{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimal complexity finite element assembly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sum factorisation\n", "Suppose we are assemblying the 2-form of the Laplace operator on a hexahedral element, e.g. the Q3 element:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\"Q3\"
\n", "
$Q_3$ element on hexahedron. Image from the periodic table.
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since each quadrature point $X$ can be represented as a 3-tuple $q=\\{q_1,q_2,q_3\\}$, and each basis function by a 3-tuple $i = \\{i_1,i_2,i_3\\}$, the naive local assembly kernel for the local tensor $A_{ij}$ contains the loop structure:\n", "```\n", "for q1, q2, q3\n", " for i1, i2, i3\n", " for j1, j2, j3\n", " A[i1,i2,i3,j1,j2,j3] += ...\n", "```\n", "This requires $O(N_{q}^3N_{i}^6)$ FLOPs. For polynomial degree $p$, both $N_q$ and $N_i$ are $O(p)$, so this local assembly requires $O(p^9)$ FLOPs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For *tensor product elements* like this, we can rearrange the contraction over quadrature points and hoist invariant sub-expressions out of the innermost loop into temporary variables. This is known as *sum factorisation*:\n", "```\n", "for q1, i1, j1\n", " t1[i1,j1] += ...\n", "for q2, i2, j2\n", " t2[i2,j2] += ...\n", "for q3\n", " for i1, i2, i3\n", " for j1, j2, j3\n", " A[i1,i2,i3,j1,j2,j3] += t1*t2*...\n", "```\n", "This reduces the complexity to $O(p^7)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TSFC \\[1\\], the form compiler of Firedrake, is capable of exploiting this intrinsic structure of the finite element, provided by FInAT \\[2\\], and apply sum factorisation automatically to generate assembly kernels with optimal algorithmic complexity." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib widget\n", "%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams['figure.figsize'] = (11, 6)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from firedrake import *\n", "set_log_level(ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can create a hexahedral mesh by extruding a quadrilateral mesh." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "mesh = ExtrudedMesh(UnitSquareMesh(10, 10, quadrilateral=True), 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's choose the continuous Lagrange element of degree 5 as our function space." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "p = 5\n", "V = FunctionSpace(mesh, \"CG\", p)\n", "u = TrialFunction(V)\n", "v = TestFunction(V)\n", "a = dot(grad(u), grad(v)) *dx # Laplace operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firedrake internalises the process of local assembly. In order to look at the kernel, we need to import the compilation interface from TSFC." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from tsfc import compile_form" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TSFC organises the optimisation passes into *modes*. Let's first try the *vanilla* mode, which does as little optimisation as possible:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "kernel_vanilla, = compile_form(a, parameters={\"mode\": \"vanilla\"})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TSFC also lets us estimate the number of FLOPs performed by the kernel:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local assembly FLOPs with vanilla mode is 4.45e+08\n" ] } ], "source": [ "print(\"Local assembly FLOPs with vanilla mode is {0:.3g}\".format(kernel_vanilla.flop_count))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default optimisation mode in TSFC is *spectral*, which applies sum factorisation to determine the tensor contraction order, and at each level, apply *argument factorisation* \\[3\\] to rearrange the expression using associative and distributive laws. Since *spectral* is the default mode, we do not need to specify it in the parameters." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local assembly FLOPs with spectral mode is 5.45e+06\n" ] } ], "source": [ "kernel_spectral, = compile_form(a)\n", "print(\"Local assembly FLOPs with spectral mode is {0:.3g}\".format(kernel_spectral.flop_count))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a 43x reduction in FLOPs. Not bad, but there's opportunity to do better. For spectral elements, if we use the Gauss–Lobatto–Legendre (GLL) quadrature scheme, which has quadrature points collated with the Lagrange basis function nodes, then we know that the basis function tabulation is an indentity matrix. TSFC and FInAT can further simplify the loop structure of the local assembly kernels. This reduces the complexity to $O(p^5)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to specify the GLL quadrature scheme for hexahedra. We can do this with FIAT, which defines GLL on intervals, and FInAT, which makes the tensor product scheme from the interval scheme." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import FIAT, finat\n", "\n", "def gauss_lobatto_legendre_line_rule(degree):\n", " fiat_make_rule = FIAT.quadrature.GaussLobattoLegendreQuadratureLineRule\n", " fiat_rule = fiat_make_rule(FIAT.ufc_simplex(1), degree + 1)\n", " finat_ps = finat.point_set.GaussLobattoLegendrePointSet\n", " points = finat_ps(fiat_rule.get_points())\n", " weights = fiat_rule.get_weights()\n", " return finat.quadrature.QuadratureRule(points, weights)\n", "\n", "def gauss_lobatto_legendre_cube_rule(dimension, degree):\n", " make_tensor_rule = finat.quadrature.TensorProductQuadratureRule\n", " result = gauss_lobatto_legendre_line_rule(degree)\n", " for _ in range(1, dimension):\n", " line_rule = gauss_lobatto_legendre_line_rule(degree)\n", " result = make_tensor_rule([result, line_rule])\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by creating the spectral finite element function space of the same polynomial degree." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "element = FiniteElement('CG', mesh.ufl_cell(), degree=p, variant='spectral')\n", "V = FunctionSpace(mesh, element)\n", "u = TrialFunction(V)\n", "v = TestFunction(V)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to tell Firedrake to use the GLL quadratures for numerical integration." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "gll_quadrature_rule = gauss_lobatto_legendre_cube_rule(dimension=3, degree=p)\n", "a_gll = dot(grad(u), grad(v)) *dx(scheme=gll_quadrature_rule)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local assembly FLOPs with GLL quadrature is 2.14e+05\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n" ] } ], "source": [ "kernel_gll, = compile_form(a_gll)\n", "print(\"Local assembly FLOPs with GLL quadrature is {0:.3g}\".format(kernel_gll.flop_count))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a further 10x reduction in FLOPs.\n", "\n", "Now, let's verify that we achieve the expected asymptotic algorithmic complexity with respect to polynomial degrees." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "import matplotlib.pyplot as plt\n", "import numpy" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n" ] } ], "source": [ "flops = defaultdict(list)\n", "ps = range(1, 33) # polynomial degrees\n", "modes = {\n", " 'gll': {'mode': 'spectral', 'variant': 'spectral', 'rule': gauss_lobatto_legendre_cube_rule},\n", " 'spectral': {'mode': 'spectral', 'variant': None, 'rule': lambda *args: None},\n", " 'vanilla': {'mode': 'vanilla', 'variant': None, 'rule': lambda *args: None}\n", "}\n", "\n", "for p in ps:\n", " for mode in modes:\n", " element = FiniteElement('CG', mesh.ufl_cell(), degree=p, variant=modes[mode]['variant'])\n", " V = FunctionSpace(mesh, element)\n", " u = TrialFunction(V)\n", " v = TestFunction(V)\n", " a = dot(grad(u), grad(v))*dx(scheme=modes[mode]['rule'](3, p))\n", " kernel, = compile_form(a, parameters={\"mode\": modes[mode]['mode']})\n", " flops[mode].append(kernel.flop_count)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d4fa8aa7c77a4fcdab3054cdf712d823", "version_major": 2, "version_minor": 0 }, "image/png": 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\n", " Figure\n", "
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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "ax.set_xscale('log')\n", "ax.set_yscale('log')\n", "for mode in modes:\n", " ax.plot(ps, flops[mode], label=mode)\n", "x = numpy.linspace(1, 32, 100)\n", "for p, style, offset in zip([5,7,9], ['-.','--',':'], [10, 3, 5]):\n", " ax.plot(x, numpy.power(x, p)*offset, label=r\"$p^{0}$\".format(p), color='grey', linestyle=style)\n", "ax.legend(loc='upper left');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's do the same analysis on a different problem: $a (u,v) = \\int_\\Omega\\left( \\nabla\\times u \\right) \\cdot \\left( \\nabla\\times v \\right)\\,\\text{d}x$, on the H(curl) conforming NCE element." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n", "/Users/dham/src/fd-docs/fd-docs/lib/python3.12/site-packages/ufl/utils/sorting.py:88: UserWarning: Applying str() to a metadata value of type TensorProductQuadratureRule, don't know if this is safe.\n", " warnings.warn(\n" ] } ], "source": [ "# This might take some time to run\n", "flops_curl = defaultdict(list)\n", "ps_curl = range(1, 17)\n", "for p in ps_curl:\n", " for mode in modes:\n", " element = FiniteElement('NCE', mesh.ufl_cell(), degree=p, variant=modes[mode]['variant'])\n", " V = FunctionSpace(mesh, element)\n", " u = TrialFunction(V)\n", " v = TestFunction(V)\n", " a = dot(curl(u), curl(v))*dx(scheme=modes[mode]['rule'](3, p))\n", " kernel, = compile_form(a, parameters={\"mode\": modes[mode]['mode']})\n", " flops_curl[mode].append(kernel.flop_count)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ba27e01f46074c17a478ccf9c14fe3ab", "version_major": 2, "version_minor": 0 }, "image/png": 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