OpenJij/OpenJij

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docs/tutorial/en/005-benchmark_sparse_dense.ipynb

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sparse/Dense QUBO Matrix in OpenJij\n",
    "The number of nonzero elements in the interaction matrix $J$ of the Ising Hamiltonian or QUBO matrix $Q$ can vary greatly depending on the problem.\n",
    "Many real-life problems have sparse interaction matrices with relatively few nonzero values.\n",
    "Therefore, by default, OpenJij internally keeps the interaction coefficients as a sparse matrix.\n",
    "However, depending on the problem, many elements of the interaction matrix may be non-zero.\n",
    "In this case, data retention in sparse matrix form will slow down the computation.\n",
    "To solve this problem, OpenJij provides a parameter, `sparse`, that allows users to control how the matrix is retained.\n",
    "In this tutorial, we will explain how to use `sparse`. We will also compare the speed of keeping data in a sparse matrix with that of keeping data in a dense matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openjij as oj\n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The internal retention of data can be configured as follows:\n",
    "```python\n",
    "sampler = oj.SASampler()\n",
    "response = sampler.sample_ising(h, J, sparse = True)\n",
    "```\n",
    "With `sparse = True`, the sampler retains the interaction matrix as a sparse matrix.\n",
    "The latest OpenJij has `sparse = True` by default (as of 2022/10 (v0.6.5)).\n",
    "You can change this flag by:\n",
    "```python\n",
    "response = sampler.sample_ising(h, J, sparse = False)\n",
    "```\n",
    "This would make internal data retention a dense matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Execution Speed in Sparse and Dense Mode\n",
    "Next, let us examine how data retention can affect execution time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_READS = 1\n",
    "\n",
    "BETA_MAX = 14000000\n",
    "BETA_MIN = 0.0015\n",
    "\n",
    "num_spins = [100,200,500,1000,2000,5000]\n",
    "p_list = [0.1,0.3,0.5,0.8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A variant of the Sherrington-Kirkpatrick (SK) model without external fields is used here as a benchmark.\n",
    "The SK model without external fields is a model with the following Hamiltonian:\n",
    "\n",
    "$$ H = \\frac{1}{N}\\sum_{ij} J_{ij} \\sigma_i \\sigma_j,\\\\ \\ J_{ij} \\sim \\mathcal{N}(0,1), J_{ij} = J_{ji}$$\n",
    "\n",
    "where $\\mathcal{N}(0,1)$ is a random Gaussian noise with a mean of 0 and variance of 1.\n",
    "In the SK model, all elements follow a random distribution.\n",
    "However, here we will examine how computation time changes as we vary the fraction of elements in the interaction matrix $J$ and make the interaction sparse.\n",
    "\n",
    "First, we set up a random interaction. Here, the parameters are the number of spins $n$ and the fraction of elements $p$ in the interaction matrix $J$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_random_interaction(n,p):\n",
    "    h,J = {},{}\n",
    "    for i in range(n-1):\n",
    "        for j in range(i+1, n):\n",
    "            if np.random.random() <= p:\n",
    "                # OpenJij keeps data in the form of an upper triangular matrix.\n",
    "                J[i, j] = np.random.normal(0, 1) / np.sqrt(n)\n",
    "\n",
    "    return J,h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first calculate the case with a dense matrix.\n",
    "Here, we change the number of spins $n=100,200,500,1000,2000,5000$, and the proportion of elements $p$ in the interaction matrix $J$ is also varied $p=0.1,0.3,0.5,0.8$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenJij Dense\n",
      "p = 0.1, n = 100 : \telapsed_time:0.010784893999999934[sec]\n",
      "p = 0.1, n = 200 : \telapsed_time:0.026233769000000073[sec]\n",
      "p = 0.1, n = 500 : \telapsed_time:0.21190827999999984[sec]\n",
      "p = 0.1, n = 1000 : \telapsed_time:0.6879572889999999[sec]\n",
      "p = 0.1, n = 2000 : \telapsed_time:2.9189378899999996[sec]\n",
      "p = 0.1, n = 5000 : \telapsed_time:17.226335605999996[sec]\n",
      "p = 0.3, n = 100 : \telapsed_time:0.011216662999999016[sec]\n",
      "p = 0.3, n = 200 : \telapsed_time:0.02720410599999923[sec]\n",
      "p = 0.3, n = 500 : \telapsed_time:0.1655943339999979[sec]\n",
      "p = 0.3, n = 1000 : \telapsed_time:0.7373647790000035[sec]\n",
      "p = 0.3, n = 2000 : \telapsed_time:2.9717210430000023[sec]\n",
      "p = 0.3, n = 5000 : \telapsed_time:18.64540647900001[sec]\n",
      "p = 0.5, n = 100 : \telapsed_time:0.009729735999997047[sec]\n",
      "p = 0.5, n = 200 : \telapsed_time:0.03336171099999774[sec]\n",
      "p = 0.5, n = 500 : \telapsed_time:0.18042641499999945[sec]\n",
      "p = 0.5, n = 1000 : \telapsed_time:0.8616806240000017[sec]\n",
      "p = 0.5, n = 2000 : \telapsed_time:5.395520128000001[sec]\n",
      "p = 0.5, n = 5000 : \telapsed_time:20.113565592000015[sec]\n",
      "p = 0.8, n = 100 : \telapsed_time:0.017290124999988166[sec]\n",
      "p = 0.8, n = 200 : \telapsed_time:0.03757079400000407[sec]\n",
      "p = 0.8, n = 500 : \telapsed_time:0.21405931399999645[sec]\n",
      "p = 0.8, n = 1000 : \telapsed_time:1.0665648680000004[sec]\n",
      "p = 0.8, n = 2000 : \telapsed_time:4.003118959000005[sec]\n",
      "p = 0.8, n = 5000 : \telapsed_time:22.808700740999996[sec]\n"
     ]
    }
   ],
   "source": [
    "# Benchmark OpenJij Dense\n",
    "print('OpenJij Dense')\n",
    "sampler = oj.SASampler()\n",
    "openjij_dense_time = []\n",
    "openjij_dense_energy = []\n",
    "\n",
    "for p in p_list:\n",
    "    for n in num_spins:\n",
    "        J,h = create_random_interaction(n,p)\n",
    "        start = time.perf_counter()\n",
    "        response = sampler.sample_ising(h, J, num_sweeps=1000, num_reads=NUM_READS, beta_max=BETA_MAX, beta_min=BETA_MIN, sparse = False)\n",
    "        elapsed_time = time.perf_counter() - start\n",
    "\n",
    "        openjij_dense_time.append(elapsed_time)\n",
    "        openjij_dense_energy.append(np.mean(response.energies))\n",
    "\n",
    "        print(f\"p = {p}, n = {n} : \\telapsed_time:{elapsed_time}[sec]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we examine the case with a sparse matrix.\n",
    "The execution conditions are the same as for the dense matrix case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenJij Sparse\n",
      "p = 0.1, n = 100 : \telapsed_time:0.015455876999993734[sec]\n",
      "p = 0.1, n = 200 : \telapsed_time:0.01172593000001143[sec]\n",
      "p = 0.1, n = 500 : \telapsed_time:0.05383983299998363[sec]\n",
      "p = 0.1, n = 1000 : \telapsed_time:0.18197211800000446[sec]\n",
      "p = 0.1, n = 2000 : \telapsed_time:0.727910971[sec]\n",
      "p = 0.1, n = 5000 : \telapsed_time:4.896766718999999[sec]\n",
      "p = 0.3, n = 100 : \telapsed_time:0.007563688999994156[sec]\n",
      "p = 0.3, n = 200 : \telapsed_time:0.023680912000003218[sec]\n",
      "p = 0.3, n = 500 : \telapsed_time:0.12488942999999608[sec]\n",
      "p = 0.3, n = 1000 : \telapsed_time:0.663864641999993[sec]\n",
      "p = 0.3, n = 2000 : \telapsed_time:2.272948852999974[sec]\n",
      "p = 0.3, n = 5000 : \telapsed_time:14.29901092199998[sec]\n",
      "p = 0.5, n = 100 : \telapsed_time:0.012703374000011536[sec]\n",
      "p = 0.5, n = 200 : \telapsed_time:0.052227919000017664[sec]\n",
      "p = 0.5, n = 500 : \telapsed_time:0.19213491300001806[sec]\n",
      "p = 0.5, n = 1000 : \telapsed_time:0.8431962399999975[sec]\n",
      "p = 0.5, n = 2000 : \telapsed_time:3.5385885899999607[sec]\n",
      "p = 0.5, n = 5000 : \telapsed_time:23.98366295200003[sec]\n",
      "p = 0.8, n = 100 : \telapsed_time:0.01682115800002748[sec]\n",
      "p = 0.8, n = 200 : \telapsed_time:0.07765323400002444[sec]\n",
      "p = 0.8, n = 500 : \telapsed_time:0.5316407230000095[sec]\n",
      "p = 0.8, n = 1000 : \telapsed_time:1.358698779000008[sec]\n",
      "p = 0.8, n = 2000 : \telapsed_time:5.622073602[sec]\n",
      "p = 0.8, n = 5000 : \telapsed_time:37.54809538400002[sec]\n"
     ]
    }
   ],
   "source": [
    "# Benchmark OpenJij Sparse\n",
    "print('OpenJij Sparse')\n",
    "sampler = oj.SASampler()\n",
    "openjij_sparse_time = []\n",
    "openjij_sparse_energy = []\n",
    "for p in p_list:\n",
    "    for n in num_spins:\n",
    "        J,h = create_random_interaction(n,p)\n",
    "        start = time.perf_counter()\n",
    "        response = sampler.sample_ising(h, J, num_sweeps=1000, num_reads=NUM_READS, beta_max=BETA_MAX, beta_min=BETA_MIN, sparse=True)\n",
    "        elapsed_time = time.perf_counter() - start\n",
    "\n",
    "        openjij_sparse_time.append(elapsed_time)\n",
    "        openjij_sparse_energy.append(np.mean(response.energies))\n",
    "\n",
    "        print(f\"p = {p}, n = {n} : \\telapsed_time:{elapsed_time}[sec]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "sparse_time = np.array(openjij_sparse_time).reshape(len(p_list),-1)\n",
    "dense_time = np.array(openjij_dense_time).reshape(len(p_list),-1)\n",
    "color_list = [\"blue\", \"red\", \"green\", \"orange\"]\n",
    "for index,p in enumerate(p_list):\n",
    "    plt.plot(num_spins,dense_time[index], '-o', label=f'dense $p$ ={p}', color = color_list[index], markersize=8)\n",
    "    plt.plot(num_spins,sparse_time[index], '-^', label=f'sparse $p$ ={p}', color = color_list[index], markersize=8)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel('number of spins', fontsize=18)\n",
    "plt.ylabel('time [sec]', fontsize=18)\n",
    "plt.legend(fontsize=12, ncol = 4, bbox_to_anchor=(0.7, 0.7, 0.3, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the fraction of elements packed in the interaction matrix is $p=0.1$, the sparse matrix case with `sparse=True` is several times faster than the dense matrix case.\n",
    "However, at around $p=0.3$, both speeds are approximately the same.\n",
    "Furthermore, the dense matrix is faster when $p=0.5,0.8$.\n",
    "Thus, we can see that execution speed varies depending on whether a sparse or dense matrix is used.\n",
    "As a result, you will achieve faster results by switching accordingly to the problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.9.15"
  },
  "vscode": {
   "interpreter": {
    "hash": "2e8d7574d7ec71e14cb1575cf43673432d6fae464c836a7b3733d4f6c20243fb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}