm Up\n",
      "STAGE:2024-02-13 00:11:33 864:864 ActivityProfilerController.cpp:317] Completed Stage: Collection\n",
      "STAGE:2024-02-13 00:11:33 864:864 ActivityProfilerController.cpp:321] Completed Stage: Post Processing\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                                   Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg    # of Calls  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "                                         neighborSearch         0.20%       8.965ms       100.00%        4.456s     139.248ms       1.045ms         0.02%        4.517s     141.171ms            32  \n",
      "                neighborSearch - sortReferenceParticles         1.66%      73.884ms        59.37%        2.645s      41.336ms      37.607ms         0.83%     705.903ms      11.030ms            64  \n",
      "                                             aten::item         0.20%       9.079ms        48.40%        2.157s       7.488ms       5.411ms         0.12%      30.018ms     104.229us           288  \n",
      "                              aten::_local_scalar_dense        48.19%        2.147s        48.19%        2.147s       7.456ms      24.607ms         0.54%      24.607ms      85.441us           288  \n",
      "                   neighborSearch - searchNeighbors_cpp         0.14%       6.072ms        43.74%        1.949s      60.907ms       3.089ms         0.07%        3.990s     124.694ms            32  \n",
      "                                               aten::to         0.26%      11.625ms        42.43%        1.891s       3.109ms      10.475ms         0.23%      72.838ms     119.799us           608  \n",
      "neighborSearch - searchNeighbors_cpp[build NeighborO...         0.54%      24.231ms        42.32%        1.886s      58.934ms        1.833s        40.57%        1.874s      58.553ms            32  \n",
      "                                         aten::_to_copy         0.62%      27.539ms        42.17%        1.879s       3.670ms      17.958ms         0.40%      62.363ms     121.803us           512  \n",
      "                                            aten::copy_        41.54%        1.851s        41.54%        1.851s       3.214ms      46.337ms         1.03%      46.337ms      80.446us           576  \n",
      "                                 sortReferenceParticles         0.05%       2.033ms         4.29%     190.944ms       5.967ms     888.000us         0.02%     191.088ms       5.971ms            32  \n",
      "neighborSearch - sortReferenceParticles[index Calcul...         0.42%      18.776ms         3.04%     135.305ms       4.228ms      13.301ms         0.29%     135.711ms       4.241ms            32  \n",
      "                               aten::unique_consecutive         0.31%      13.644ms         1.33%      59.428ms     928.562us       9.134ms         0.20%      20.771ms     324.547us            64  \n",
      "neighborSearch - searchNeighbors_cpp[build NeighborL...         0.29%      13.081ms         1.26%      56.163ms       1.755ms        1.872s        41.44%        2.113s      66.043ms            32  \n",
      "                                            aten::index         0.75%      33.611ms         1.00%      44.717ms     139.741us     151.548ms         3.35%     169.016ms     528.175us           320  \n",
      "                                            aten::zeros         0.16%       7.224ms         0.74%      33.070ms     206.688us       3.896ms         0.09%     155.306ms     970.663us           160  \n",
      "                                           aten::hstack         0.13%       5.905ms         0.52%      23.170ms     241.354us       3.323ms         0.07%      27.266ms     284.021us            96  \n",
      "                                           aten::cumsum         0.19%       8.641ms         0.45%      20.208ms     210.500us       9.092ms         0.20%      22.140ms     230.625us            96  \n",
      "                                              aten::cat         0.29%      12.849ms         0.45%      20.121ms     104.797us      22.625ms         0.50%      32.598ms     169.781us           192  \n",
      "                                            aten::empty         0.45%      19.875ms         0.45%      19.875ms      29.576us      66.305ms         1.47%      66.305ms      98.668us           672  \n",
      "                                          aten::argsort         0.03%       1.433ms         0.43%      19.196ms     299.938us       2.219ms         0.05%      48.567ms     758.859us            64  \n",
      "                                             aten::sort         0.34%      15.066ms         0.40%      17.763ms     277.547us      35.986ms         0.80%      46.348ms     724.188us            64  \n",
      "                                           aten::select         0.32%      14.313ms         0.34%      14.934ms      27.452us      14.038ms         0.31%      19.893ms      36.568us           544  \n",
      "                                            aten::stack         0.13%       5.864ms         0.32%      14.298ms     223.406us       4.062ms         0.09%      14.455ms     225.859us            64  \n",
      "                                      fallback_function         0.17%       7.393ms         0.31%      13.866ms     216.656us       4.316ms         0.10%      16.620ms     259.688us            64  \n",
      "                                            aten::slice         0.26%      11.516ms         0.27%      12.227ms      29.392us       9.916ms         0.22%      15.111ms      36.325us           416  \n",
      "       neighborSearch - sortReferenceParticles[argsort]         0.04%       1.568ms         0.26%      11.370ms     355.312us       1.033ms         0.02%      40.209ms       1.257ms            32  \n",
      "                                       aten::index_put_         0.08%       3.384ms         0.23%      10.060ms     157.188us       4.042ms         0.09%      12.962ms     202.531us            64  \n",
      "                                           aten::unbind         0.12%       5.504ms         0.22%       9.765ms     152.578us       4.121ms         0.09%      12.035ms     188.047us            64  \n",
      "                                          aten::reshape         0.21%       9.219ms         0.22%       9.735ms      25.352us      11.311ms         0.25%      14.143ms      36.831us           384  \n",
      "                                        aten::unsqueeze         0.18%       8.229ms         0.19%       8.501ms      29.517us       7.277ms         0.16%      12.071ms      41.913us           288  \n",
      "                                              aten::max         0.17%       7.403ms         0.18%       8.133ms     127.078us       6.336ms         0.14%       7.529ms     117.641us            64  \n",
      "                                            aten::zero_         0.10%       4.248ms         0.18%       7.992ms      49.950us       2.870ms         0.06%      90.002ms     562.513us           160  \n",
      "                                           aten::narrow         0.08%       3.605ms         0.16%       7.272ms      56.812us       4.836ms         0.11%       9.973ms      77.914us           128  \n",
      "                    neighborSearch - computeGridSupport         0.05%       2.304ms         0.16%       7.217ms     225.531us     948.000us         0.02%       5.763ms     180.094us            32  \n",
      "                                              aten::mul         0.16%       7.145ms         0.16%       7.145ms      55.820us      19.603ms         0.43%      19.603ms     153.148us           128  \n",
      "                                 aten::_index_put_impl_         0.11%       4.957ms         0.15%       6.676ms     104.312us       4.209ms         0.09%       8.920ms     139.375us            64  \n",
      "                                            aten::fill_         0.13%       5.703ms         0.15%       6.636ms      29.625us      92.441ms         2.05%      93.187ms     416.013us           224  \n",
      "                                           aten::vstack         0.03%       1.252ms         0.13%       5.733ms     179.156us       1.043ms         0.02%      16.323ms     510.094us            32  \n",
      "                                       run_fused_kernel         0.11%       5.083ms         0.11%       5.083ms      79.422us      10.776ms         0.24%      10.776ms     168.375us            64  \n",
      "        neighborSearch - sortReferenceParticles[resort]         0.03%       1.433ms         0.11%       4.971ms     155.344us     238.000us         0.01%       7.073ms     221.031us            32  \n",
      "                                     computeGridSupport         0.02%     916.000us         0.11%       4.913ms     153.531us       1.545ms         0.03%       4.815ms     150.469us            32  \n",
      "                                          aten::detach_         0.09%       4.017ms         0.10%       4.399ms      27.494us       3.882ms         0.09%       5.761ms      36.006us           160  \n",
      "                                       aten::atleast_2d         0.03%       1.494ms         0.08%       3.344ms     104.500us       1.963ms         0.04%       5.798ms     181.188us            32  \n",
      "                                    aten::empty_strided         0.07%       3.035ms         0.07%       3.035ms       5.928us       8.430ms         0.19%       8.430ms      16.465us           512  \n",
      "                                         aten::new_ones         0.03%       1.446ms         0.07%       3.032ms      94.750us     848.000us         0.02%       4.554ms     142.312us            32  \n",
      "                                               aten::mT         0.03%       1.202ms         0.06%       2.815ms      43.984us     709.000us         0.02%       2.416ms      37.750us            64  \n",
      "                                       aten::as_strided         0.06%       2.620ms         0.06%       2.620ms       1.462us      22.942ms         0.51%      22.942ms      12.802us          1792  \n",
      "                      neighborSearch - getDomainExtents         0.05%       2.123ms         0.05%       2.276ms      71.125us     233.000us         0.01%     716.000us      22.375us            32  \n",
      "                                              __enter__         0.05%       2.232ms         0.05%       2.232ms      17.438us       2.910ms         0.06%       2.910ms      22.734us           128  \n",
      "                                             aten::add_         0.05%       2.177ms         0.05%       2.177ms      22.677us      64.870ms         1.44%      64.870ms     675.729us            96  \n",
      "                                               __exit__         0.04%       1.713ms         0.04%       1.713ms      13.383us       1.191ms         0.03%       1.191ms       9.305us           128  \n",
      "                                        aten::transpose         0.03%       1.446ms         0.04%       1.613ms      25.203us     651.000us         0.01%       1.707ms      26.672us            64  \n",
      "                                          aten::permute         0.03%       1.269ms         0.03%       1.390ms      43.438us       1.159ms         0.03%       1.528ms      47.750us            32  \n",
      "                                              aten::sub         0.03%       1.380ms         0.03%       1.380ms      43.125us       6.473ms         0.14%       6.473ms     202.281us            32  \n",
      "                                              aten::add         0.03%       1.157ms         0.03%       1.157ms      36.156us       1.117ms         0.02%       1.117ms      34.906us            32  \n",
      "                                        aten::new_empty         0.02%     863.000us         0.02%       1.016ms      31.750us     718.000us         0.02%       1.258ms      39.312us            32  \n",
      "    neighborSearch - searchNeighbors_cpp[count entries]         0.02%     882.000us         0.02%     882.000us      27.562us      57.000us         0.00%      57.000us       1.781us            32  \n",
      "                                              aten::div         0.02%     845.000us         0.02%     845.000us      26.406us       5.665ms         0.13%       5.665ms     177.031us            32  \n",
      "                                            aten::floor         0.02%     802.000us         0.02%     802.000us      25.062us       5.542ms         0.12%       5.542ms     173.188us            32  \n",
      "                                   aten::_reshape_alias         0.01%     516.000us         0.01%     516.000us       1.344us       2.832ms         0.06%       2.832ms       7.375us           384  \n",
      "                                          aten::resize_         0.01%     446.000us         0.01%     446.000us       2.323us       3.514ms         0.08%       3.514ms      18.302us           192  \n",
      "                                             aten::view         0.01%     386.000us         0.01%     386.000us      12.062us      83.000us         0.00%      83.000us       2.594us            32  \n",
      "                                                detach_         0.01%     382.000us         0.01%     382.000us       2.388us       1.879ms         0.04%       1.879ms      11.744us           160  \n",
      "                                       getDomainExtents         0.01%     268.000us         0.01%     268.000us       4.188us     538.000us         0.01%     538.000us       8.406us            64  \n",
      "                                       aten::lift_fresh         0.00%     107.000us         0.00%     107.000us       0.557us       1.951ms         0.04%       1.951ms      10.161us           192  \n",
      "                                       aten::atleast_1d         0.00%      64.000us         0.00%      64.000us       0.667us       2.984ms         0.07%       2.984ms      31.083us            96  \n",
      "-------------------------------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  \n",
      "Self CPU time total: 4.456s\n",
      "Self CUDA time total: 4.517s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import torch.autograd.profiler as profiler\n",
    "\n",
    "# with profiler.profile(record_shapes=False, use_cuda=torch.cuda.is_available()) as prof:\n",
    "#     for i in range(32):\n",
    "#         with profiler.record_function(\"neighborSearch\"):\n",
    "#             (i_cpu, j_cpu), ni_cpu, nj_cpu, neighborDict = neighborSearch((y, positions), (ySupport, supports), (minDomain, maxDomain), periodicity, hashMapLength, 'scatter', 'cpp')\n",
    "\n",
    "# print(prof.key_averages().table(sort_by=\"cpu_time_total\"))\n",
    "\n",
    "# # Export the trace\n",
    "# # prof.export_chrome_trace(\"trace.json\")\n"
   ]
  }
 ],
 "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
}