def _apply_face_mass_operator(dcoll: DiscretizationCollection, dd, vec):
if not isinstance(vec, DOFArray):
return map_array_container(
partial(_apply_face_mass_operator, dcoll, dd), vec)
from grudge.geometry import area_element
volm_discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME)
face_discr = dcoll.discr_from_dd(dd)
dtype = vec.entry_dtype
actx = vec.array_context
assert len(face_discr.groups) == len(volm_discr.groups)
surf_area_elements = area_element(
actx,
dcoll,
dd=dd,
_use_geoderiv_connection=actx.supports_nonscalar_broadcasting)
return DOFArray(
actx,
data=tuple(
actx.einsum("ifj,fej,fej->ei",
reference_face_mass_matrix(actx,
face_element_group=afgrp,
vol_element_group=vgrp,
dtype=dtype),
surf_ae_i.reshape(vgrp.mesh_el_group.nfaces,
vgrp.nelements, -1),
vec_i.reshape(vgrp.mesh_el_group.nfaces,
vgrp.nelements, afgrp.nunit_dofs),
arg_names=("ref_face_mass_mat", "jac_surf", "vec"),
tagged=(FirstAxisIsElementsTag(), )) for vgrp, afgrp,
vec_i, surf_ae_i in zip(volm_discr.groups, face_discr.groups, vec,
surf_area_elements)))
def _apply_mass_operator(dcoll: DiscretizationCollection, dd_out, dd_in, vec):
if not isinstance(vec, DOFArray):
return map_array_container(
partial(_apply_mass_operator, dcoll, dd_out, dd_in), vec)
from grudge.geometry import area_element
in_discr = dcoll.discr_from_dd(dd_in)
out_discr = dcoll.discr_from_dd(dd_out)
actx = vec.array_context
area_elements = area_element(
actx,
dcoll,
dd=dd_in,
_use_geoderiv_connection=actx.supports_nonscalar_broadcasting)
return DOFArray(
actx,
data=tuple(
actx.einsum("ij,ej,ej->ei",
reference_mass_matrix(actx,
out_element_group=out_grp,
in_element_group=in_grp),
ae_i,
vec_i,
arg_names=("mass_mat", "jac", "vec"),
tagged=(FirstAxisIsElementsTag(), ))
for in_grp, out_grp, ae_i, vec_i in zip(
in_discr.groups, out_discr.groups, area_elements, vec)))
def _apply_mass_operator(dcoll: DiscretizationCollection, dd_out, dd_in, vec):
if isinstance(vec, np.ndarray):
return obj_array_vectorize(
lambda vi: _apply_mass_operator(dcoll, dd_out, dd_in, vi), vec)
from grudge.geometry import area_element
in_discr = dcoll.discr_from_dd(dd_in)
out_discr = dcoll.discr_from_dd(dd_out)
actx = vec.array_context
area_elements = area_element(actx, dcoll, dd=dd_in)
return DOFArray(
actx,
data=tuple(
actx.einsum("ij,ej,ej->ei",
reference_mass_matrix(actx,
out_element_group=out_grp,
in_element_group=in_grp),
ae_i,
vec_i,
arg_names=("mass_mat", "jac", "vec"),
tagged=(FirstAxisIsElementsTag(), ))
for in_grp, out_grp, ae_i, vec_i in zip(
in_discr.groups, out_discr.groups, area_elements, vec)))
def _apply_stiffness_transpose_operator(dcoll: DiscretizationCollection,
dd_out, dd_in, vec, xyz_axis):
from grudge.geometry import \
inverse_surface_metric_derivative, area_element
in_discr = dcoll.discr_from_dd(dd_in)
out_discr = dcoll.discr_from_dd(dd_out)
actx = vec.array_context
area_elements = area_element(actx, dcoll, dd=dd_in)
inverse_jac_t = actx.np.stack([
inverse_surface_metric_derivative(actx,
dcoll,
rst_axis,
xyz_axis,
dd=dd_in)
for rst_axis in range(dcoll.dim)
])
return DOFArray(
actx,
data=tuple(
actx.einsum(
"dij,ej,ej,dej->ei",
reference_stiffness_transpose_matrix(
actx, out_element_group=out_grp, in_element_group=in_grp),
ae_i,
vec_i,
inv_jac_t_i,
arg_names=("ref_stiffT_mat", "jac", "vec", "inv_jac_t"),
tagged=(FirstAxisIsElementsTag(), ))
for out_grp, in_grp, vec_i, ae_i, inv_jac_t_i in zip(
out_discr.groups, in_discr.groups, vec, area_elements,
inverse_jac_t)))
def _apply_face_mass_operator(dcoll: DiscretizationCollection, dd, vec):
if isinstance(vec, np.ndarray):
return obj_array_vectorize(
lambda vi: _apply_face_mass_operator(dcoll, dd, vi), vec)
from grudge.geometry import area_element
volm_discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME)
face_discr = dcoll.discr_from_dd(dd)
dtype = vec.entry_dtype
actx = vec.array_context
@memoize_in(actx, (_apply_face_mass_operator, "face_mass_knl"))
def prg():
t_unit = make_loopy_program([
"{[iel]: 0 <= iel < nelements}", "{[f]: 0 <= f < nfaces}",
"{[idof]: 0 <= idof < nvol_nodes}",
"{[jdof]: 0 <= jdof < nface_nodes}"
],
"""
result[iel, idof] = sum(f, sum(jdof, mat[idof, f, jdof]
* jac_surf[f, iel, jdof]
* vec[f, iel, jdof]))
""",
name="face_mass")
import loopy as lp
from meshmode.transform_metadata import (ConcurrentElementInameTag,
ConcurrentDOFInameTag)
return lp.tag_inames(t_unit, {
"iel": ConcurrentElementInameTag(),
"idof": ConcurrentDOFInameTag()
})
assert len(face_discr.groups) == len(volm_discr.groups)
surf_area_elements = area_element(actx, dcoll, dd=dd)
return DOFArray(
actx,
data=tuple(
actx.call_loopy(
prg(),
mat=reference_face_mass_matrix(actx,
face_element_group=afgrp,
vol_element_group=vgrp,
dtype=dtype),
jac_surf=surf_ae_i.reshape(vgrp.mesh_el_group.nfaces,
vgrp.nelements, afgrp.nunit_dofs),
vec=vec_i.reshape(vgrp.mesh_el_group.nfaces, vgrp.nelements,
afgrp.nunit_dofs))["result"] for vgrp, afgrp,
vec_i, surf_ae_i in zip(volm_discr.groups, face_discr.groups, vec,
surf_area_elements)))
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dim = 2
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 3
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.75 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.45 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
print("%d elements" % mesh.nelements)
dcoll = DiscretizationCollection(actx, mesh, order=order)
fields = flat_obj_array(bump(actx, dcoll),
[dcoll.zeros(actx) for i in range(dcoll.dim)])
vis = make_visualizer(dcoll)
def rhs(t, w):
return wave_operator(dcoll, c=1, w=w)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
print(f"step: {istep} t: {t} L2: {op.norm(dcoll, fields[0], 2)} "
f"sol max: {op.nodal_max(dcoll, 'vol', fields[0])}")
vis.write_vtk_file("fld-wave-eager-%04d.vtu" % istep, [
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def _apply_inverse_mass_operator(dcoll: DiscretizationCollection, dd_out,
dd_in, vec):
if isinstance(vec, np.ndarray):
return obj_array_vectorize(
lambda vi: _apply_inverse_mass_operator(dcoll, dd_out, dd_in, vi),
vec)
from grudge.geometry import area_element
if dd_out != dd_in:
raise ValueError("Cannot compute inverse of a mass matrix mapping "
"between different element groups; inverse is not "
"guaranteed to be well-defined")
actx = vec.array_context
discr = dcoll.discr_from_dd(dd_in)
inv_area_elements = 1. / area_element(actx, dcoll, dd=dd_in)
group_data = []
for grp, jac_inv, vec_i in zip(discr.groups, inv_area_elements, vec):
ref_mass_inverse = reference_inverse_mass_matrix(actx,
element_group=grp)
group_data.append(
# Based on https://arxiv.org/pdf/1608.03836.pdf
# true_Minv ~ ref_Minv * ref_M * (1/jac_det) * ref_Minv
actx.einsum("ei,ij,ej->ei",
jac_inv,
ref_mass_inverse,
vec_i,
tagged=(FirstAxisIsElementsTag(), )))
return DOFArray(actx, data=tuple(group_data))
def h_min_from_volume(dcoll: DiscretizationCollection,
dim=None,
dd=None) -> float:
"""Returns a (minimum) characteristic length based on the volume of the
elements. This length may not be representative if the elements have very
high aspect ratios.
:arg dim: an integer denoting topological dimension. If *None*, the
spatial dimension specified by
:attr:`grudge.DiscretizationCollection.dim` is used.
:arg dd: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
Defaults to the base volume discretization if not provided.
:returns: a scalar denoting the minimum characteristic length.
"""
from grudge.reductions import nodal_min, elementwise_sum
if dd is None:
dd = DD_VOLUME
dd = as_dofdesc(dd)
if dim is None:
dim = dcoll.dim
ones = dcoll.discr_from_dd(dd).zeros(dcoll._setup_actx) + 1.0
return nodal_min(dcoll, dd, elementwise_sum(dcoll,
op.mass(dcoll, dd,
ones)))**(1.0 / dim)
def __init__(self,
dcoll: DiscretizationCollection,
remote_rank,
vol_field,
tag=None):
self.tag = self.base_tag
if tag is not None:
self.tag += tag
self.dcoll = dcoll
self.array_context = vol_field.array_context
self.remote_btag = BTAG_PARTITION(remote_rank)
self.bdry_discr = dcoll.discr_from_dd(self.remote_btag)
from grudge.op import project
self.local_dof_array = project(dcoll, "vol", self.remote_btag,
vol_field)
local_data = self.array_context.to_numpy(flatten(self.local_dof_array))
comm = self.dcoll.mpi_communicator
self.send_req = comm.Isend(local_data, remote_rank, tag=self.tag)
self.remote_data_host = np.empty_like(local_data)
self.recv_req = comm.Irecv(self.remote_data_host, remote_rank,
self.tag)
def integral(dcoll: DiscretizationCollection, dd, vec) -> float:
"""Numerically integrates a function represented by a
:class:`~meshmode.dof_array.DOFArray` of degrees of freedom.
:arg dd: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
:arg vec: a :class:`~meshmode.dof_array.DOFArray`
:returns: a scalar denoting the evaluated integral.
"""
from grudge.op import _apply_mass_operator
dd = dof_desc.as_dofdesc(dd)
ones = dcoll.discr_from_dd(dd).zeros(vec.array_context) + 1.0
return nodal_sum(dcoll, dd,
vec * _apply_mass_operator(dcoll, dd, dd, ones))
def __init__(self,
dcoll: DiscretizationCollection,
array_container: ArrayOrContainerT,
remote_rank, tag=None):
actx = get_container_context_recursively(array_container)
btag = BTAG_PARTITION(remote_rank)
local_bdry_data = project(dcoll, "vol", btag, array_container)
comm = dcoll.mpi_communicator
self.dcoll = dcoll
self.array_context = actx
self.remote_btag = btag
self.bdry_discr = dcoll.discr_from_dd(btag)
self.local_bdry_data = local_bdry_data
self.local_bdry_data_np = \
to_numpy(flatten(self.local_bdry_data, actx), actx)
self.tag = self.base_tag
if tag is not None:
self.tag += tag
# Here, we initialize both send and recieve operations through
# mpi4py `Request` (MPI_Request) instances for comm.Isend (MPI_Isend)
# and comm.Irecv (MPI_Irecv) respectively. These initiate non-blocking
# point-to-point communication requests and require explicit management
# via the use of wait (MPI_Wait, MPI_Waitall, MPI_Waitany, MPI_Waitsome),
# test (MPI_Test, MPI_Testall, MPI_Testany, MPI_Testsome), and cancel
# (MPI_Cancel). The rank-local data `self.local_bdry_data_np` will have its
# associated memory buffer sent across connected ranks and must not be
# modified at the Python level during this process. Completion of the
# requests is handled in :meth:`finish`.
#
# For more details on the mpi4py semantics, see:
# https://mpi4py.readthedocs.io/en/stable/overview.html#nonblocking-communications
#
# NOTE: mpi4py currently (2021-11-03) holds a reference to the send
# memory buffer for (i.e. `self.local_bdry_data_np`) until the send
# requests is complete, however it is not clear that this is documented
# behavior. We hold on to the buffer (via the instance attribute)
# as well, just in case.
self.send_req = comm.Isend(self.local_bdry_data_np,
remote_rank,
tag=self.tag)
self.remote_data_host_numpy = np.empty_like(self.local_bdry_data_np)
self.recv_req = comm.Irecv(self.remote_data_host_numpy,
remote_rank,
tag=self.tag)
def elementwise_integral(dcoll: DiscretizationCollection, dd, vec) -> DOFArray:
"""Numerically integrates a function represented by a
:class:`~meshmode.dof_array.DOFArray` of degrees of freedom in
each element of a discretization, given by *dd*.
:arg dd: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
:arg vec: a :class:`~meshmode.dof_array.DOFArray`
:returns: a :class:`~meshmode.dof_array.DOFArray` containing the
elementwise integral if *vec*.
"""
from grudge.op import _apply_mass_operator
dd = dof_desc.as_dofdesc(dd)
ones = dcoll.discr_from_dd(dd).zeros(vec.array_context) + 1.0
return elementwise_sum(dcoll, dd,
vec * _apply_mass_operator(dcoll, dd, dd, ones))
def elementwise_integral(dcoll: DiscretizationCollection,
*args) -> ArrayOrContainerT:
"""Numerically integrates a function represented by a
:class:`~meshmode.dof_array.DOFArray` of degrees of freedom in
each element of a discretization, given by *dd*.
May be called with ``(vec)`` or ``(dd, vec)``.
The input *vec* can either be a :class:`~meshmode.dof_array.DOFArray` or
an :class:`~arraycontext.container.ArrayContainer` with
:class:`~meshmode.dof_array.DOFArray` entries. If the underlying
array context (see :class:`arraycontext.ArrayContext`) for *vec*
supports nonscalar broadcasting, all :class:`~meshmode.dof_array.DOFArray`
entries will contain a single value for each element. Otherwise, the
entries will have the same number of degrees of freedom as *vec*, but
set to the same value.
:arg dcoll: a :class:`grudge.discretization.DiscretizationCollection`.
:arg dd: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
Defaults to the base volume discretization if not provided.
:arg vec: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` of them.
:returns: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` like *vec* containing the
elementwise integral if *vec*.
"""
if len(args) == 1:
vec, = args
dd = dof_desc.DOFDesc("vol", dof_desc.DISCR_TAG_BASE)
elif len(args) == 2:
dd, vec = args
else:
raise TypeError("invalid number of arguments")
dd = dof_desc.as_dofdesc(dd)
from grudge.op import _apply_mass_operator
ones = dcoll.discr_from_dd(dd).zeros(vec.array_context) + 1.0
return elementwise_sum(dcoll, dd,
vec * _apply_mass_operator(dcoll, dd, dd, ones))
def _compute_local_gradient(dcoll: DiscretizationCollection, vec, xyz_axis):
from grudge.geometry import inverse_surface_metric_derivative
discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME)
actx = vec.array_context
inverse_jac_t = actx.np.stack([
inverse_surface_metric_derivative(actx, dcoll, rst_axis, xyz_axis)
for rst_axis in range(dcoll.dim)
])
return DOFArray(actx,
data=tuple(
actx.einsum("dei,dij,ej->ei",
inv_jac_t_i,
reference_derivative_matrices(actx, grp),
vec_i,
arg_names=("inv_jac_t", "ref_diff_mat",
"vec"),
tagged=(FirstAxisIsElementsTag(), ))
for grp, vec_i, inv_jac_t_i in zip(
discr.groups, vec, inverse_jac_t)))
def dt_non_geometric_factors(dcoll: DiscretizationCollection, dd=None) -> list:
r"""Computes the non-geometric scale factors following [Hesthaven_2008]_,
section 6.4, for each element group in the *dd* discretization:
.. math::
c_{ng} = \operatorname{min}\left( \Delta r_i \right),
where :math:`\Delta r_i` denotes the distance between two distinct
nodal points on the reference element.
:arg dd: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
Defaults to the base volume discretization if not provided.
:returns: a :class:`list` of :class:`float` values denoting the minimum
node distance on the reference element for each group.
"""
if dd is None:
dd = DD_VOLUME
discr = dcoll.discr_from_dd(dd)
min_delta_rs = []
for grp in discr.groups:
nodes = np.asarray(list(zip(*grp.unit_nodes)))
nnodes = grp.nunit_dofs
# NOTE: order 0 elements have 1 node located at the centroid of
# the reference element and is equidistant from each vertex
if grp.order == 0:
assert nnodes == 1
min_delta_rs.append(
np.linalg.norm(nodes[0] -
grp.mesh_el_group.vertex_unit_coordinates()[0]))
else:
min_delta_rs.append(
min(
np.linalg.norm(nodes[i] - nodes[j]) for i in range(nnodes)
for j in range(nnodes) if i != j))
return min_delta_rs
def project(dcoll: DiscretizationCollection, src, tgt,
vec) -> ArrayOrContainerT:
"""Project from one discretization to another, e.g. from the
volume to the boundary, or from the base to the an overintegrated
quadrature discretization.
:arg src: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
:arg tgt: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
:arg vec: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` of them.
:returns: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` like *vec*.
"""
src = as_dofdesc(src)
tgt = as_dofdesc(tgt)
if isinstance(vec, Number) or src == tgt:
return vec
if not isinstance(vec, DOFArray):
return map_array_container(partial(project, dcoll, src, tgt), vec)
return dcoll.connection_from_dds(src, tgt)(vec)
def local_d_dx(dcoll: DiscretizationCollection, xyz_axis,
vec) -> ArrayOrContainerT:
r"""Return the element-local derivative along axis *xyz_axis* of a
function :math:`f` represented by *vec*:
.. math::
\frac{\partial f}{\partial \lbrace x,y,z\rbrace}\Big|_E
:arg xyz_axis: an integer indicating the axis along which the derivative
is taken.
:arg vec: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` of them.
:returns: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` of them.
"""
if not isinstance(vec, DOFArray):
return map_array_container(partial(local_d_dx, dcoll, xyz_axis), vec)
discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME)
actx = vec.array_context
from grudge.geometry import inverse_surface_metric_derivative_mat
inverse_jac_mat = inverse_surface_metric_derivative_mat(
actx,
dcoll,
_use_geoderiv_connection=actx.supports_nonscalar_broadcasting)
return _single_axis_derivative_kernel(actx,
discr,
discr,
_reference_derivative_matrices,
inverse_jac_mat,
xyz_axis,
vec,
metric_in_matvec=False)
def project(dcoll: DiscretizationCollection, src, tgt, vec):
"""Project from one discretization to another, e.g. from the
volume to the boundary, or from the base to the an overintegrated
quadrature discretization.
:arg src: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
:arg tgt: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
:arg vec: a :class:`~meshmode.dof_array.DOFArray` or a
:class:`~arraycontext.ArrayContainer`.
"""
src = as_dofdesc(src)
tgt = as_dofdesc(tgt)
if src == tgt:
return vec
if isinstance(vec, np.ndarray):
return obj_array_vectorize(lambda el: project(dcoll, src, tgt, el),
vec)
if isinstance(vec, Number):
return vec
return dcoll.connection_from_dds(src, tgt)(vec)
def weak_local_d_dx(dcoll: DiscretizationCollection,
*args) -> ArrayOrContainerT:
r"""Return the element-local weak derivative along axis *xyz_axis* of the
volume function represented by *vec*.
May be called with ``(xyz_axis, vec)`` or ``(dd_in, xyz_axis, vec)``.
Specifically, this function computes the volume contribution of the
weak derivative in the :math:`i`-th component (specified by *xyz_axis*)
of a function :math:`f`, in each element :math:`E`, with respect to polynomial
test functions :math:`\phi`:
.. math::
\int_E \partial_i\phi\,f\,\mathrm{d}x \sim
\mathbf{D}_{E,i}^T \mathbf{M}_{E}^T\mathbf{f}|_E,
where :math:`\mathbf{D}_{E,i}` is the polynomial differentiation matrix on
an :math:`E` for the :math:`i`-th spatial coordinate, :math:`\mathbf{M}_E`
is the elemental mass matrix (see :func:`mass` for more information), and
:math:`\mathbf{f}|_E` is a vector of coefficients for :math:`f` on :math:`E`.
:arg dd_in: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
Defaults to the base volume discretization if not provided.
:arg xyz_axis: an integer indicating the axis along which the derivative
is taken.
:arg vec: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` of them.
:returns: a :class:`~meshmode.dof_array.DOFArray` or an
:class:`~arraycontext.container.ArrayContainer` of them.
"""
if len(args) == 2:
xyz_axis, vec = args
dd_in = dof_desc.DOFDesc("vol", dof_desc.DISCR_TAG_BASE)
elif len(args) == 3:
dd_in, xyz_axis, vec = args
else:
raise TypeError("invalid number of arguments")
if not isinstance(vec, DOFArray):
return map_array_container(
partial(weak_local_d_dx, dcoll, dd_in, xyz_axis), vec)
from grudge.geometry import inverse_surface_metric_derivative_mat
in_discr = dcoll.discr_from_dd(dd_in)
out_discr = dcoll.discr_from_dd(dof_desc.DD_VOLUME)
actx = vec.array_context
inverse_jac_mat = inverse_surface_metric_derivative_mat(
actx,
dcoll,
dd=dd_in,
times_area_element=True,
_use_geoderiv_connection=actx.supports_nonscalar_broadcasting)
return _single_axis_derivative_kernel(
actx,
out_discr,
in_discr,
_reference_stiffness_transpose_matrix,
inverse_jac_mat,
xyz_axis,
vec,
metric_in_matvec=True)
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
dim = 2
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
order = 3
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.75 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.45 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
print("%d elements" % mesh.nelements)
from meshmode.discretization.poly_element import \
QuadratureSimplexGroupFactory, \
PolynomialWarpAndBlendGroupFactory
dcoll = DiscretizationCollection(
actx,
mesh,
discr_tag_to_group_factory={
DISCR_TAG_BASE: PolynomialWarpAndBlendGroupFactory(order),
DISCR_TAG_QUAD: QuadratureSimplexGroupFactory(3 * order),
})
# bounded above by 1
c = 0.2 + 0.8 * bump(actx, dcoll, center=np.zeros(3), width=0.5)
fields = flat_obj_array(bump(
actx,
dcoll,
), [dcoll.zeros(actx) for i in range(dcoll.dim)])
vis = make_visualizer(dcoll)
def rhs(t, w):
return wave_operator(dcoll, c=c, w=w)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
print(istep, t, op.norm(dcoll, fields[0], p=2))
vis.write_vtk_file("fld-wave-eager-var-velocity-%04d.vtu" % istep,
[
("c", c),
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def dt_geometric_factors(dcoll: DiscretizationCollection, dd=None) -> DOFArray:
r"""Computes a geometric scaling factor for each cell following [Hesthaven_2008]_,
section 6.4, defined as the inradius (radius of an inscribed circle/sphere).
Specifically, the inradius for each element is computed using the following
formula from [Shewchuk_2002]_, Table 1, for simplicial cells
(triangles/tetrahedra):
.. math::
r_D = \frac{d V}{\sum_{i=1}^{N_{faces}} F_i},
where :math:`d` is the topological dimension, :math:`V` is the cell volume,
and :math:`F_i` are the areas of each face of the cell.
:arg dd: a :class:`~grudge.dof_desc.DOFDesc`, or a value convertible to one.
Defaults to the base volume discretization if not provided.
:returns: a frozen :class:`~meshmode.dof_array.DOFArray` containing the
geometric factors for each cell and at each nodal location.
"""
from meshmode.discretization.poly_element import SimplexElementGroupBase
if dd is None:
dd = DD_VOLUME
actx = dcoll._setup_actx
volm_discr = dcoll.discr_from_dd(dd)
if any(not isinstance(grp, SimplexElementGroupBase)
for grp in volm_discr.groups):
raise NotImplementedError(
"Geometric factors are only implemented for simplex element groups"
)
if volm_discr.dim != volm_discr.ambient_dim:
from warnings import warn
warn(
"The geometric factor for the characteristic length scale in "
"time step estimation is not necessarily valid for non-volume-"
"filling discretizations. Continuing anyway.",
stacklevel=3)
cell_vols = abs(
op.elementwise_integral(dcoll, dd,
volm_discr.zeros(actx) + 1.0))
if dcoll.dim == 1:
return freeze(cell_vols)
dd_face = DOFDesc("all_faces", dd.discretization_tag)
face_discr = dcoll.discr_from_dd(dd_face)
# To get a single value for the total surface area of a cell, we
# take the sum over the averaged face areas on each face.
# NOTE: The face areas are the *same* at each face nodal location.
# This assumes there are the *same* number of face nodes on each face.
surface_areas = abs(
op.elementwise_integral(dcoll, dd_face,
face_discr.zeros(actx) + 1.0))
surface_areas = DOFArray(
actx,
data=tuple(
actx.einsum("fej->e",
face_ae_i.reshape(vgrp.mesh_el_group.nfaces,
vgrp.nelements, afgrp.nunit_dofs),
tagged=(FirstAxisIsElementsTag(), )) / afgrp.nunit_dofs
for vgrp, afgrp, face_ae_i in zip(
volm_discr.groups, face_discr.groups, surface_areas)))
return freeze(
DOFArray(actx,
data=tuple(
actx.einsum("e,ei->ei",
1 / sae_i,
cv_i,
tagged=(FirstAxisIsElementsTag(), )) *
dcoll.dim
for cv_i, sae_i in zip(cell_vols, surface_areas))))
def main(ctx_factory, dim=2, order=3, visualize=False, lazy=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
if lazy:
actx = PytatoPyOpenCLArrayContext(queue)
else:
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
nel_1d = 16
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
logger.info("%d elements", mesh.nelements)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element,
num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
dcoll = DiscretizationCollection(actx,
local_mesh,
order=order,
mpi_communicator=comm)
fields = WaveState(u=bump(actx, dcoll),
v=make_obj_array(
[dcoll.zeros(actx) for i in range(dcoll.dim)]))
c = 1
dt = actx.to_numpy(0.45 * estimate_rk4_timestep(actx, dcoll, c))
vis = make_visualizer(dcoll)
def rhs(t, w):
return wave_operator(dcoll, c=c, w=w)
compiled_rhs = actx.compile(rhs)
if comm.rank == 0:
logger.info("dt = %g", dt)
import time
start = time.time()
t = 0
t_final = 3
istep = 0
while t < t_final:
if lazy:
fields = thaw(freeze(fields, actx), actx)
fields = rk4_step(fields, t, dt, compiled_rhs)
l2norm = actx.to_numpy(op.norm(dcoll, fields.u, 2))
if istep % 10 == 0:
stop = time.time()
linfnorm = actx.to_numpy(op.norm(dcoll, fields.u, np.inf))
nodalmax = actx.to_numpy(op.nodal_max(dcoll, "vol", fields.u))
nodalmin = actx.to_numpy(op.nodal_min(dcoll, "vol", fields.u))
if comm.rank == 0:
logger.info(f"step: {istep} t: {t} "
f"L2: {l2norm} "
f"Linf: {linfnorm} "
f"sol max: {nodalmax} "
f"sol min: {nodalmin} "
f"wall: {stop-start} ")
if visualize:
vis.write_parallel_vtk_file(
comm, f"fld-wave-eager-mpi-{{rank:03d}}-{istep:04d}.vtu", [
("u", fields.u),
("v", fields.v),
])
start = stop
t += dt
istep += 1
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert l2norm < 1
from meshmode.mesh.generation import generate_box_mesh
from meshmode.array_context import PyOpenCLArrayContext
from arraycontext import thaw
from grudge.dof_desc import DTAG_BOUNDARY, FACE_RESTR_INTERIOR
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
actx = PyOpenCLArrayContext(queue)
nel = 10
coords = np.linspace(0, 2*np.pi, nel)
mesh = generate_box_mesh((coords,),
boundary_tag_to_face={"left": ["-x"],
"right": ["+x"]})
dcoll = DiscretizationCollection(actx, mesh, order=1)
def initial_condition(x):
# 'x' contains ndim arrays.
# 'x[0]' gets the first coordinate value of all the nodes
return actx.np.sin(x[0])
def left_boundary_condition(x, t):
return actx.np.sin(x[0] - 2 * np.pi * t)
def flux(dcoll, u_tpair):
dd = u_tpair.dd
velocity = np.array([2 * np.pi])
def main():
cl_ctx = cl.create_some_context()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
comm = MPI.COMM_WORLD
num_parts = comm.Get_size()
from meshmode.distributed import MPIMeshDistributor, get_partition_by_pymetis
mesh_dist = MPIMeshDistributor(comm)
dim = 2
nel_1d = 16
if mesh_dist.is_mananger_rank():
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(a=(-0.5, ) * dim,
b=(0.5, ) * dim,
nelements_per_axis=(nel_1d, ) * dim)
print("%d elements" % mesh.nelements)
part_per_element = get_partition_by_pymetis(mesh, num_parts)
local_mesh = mesh_dist.send_mesh_parts(mesh, part_per_element,
num_parts)
del mesh
else:
local_mesh = mesh_dist.receive_mesh_part()
order = 3
dcoll = DiscretizationCollection(actx,
local_mesh,
order=order,
mpi_communicator=comm)
if dim == 2:
# no deep meaning here, just a fudge factor
dt = 0.75 / (nel_1d * order**2)
elif dim == 3:
# no deep meaning here, just a fudge factor
dt = 0.45 / (nel_1d * order**2)
else:
raise ValueError("don't have a stable time step guesstimate")
fields = flat_obj_array(bump(actx, dcoll),
[dcoll.zeros(actx) for i in range(dcoll.dim)])
vis = make_visualizer(dcoll)
def rhs(t, w):
return wave_operator(dcoll, c=1, w=w)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
print(istep, t, op.norm(dcoll, fields[0], p=2))
vis.write_parallel_vtk_file(
comm, f"fld-wave-eager-mpi-{{rank:03d}}-{istep:04d}.vtu", [
("u", fields[0]),
("v", fields[1:]),
])
t += dt
istep += 1
def main(ctx_factory, dim=2, order=3, visualize=False):
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(
queue,
allocator=cl_tools.MemoryPool(cl_tools.ImmediateAllocator(queue)),
force_device_scalars=True,
)
nel_1d = 16
from meshmode.mesh.generation import generate_regular_rect_mesh
mesh = generate_regular_rect_mesh(
a=(-0.5,)*dim,
b=(0.5,)*dim,
nelements_per_axis=(nel_1d,)*dim)
logger.info("%d elements", mesh.nelements)
from meshmode.discretization.poly_element import \
QuadratureSimplexGroupFactory, \
default_simplex_group_factory
dcoll = DiscretizationCollection(
actx, mesh,
discr_tag_to_group_factory={
DISCR_TAG_BASE: default_simplex_group_factory(base_dim=dim, order=order),
DISCR_TAG_QUAD: QuadratureSimplexGroupFactory(3*order),
}
)
# bounded above by 1
c = 0.2 + 0.8*bump(actx, dcoll, center=np.zeros(3), width=0.5)
dt = 0.5 * estimate_rk4_timestep(actx, dcoll, c=1)
fields = flat_obj_array(
bump(actx, dcoll, ),
[dcoll.zeros(actx) for i in range(dcoll.dim)]
)
vis = make_visualizer(dcoll)
def rhs(t, w):
return wave_operator(dcoll, c=c, w=w)
logger.info("dt = %g", dt)
t = 0
t_final = 3
istep = 0
while t < t_final:
fields = rk4_step(fields, t, dt, rhs)
if istep % 10 == 0:
logger.info(f"step: {istep} t: {t} "
f"L2: {op.norm(dcoll, fields[0], 2)} "
f"Linf: {op.norm(dcoll, fields[0], np.inf)} "
f"sol max: {op.nodal_max(dcoll, 'vol', fields[0])} "
f"sol min: {op.nodal_min(dcoll, 'vol', fields[0])}")
if visualize:
vis.write_vtk_file(
f"fld-wave-eager-var-velocity-{istep:04d}.vtu",
[
("c", c),
("u", fields[0]),
("v", fields[1:]),
]
)
t += dt
istep += 1
# NOTE: These are here to ensure the solution is bounded for the
# time interval specified
assert op.norm(dcoll, fields[0], 2) < 1
