{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Geometric multigrid solvers\n", "\n", "In addition to the full gamut of algebraic solvers offered by PETSc, Firedrake also provides access to multilevel solvers with geometric hierarchies. In this tutorial, we will study strategies to solve the Stokes equations, demonstrating how the multigrid functionality composes with fieldsplit preconditioning.\n", "\n", "\n", "## Creating a geometric hierarchy\n", "\n", "Geometric multigrid requires a geometric hierarchy of meshes on which the equations will be discretised. For now, Firedrake supports hierarchies of *regularly refined* meshes, which we create by providing a *coarse mesh* and building a `MeshHierarchy`. This hierarchy encapsulates the relationship between coarse and fine cells." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Code in this cell makes plots appear an appropriate size and resolution in the browser window\n", "%matplotlib widget\n", "%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams['figure.figsize'] = (11, 4)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from firedrake import *\n", "\n", "coarse_mesh = RectangleMesh(15, 10, 1.5, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having made the coarse mesh, we create the hierarchy of meshes. The second argument tells Firedrake how many levels of refinement to use. Here we refine three times, so that in total we have four meshes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "hierarchy = MeshHierarchy(coarse_mesh, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `hierarchy` object behaves like a Python *iterable*, so we can ask for its length and index it to extract meshes on a given level in the normal way:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(hierarchy)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "finest_mesh = hierarchy[-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grid transfer\n", "\n", "If you want to control all aspects of the multigrid cycle, Firedrake offers the necessary building blocks. You just need to create the relevant objects on the levels of the mesh hierarchy, and then use provided functions to transfer information between levels.\n", "\n", "Firedrake provides the three functions, `prolong`, `restrict`, and `inject`.\n", "\n", "- `prolong` transfers primal quantities from coarse to fine meshes.\n", "- `restrict` transfers dual quantities (residuals) from fine to coarse meshes. It is the dual of `prolong`.\n", "- `inject` transfers primal quantities from fine to coarse meshes.\n", "\n", "Most of the time, there is no need to access the interface at this level. Instead, it suffices to define the variational problem on the finest level of a mesh hierarchy and then drive the solver using PETSc options. This is the most flexible method, which we now demonstrate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "Create mesh hierarchy of two levels on an interval mesh. Create a piecewise linear function on the fine mesh and interpolate the function $f(x) = x$. Now use the grid transfer functionality to move this function to the coarse mesh (using both `restrict` and `inject`). Do you notice a difference between the two outcomes?\n", "\n", "- Hint 1: You will need to create a `FunctionSpace` on both the coarse and fine mesh.\n", "- Hint 2: use `x, = SpatialCoordinate(mesh)` to gain access to the `x` coordinate on a given mesh." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem setup\n", "\n", "We will solve the Stokes equations on a rectangular domain $\\Omega = [0, 1.5] \\times [0, 1]$. With constant inflow and outflow through two \"pipes\" and no slip boundaries everywhere else.\n", "\n", "Our problem is to find $(u, p) \\in V\\times Q$ such that:\n", "\n", "$$\n", "\\begin{align}\n", "-\\nu \\nabla^2 u + \\nabla p &= 0 \\quad \\text{in $\\Omega$},\\\\\n", "\\nabla \\cdot u &= 0 \\quad \\text{in $\\Omega$},\\\\\n", "u &= u_0 \\quad \\text{on $\\Gamma_{\\text{inout}}$},\\\\\n", "u &= (0, 0) \\quad \\text{on $\\Gamma \\setminus \\Gamma_{\\text{inout}}$},\n", "\\end{align}\n", "$$\n", "where \n", "$$\n", "\\Gamma_\\text{inout}(x, y) = \\{(x, y)\\, |\\, y \\in [1/6, 1/3] \\cup [2/3, 5/6], x \\in \\{0, 1.5\\} \\}\n", "$$\n", "and\n", "$$\n", "u_0(x, y) = \\bigg\\{\\\n", "\\begin{split} \n", "1 - (12 (y - 1/4))^2 \\quad y &< 1/2, \\\\\n", "1 - (12 (y - 3/4))^2 \\quad y &> 1/2. \\\\\n", "\\end{split}\n", "$$\n", "\n", "We will use Taylor-Hood elements. In the usual way, we multiply by test functions and after integrating by parts and incorporating boundary conditions, we arrive at the weak formulation, find $(u, p) \\in V \\times Q$ such that\n", "\n", "$$\n", "\\begin{align}\n", "\\int_\\Omega \\nu \\nabla u : \\nabla v - p\\nabla \\cdot v\\,\\text{d}x &= 0 \\quad \\forall v \\in V,\\\\\n", "\\int_\\Omega q \\nabla \\cdot u\\,\\text{d}x &= 0 \\quad \\forall q \\in Q, \\\\\n", "u &= (1, 0) \\quad \\text{on $\\Gamma_{\\text{inout}}$},\\\\\n", "u &= (0, 0) \\quad \\text{on $\\Gamma \\setminus \\Gamma_{\\text{inout}}$}.\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation\n", "\n", "To make things easier to play with, we'll wrap everything up in a function that we can call to produce a solver." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def create_solver(parameters=None):\n", " coarse_mesh = RectangleMesh(15, 10, 1.5, 1)\n", " hierarchy = MeshHierarchy(coarse_mesh, 3)\n", " \n", " mesh = hierarchy[-1]\n", " \n", " V = VectorFunctionSpace(mesh, \"Lagrange\", 2)\n", " Q = FunctionSpace(mesh, \"Lagrange\", 1)\n", " W = V*Q\n", " \n", " u, p = TrialFunctions(W)\n", " v, q = TestFunctions(W)\n", " \n", " nu = Constant(1)\n", " x, y = SpatialCoordinate(mesh)\n", " \n", " t = conditional(y < 0.5, y - 1/4, y - 3/4)\n", " gbar = conditional(Or(And(1/6 < y,\n", " y < 1/3),\n", " And(2/3 < y,\n", " y < 5/6)),\n", " 1, \n", " 0)\n", "\n", " value = as_vector([gbar*(1 - (12*t)**2), 0])\n", " bcs = [DirichletBC(W.sub(0), interpolate(value, V), (1, 2)),\n", " DirichletBC(W.sub(0), zero(2), (3, 4))]\n", " \n", " a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n", " L = inner(Constant((0, 0)), v)*dx\n", " wh = Function(W)\n", " problem = LinearVariationalProblem(a, L, wh, bcs=bcs)\n", " solver = LinearVariationalSolver(problem, solver_parameters=parameters, appctx={\"nu\": nu})\n", " return solver" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Residual norms for firedrake_0_ solve.\n", " 0 KSP Residual norm 1.021257383821e+01 \n", " 1 KSP Residual norm 6.404133272505e-12 \n" ] } ], "source": [ "solver = create_solver({\"ksp_type\": \"preonly\",\n", " \"pc_type\": \"lu\",\n", " \"pc_factor_shift_type\": \"inblocks\",\n", " \"ksp_monitor\": None,\n", " \"pmat_type\": \"aij\"})\n", "solver.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can extract the solution variable from the solver object and plot the velocity and pressure fields.\n", "In previous notebooks, we've seen how to create multiple subplots, arrange them within a figure, share the axes, set the aspect ratio, and add titles.\n", "Here we're also taking the output of each plotting function -- a set of streamlines from streamplot and a set of colored triangles from tripcolor -- and using them to add a colorbar to each subplot.\n", "When we create the colorbar, we also need to tell matplotlib which axis to draw it on.\n", "Finally, we're adjusting the size and spacing of the colorbar to make the result look nicer.\n", "Steps like this often require some trial and error, but they're essential for making publication-quality figures." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4f6ad0c79e204a479e535c7b10de85af", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from firedrake.pyplot import streamplot, tripcolor\n", "\n", "w = solver._problem.u\n", "u, p = w.subfunctions\n", "fig, axes = plt.subplots(ncols=2, sharex=True, sharey=True)\n", "streamlines = streamplot(u, resolution=1/30, seed=4, axes=axes[0])\n", "axes[0].set_aspect(\"equal\")\n", "axes[0].set_title(\"Velocity\")\n", "fig.colorbar(streamlines, ax=axes[0], fraction=0.032, pad=0.02)\n", "\n", "triangles = tripcolor(p, axes=axes[1], cmap='coolwarm')\n", "axes[1].set_aspect(\"equal\")\n", "axes[1].set_title(\"Pressure\")\n", "fig.colorbar(triangles, ax=axes[1], fraction=0.032, pad=0.02);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This direct method is not a scalable solution technique for large problems. Similar to our earlier example involving the mixed Poisson problem, a Schur complement method can be more efficient. We'll use geometric multigrid to invert the elliptic velocity block, and use a viscosity-weighted pressure mass matrix to precondition the Schur complement. This gives good results as long as viscosity contrasts are not too strong. The Python preconditioner that we use to create the mass matrix is described in more detail in the composable solvers notebook." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "class MassMatrix(AuxiliaryOperatorPC):\n", " def form(self, pc, test, trial):\n", " # Grab the viscosity\n", " nu = self.get_appctx(pc)[\"nu\"]\n", " return (nu*test*trial*dx, None)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Residual norms for firedrake_2_ solve.\n", " 0 KSP Residual norm 1.793015736715e+02 \n", " 1 KSP Residual norm 8.716276054615e+01 \n", " 2 KSP Residual norm 1.669109319703e+01 \n", " 3 KSP Residual norm 1.123132759507e+01 \n", " 4 KSP Residual norm 5.140215836973e+00 \n", " 5 KSP Residual norm 2.860823020181e+00 \n", " 6 KSP Residual norm 1.241682759464e+00 \n", " 7 KSP Residual norm 7.188188009066e-01 \n", " 8 KSP Residual norm 4.044004541226e-01 \n", " 9 KSP Residual norm 1.795717211991e-01 \n", " 10 KSP Residual norm 1.184207774231e-01 \n", " 11 KSP Residual norm 7.266399169233e-02 \n", " 12 KSP Residual norm 4.748358849735e-02 \n", " 13 KSP Residual norm 2.947011552603e-02 \n", " 14 KSP Residual norm 1.649709615925e-02 \n", " 15 KSP Residual norm 9.288959964288e-03 \n", " 16 KSP Residual norm 5.115169026184e-03 \n", " 17 KSP Residual norm 2.604131289585e-03 \n", " 18 KSP Residual norm 1.155550083370e-03 \n", " 19 KSP Residual norm 4.714818940666e-04 \n", " 20 KSP Residual norm 2.159213406651e-04 \n", " 21 KSP Residual norm 1.075446620939e-04 \n", " 22 KSP Residual norm 5.902061753908e-05 \n", " 23 KSP Residual norm 2.906398089999e-05 \n", " 24 KSP Residual norm 1.199566391672e-05 \n" ] } ], "source": [ "parameters = {\n", " \"ksp_type\": \"gmres\",\n", " \"ksp_monitor\": None,\n", " \"pc_type\": \"fieldsplit\",\n", " \"pc_use_amat\": True,\n", " \"pc_fieldsplit_type\": \"schur\",\n", " \"pc_fieldsplit_schur_fact_type\": \"lower\",\n", " \"fieldsplit_0_ksp_type\": \"preonly\",\n", " \"fieldsplit_0_pc_type\": \"mg\",\n", " \"fieldsplit_1_ksp_type\": \"preonly\",\n", " \"fieldsplit_1_pc_type\": \"python\",\n", " \"fieldsplit_1_pc_python_type\": \"__main__.MassMatrix\",\n", " \"fieldsplit_1_aux_pc_type\": \"icc\",\n", "}\n", "\n", "solver = create_solver(parameters)\n", "solver.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another option is to use a distributive smoother. Instead of using a Schur complement on the outside and multigrid for the velocity block, we can instead use multigrid on the outside and Schur complements as a \"smoother\" on each level. This requires more parameters, but no other change in our problem setup.\n", "\n", "Notice how we provide the `MassMatrix` Python preconditioner on each level for the Schur complement. An appropriate operator will be created on each level as necessary." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jack/Documents/firedrake/firedrake/src/firedrake/firedrake/dmhooks.py:270: RuntimeWarning: Creating new TransferManager to transfer data to coarse grids\n", " warnings.warn(\"Creating new TransferManager to transfer data to coarse grids\", RuntimeWarning)\n", "/home/jack/Documents/firedrake/firedrake/src/firedrake/firedrake/dmhooks.py:271: RuntimeWarning: This might be slow (you probably want to save it on an appctx)\n", " warnings.warn(\"This might be slow (you probably want to save it on an appctx)\", RuntimeWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Residual norms for firedrake_3_ solve.\n", " 0 KSP Residual norm 1.021257383821e+01 \n", " 1 KSP Residual norm 6.150697152777e+00 \n", " 2 KSP Residual norm 6.499187546880e-01 \n", " 3 KSP Residual norm 4.689516571282e-02 \n", " 4 KSP Residual norm 7.942948895085e-03 \n", " 5 KSP Residual norm 1.233190521507e-03 \n", " 6 KSP Residual norm 1.185329361920e-04 \n", " 7 KSP Residual norm 2.350517854641e-05 \n", " 8 KSP Residual norm 7.877964594448e-06 \n", " 9 KSP Residual norm 3.978917640367e-06 \n", " 10 KSP Residual norm 1.018158170524e-06 \n" ] } ], "source": [ "parameters = {\n", " \"ksp_type\": \"fgmres\",\n", " \"ksp_monitor\": None,\n", " \"mat_type\": \"nest\",\n", " \"pc_type\": \"mg\",\n", " \"mg_coarse_ksp_type\": \"preonly\",\n", " \"mg_coarse_pc_type\": \"fieldsplit\",\n", " \"mg_coarse_pc_fieldsplit_type\": \"schur\",\n", " \"mg_coarse_pc_fieldsplit_schur_fact_type\": \"full\",\n", " \"mg_coarse_fieldsplit_0_ksp_type\": \"preonly\",\n", " \"mg_coarse_fieldsplit_0_pc_type\": \"lu\",\n", " \"mg_coarse_fieldsplit_1_ksp_type\": \"richardson\",\n", " \"mg_coarse_fieldsplit_1_ksp_richardson_self_scale\": True,\n", " \"mg_coarse_fieldsplit_1_ksp_max_it\": 5,\n", " \"mg_coarse_fieldsplit_1_pc_type\": \"none\",\n", " \"mg_levels_ksp_type\": \"richardson\",\n", " \"mg_levels_ksp_richardson_self_scale\": True,\n", " \"mg_levels_ksp_max_it\": 1,\n", " \"mg_levels_pc_type\": \"fieldsplit\",\n", " \"mg_levels_pc_fieldsplit_type\": \"schur\",\n", " \"mg_levels_pc_fieldsplit_schur_fact_type\": \"upper\",\n", " \"mg_levels_fieldsplit_0_ksp_type\": \"preonly\",\n", " \"mg_levels_fieldsplit_0_pc_type\": \"bjacobi\",\n", " \"mg_levels_fieldsplit_0_sub_pc_type\": \"ilu\",\n", " \"mg_levels_fieldsplit_1_ksp_type\": \"preonly\",\n", " \"mg_levels_fieldsplit_1_pc_type\": \"python\",\n", " \"mg_levels_fieldsplit_1_pc_python_type\": \"__main__.MassMatrix\",\n", " \"mg_levels_fieldsplit_1_aux_pc_type\": \"icc\",\n", "}\n", "\n", "solver = create_solver(parameters)\n", "solver.solve()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise\n", "\n", "In most real-world scenarios, the viscosity $\\nu$ will not be constant, but rather variable. See what happens to the performance of the solver when you replace the constant viscosity $\\nu$ with a spatially varying one. In particular, try:\n", "\n", "$$\n", "\\nu(x, y) = \\bigg\\{\\begin{split}\n", "&100 \\quad&\\text{if $(x - 0.5)^2 + (y - 0.5)^2 < 0.25$},\\\\\n", "&1 \\quad&\\text{otherwise.}\n", "\\end{split}\n", "$$\n", "\n", "- Hint 1: Use `conditional` to produce an expression that varies the viscosity spatially.\n", "- Hint 2: You can determine the iteration count after the solver has finished using `solver.snes.ksp.getIterationNumber()`.\n", "\n", "Here are some questions you might consider:\n", "\n", "- What happens to the iteration count when you increase the number levels in the hierarchy?\n", "\n", "- What if you change the viscosity contrast to 1000, or 10000?\n", "\n", "- Compare with algebraic multigrid to solve the velocity block (use `\"fieldsplit_0_pc_type\": \"hypre\"` instead of `\"fieldsplit_0_pc_type\": \"mg\"`).\n", "\n", "For simplicity, the relevant setup is copied below to start with:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def create_solver(parameters=None):\n", " coarse_mesh = RectangleMesh(15, 10, 1.5, 1)\n", " hierarchy = MeshHierarchy(coarse_mesh, 3)\n", " \n", " mesh = hierarchy[-1]\n", " \n", " V = VectorFunctionSpace(mesh, \"Lagrange\", 2)\n", " Q = FunctionSpace(mesh, \"Lagrange\", 1)\n", " W = V*Q\n", " \n", " u, p = TrialFunctions(W)\n", " v, q = TestFunctions(W)\n", " \n", " # Change me to spatially varying.\n", " nu = Constant(1)\n", " x, y = SpatialCoordinate(mesh)\n", " \n", " t = conditional(y < 0.5, y - 1/4, y - 3/4)\n", " gbar = conditional(Or(And(1/6 < y,\n", " y < 1/3),\n", " And(2/3 < y,\n", " y < 5/6)),\n", " 1, \n", " 0)\n", "\n", " value = as_vector([gbar*(1 - (12*t)**2), 0])\n", " bcs = [DirichletBC(W.sub(0), interpolate(value, V), (1, 2)),\n", " DirichletBC(W.sub(0), zero(2), (3, 4))]\n", " \n", " a = (nu*inner(grad(u), grad(v)) - p*div(v) + q*div(u))*dx\n", " L = inner(Constant((0, 0)), v)*dx\n", " \n", " wh = Function(W)\n", " problem = LinearVariationalProblem(a, L, wh, bcs=bcs)\n", " solver = LinearVariationalSolver(problem, solver_parameters=parameters, appctx={\"nu\": nu})\n", " return solver" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Residual norms for firedrake_4_ solve.\n", " 0 KSP Residual norm 1.793015736715e+02 \n", " 1 KSP Residual norm 8.716276054615e+01 \n", " 2 KSP Residual norm 1.669109319703e+01 \n", " 3 KSP Residual norm 1.123132759507e+01 \n", " 4 KSP Residual norm 5.140215836973e+00 \n", " 5 KSP Residual norm 2.860823020181e+00 \n", " 6 KSP Residual norm 1.241682759464e+00 \n", " 7 KSP Residual norm 7.188188009066e-01 \n", " 8 KSP Residual norm 4.044004541226e-01 \n", " 9 KSP Residual norm 1.795717211991e-01 \n", " 10 KSP Residual norm 1.184207774231e-01 \n", " 11 KSP Residual norm 7.266399169233e-02 \n", " 12 KSP Residual norm 4.748358849735e-02 \n", " 13 KSP Residual norm 2.947011552603e-02 \n", " 14 KSP Residual norm 1.649709615925e-02 \n", " 15 KSP Residual norm 9.288959964288e-03 \n", " 16 KSP Residual norm 5.115169026184e-03 \n", " 17 KSP Residual norm 2.604131289585e-03 \n", " 18 KSP Residual norm 1.155550083370e-03 \n", " 19 KSP Residual norm 4.714818940666e-04 \n", " 20 KSP Residual norm 2.159213406651e-04 \n", " 21 KSP Residual norm 1.075446620939e-04 \n", " 22 KSP Residual norm 5.902061753908e-05 \n", " 23 KSP Residual norm 2.906398089999e-05 \n", " 24 KSP Residual norm 1.199566391672e-05 \n" ] } ], "source": [ "parameters = {\n", " \"ksp_type\": \"gmres\",\n", " \"ksp_monitor\": None,\n", " \"pc_type\": \"fieldsplit\",\n", " \"pc_use_amat\": True,\n", " \"pc_fieldsplit_type\": \"schur\",\n", " \"pc_fieldsplit_schur_fact_type\": \"lower\",\n", " \"fieldsplit_0_ksp_type\": \"preonly\",\n", " \"fieldsplit_0_pc_type\": \"mg\",\n", " \"fieldsplit_1_ksp_type\": \"preonly\",\n", " \"fieldsplit_1_pc_type\": \"python\",\n", " \"fieldsplit_1_pc_python_type\": \"__main__.MassMatrix\",\n", " \"fieldsplit_1_aux_pc_type\": \"icc\",\n", "}\n", "\n", "solver = create_solver(parameters)\n", "solver.solve()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 4 }