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ma-sadeghi/TestCupy.ipynb

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Test cupy's conjugate gradient performance against scipy's
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TestCupy.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import cupy as cp\n",
"import cupyx.scipy.sparse.linalg\n",
"import cupyx.scipy.sparse as cusparse\n",
"import scipy.sparse.linalg as spla\n",
"import openpnm as op\n",
"from openpnm.utils.misc import tic, toc\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'svg'\n",
"np.random.seed(20)\n",
"ws = op.Workspace()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average conc. (CPU): 0.67, runtime: 524.6\n"
]
}
],
"source": [
"from pypardiso import spsolve\n",
"\n",
"n = 100\n",
"t_cpu = []\n",
"t_gpu = []\n",
"\n",
"net = op.network.Cubic(shape=[n, n, n])\n",
"geom = op.geometry.StickAndBall(network=net, pores=net.Ps, throats=net.Ts)\n",
"air = op.phases.Air(network=net)\n",
"phys_air = op.physics.Standard(network=net, geometry=geom, phase=air)\n",
"\n",
"fd = op.algorithms.FickianDiffusion(network=net, phase=air)\n",
"fd.set_value_BC(pores=net.pores(\"left\"), values=1.0)\n",
"fd.set_value_BC(pores=net.pores(\"right\"), values=0.35)\n",
"fd.settings[\"solver_family\"] = \"scipy\"\n",
"fd.settings[\"solver_type\"] = \"cg\"\n",
"\n",
"fd._build_A()\n",
"fd._build_b()\n",
"fd._apply_BCs()\n",
"\n",
"A = fd.A.tocsr()\n",
"b = fd.b\n",
"tol = fd.settings[\"solver_tol\"]\n",
"atol = fd._get_atol()\n",
"\n",
"tic()\n",
"c_cpu = spla.spsolve(A, b)\n",
"t_cpu.append(toc(quiet=True))\n",
"\n",
"print(f\"Average conc. (CPU): {c_cpu.mean():.2f}, runtime: {t_cpu[-1]:.1f}\")\n",
"\n",
"ws.clear()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Number of pores: 1000\n",
"Average conc. (CPU): 0.68, runtime: 0.0\n",
"Average conc. (GPU): 0.68, runtime: 0.8\n",
"GPU speed-up: 0.0\n",
"\n",
"Number of pores: 8000\n",
"Average conc. (CPU): 0.67, runtime: 0.0\n",
"Average conc. (GPU): 0.67, runtime: 0.0\n",
"GPU speed-up: 1.0\n",
"\n",
"Number of pores: 27000\n",
"Average conc. (CPU): 0.67, runtime: 0.2\n",
"Average conc. (GPU): 0.67, runtime: 0.1\n",
"GPU speed-up: 2.5\n",
"\n",
"Number of pores: 64000\n",
"Average conc. (CPU): 0.68, runtime: 0.4\n",
"Average conc. (GPU): 0.68, runtime: 0.1\n",
"GPU speed-up: 4.3\n",
"\n",
"Number of pores: 125000\n",
"Average conc. (CPU): 0.67, runtime: 0.9\n",
"Average conc. (GPU): 0.67, runtime: 0.1\n",
"GPU speed-up: 7.2\n",
"\n",
"Number of pores: 216000\n",
"Average conc. (CPU): 0.68, runtime: 1.5\n",
"Average conc. (GPU): 0.68, runtime: 0.2\n",
"GPU speed-up: 9.3\n",
"\n",
"Number of pores: 343000\n",
"Average conc. (CPU): 0.67, runtime: 2.8\n",
"Average conc. (GPU): 0.67, runtime: 0.2\n",
"GPU speed-up: 12.2\n",
"\n",
"Number of pores: 512000\n",
"Average conc. (CPU): 0.68, runtime: 4.6\n",
"Average conc. (GPU): 0.68, runtime: 0.3\n",
"GPU speed-up: 15.2\n",
"\n",
"Number of pores: 729000\n",
"Average conc. (CPU): 0.67, runtime: 8.0\n",
"Average conc. (GPU): 0.67, runtime: 0.5\n",
"GPU speed-up: 17.6\n",
"\n",
"Number of pores: 1000000\n",
"Average conc. (CPU): 0.68, runtime: 11.8\n",
"Average conc. (GPU): 0.68, runtime: 0.6\n",
"GPU speed-up: 20.6\n",
"\n",
"Number of pores: 1331000\n",
"Average conc. (CPU): 0.68, runtime: 16.6\n",
"Average conc. (GPU): 0.68, runtime: 0.7\n",
"GPU speed-up: 22.6\n",
"\n",
"Number of pores: 1728000\n",
"Average conc. (CPU): 0.68, runtime: 24.9\n",
"Average conc. (GPU): 0.68, runtime: 1.0\n",
"GPU speed-up: 24.1\n",
"\n",
"Number of pores: 2197000\n",
"Average conc. (CPU): 0.68, runtime: 35.1\n",
"Average conc. (GPU): 0.68, runtime: 1.4\n",
"GPU speed-up: 25.2\n",
"\n",
"Number of pores: 2744000\n",
"Average conc. (CPU): 0.68, runtime: 47.0\n",
"Average conc. (GPU): 0.68, runtime: 1.8\n",
"GPU speed-up: 25.7\n",
"\n",
"Number of pores: 3375000\n",
"Average conc. (CPU): 0.68, runtime: 62.7\n",
"Average conc. (GPU): 0.68, runtime: 2.4\n",
"GPU speed-up: 26.4\n",
"\n",
"Number of pores: 4096000\n",
"Average conc. (CPU): 0.68, runtime: 74.2\n",
"Average conc. (GPU): 0.68, runtime: 2.8\n",
"GPU speed-up: 26.8\n",
"\n",
"Number of pores: 4913000\n",
"Average conc. (CPU): 0.67, runtime: 102.3\n",
"Average conc. (GPU): 0.67, runtime: 3.7\n",
"GPU speed-up: 27.4\n",
"\n",
"Number of pores: 5832000\n",
"Average conc. (CPU): 0.68, runtime: 115.3\n",
"Average conc. (GPU): 0.68, runtime: 4.2\n",
"GPU speed-up: 27.4\n",
"\n",
"Number of pores: 6859000\n",
"Average conc. (CPU): 0.68, runtime: 148.2\n",
"Average conc. (GPU): 0.68, runtime: 5.3\n",
"GPU speed-up: 27.9\n",
"\n",
"Number of pores: 8000000\n",
"Average conc. (CPU): 0.68, runtime: 174.8\n",
"Average conc. (GPU): 0.68, runtime: 6.3\n",
"GPU speed-up: 27.7\n"
]
}
],
"source": [
"nx = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200])\n",
"t_cpu = []\n",
"t_gpu = []\n",
"\n",
"for n in nx:\n",
" print(f\"\\nNumber of pores: {n**3}\")\n",
" net = op.network.Cubic(shape=[n, n, n])\n",
" geom = op.geometry.StickAndBall(network=net, pores=net.Ps, throats=net.Ts)\n",
" air = op.phases.Air(network=net)\n",
" phys_air = op.physics.Standard(network=net, geometry=geom, phase=air)\n",
"\n",
" fd = op.algorithms.FickianDiffusion(network=net, phase=air)\n",
" fd.set_value_BC(pores=net.pores(\"left\"), values=1.0)\n",
" fd.set_value_BC(pores=net.pores(\"right\"), values=0.35)\n",
" fd.settings[\"solver_family\"] = \"scipy\"\n",
" fd.settings[\"solver_type\"] = \"cg\"\n",
"\n",
" fd._build_A()\n",
" fd._build_b()\n",
" fd._apply_BCs()\n",
"\n",
" A = fd.A.tocsr()\n",
" b = fd.b\n",
" tol = fd.settings[\"solver_tol\"]\n",
" atol = fd._get_atol()\n",
"\n",
" tic()\n",
" c_cpu, exit_code = spla.cg(A, b, tol=tol, atol=atol)\n",
" t_cpu.append(toc(quiet=True))\n",
"\n",
" print(f\"Average conc. (CPU): {c_cpu.mean():.2f}, runtime: {t_cpu[-1]:.1f}\")\n",
"\n",
" A = cusparse.csr_matrix(fd.A.tocsr().copy())\n",
" b = cp.array(fd.b.copy())\n",
"\n",
" tic()\n",
" c_gpu, exit_code = cusparse.linalg.cg(A, b, tol=tol, atol=atol)\n",
" t_gpu.append(toc(quiet=True))\n",
"\n",
" print(f\"Average conc. (GPU): {float(c_gpu.mean()):.2f}, runtime: {t_gpu[-1]:.1f}\")\n",
" \n",
" print(f\"GPU speed-up: {t_cpu[-1] / t_gpu[-1]:.1f}\")\n",
"\n",
" assert np.allclose(c_cpu, c_gpu)\n",
" ws.clear()\n",
"\n",
"t_cpu = np.array(t_cpu)\n",
"t_gpu = np.array(t_gpu)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
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"text/plain": [
"<Figure size 360x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
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],
"source": [
"fig, ax1 = plt.subplots(figsize=(5, 4))\n",
"# Plot runtimes\n",
"ax1.plot(nx**3, t_cpu, \"bx-.\", label=\"CPU\")\n",
"ax1.plot(nx**3, t_gpu, \"b.-\", label=\"GPU\")\n",
"ax1.legend()\n",
"ax1.set_xlabel(\"Number of pores\")\n",
"ax1.set_ylabel(\"Runtime (s)\", color=\"b\")\n",
"ax1.set_xscale(\"log\")\n",
"ax1.tick_params(axis=\"y\", labelcolor=\"b\")\n",
"# Plot speed-up\n",
"ax2 = ax1.twinx()\n",
"ax2.plot(nx**3, t_cpu / t_gpu, \"g^--\")\n",
"ax2.set_xlabel(\"Number of pores\")\n",
"ax2.set_ylabel(\"Speed-up\", color=\"g\")\n",
"ax2.set_xscale(\"log\")\n",
"ax2.tick_params(labelcolor=\"g\")"
]
}
],
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