def get_operator(self, ambient_dim, qbx_forced_limit="avg"):
knl = self.knl_class(ambient_dim)
kwargs = self.knl_sym_kwargs.copy()
kwargs["qbx_forced_limit"] = qbx_forced_limit
if self.op_type == "scalar":
sym_u = sym.var("u")
sym_op = sym.S(knl, sym_u, **kwargs)
elif self.op_type == "scalar_mixed":
sym_u = sym.var("u")
sym_op = sym.S(knl, 0.3 * sym_u, **kwargs) \
+ sym.D(knl, 0.5 * sym_u, **kwargs)
elif self.op_type == "vector":
sym_u = sym.make_sym_vector("u", ambient_dim)
sym_op = make_obj_array([
sym.Sp(knl, sym_u[0], **kwargs) +
sym.D(knl, sym_u[1], **kwargs),
sym.S(knl, 0.4 * sym_u[0], **kwargs) +
0.3 * sym.D(knl, sym_u[0], **kwargs)
])
else:
raise ValueError(f"unknown operator type: '{self.op_type}'")
sym_op = 0.5 * sym_u + sym_op
return sym_u, sym_op
def S_G(self, i, density, qbx_forced_limit, op_map=None): # noqa: N802
if op_map is None:
op_map = lambda x: x # noqa: E731
from sumpy.kernel import HelmholtzKernel
hhk = HelmholtzKernel(2, allow_evanescent=True)
hhk_scaling = 1j / 4
if i == 0:
lam1, lam2 = self.lambdas
return (
# FIXME: Verify scaling
-1 / (2 * np.pi * (lam1**2 - lam2**2)) / hhk_scaling * (op_map(
sym.S(hhk,
density,
k=1j * lam1,
qbx_forced_limit=qbx_forced_limit)) - op_map(
sym.S(hhk,
density,
k=1j * lam2,
qbx_forced_limit=qbx_forced_limit))))
else:
return (
# FIXME: Verify scaling
-1 / (2 * np.pi) / hhk_scaling * op_map(
sym.S(hhk,
density,
k=1j * self.lambdas[i - 1],
qbx_forced_limit=qbx_forced_limit)))
def representation(self, u,
map_potentials=None, qbx_forced_limit=None, **kwargs):
"""
:param u: symbolic variable for the density.
:param map_potentials: a callable that can be used to apply
additional transformations on all the layer potentials in the
representation, e.g. to take a target derivative.
"""
sqrt_w = self.get_sqrt_weight()
inv_sqrt_w_u = sym.cse(u/sqrt_w)
laplace_s_inv_sqrt_w_u = sym.cse(sym.S(self.laplace_kernel, inv_sqrt_w_u))
if map_potentials is None:
def map_potentials(x): # pylint:disable=function-redefined
return x
kwargs["qbx_forced_limit"] = qbx_forced_limit
kwargs["kernel_arguments"] = self.kernel_arguments
return (
map_potentials(
sym.S(self.kernel, inv_sqrt_w_u, **kwargs)
)
- self.alpha * map_potentials(
sym.D(self.kernel, laplace_s_inv_sqrt_w_u, **kwargs)
)
)
def test_cost_model(ctx_factory, dim, use_target_specific_qbx):
"""Test that cost model gathering can execute successfully."""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
lpot_source = get_lpot_source(actx, dim).copy(
_use_target_specific_qbx=use_target_specific_qbx,
cost_model=CostModel())
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
sigma = get_density(actx, density_discr)
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(lpot_source.ambient_dim)
sym_op_S = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
op_S = bind(places, sym_op_S)
cost_S = op_S.get_modeled_cost(actx, sigma=sigma)
assert len(cost_S) == 1
sym_op_S_plus_D = (
sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
+ sym.D(k_sym, sigma_sym, qbx_forced_limit="avg"))
op_S_plus_D = bind(places, sym_op_S_plus_D)
cost_S_plus_D = op_S_plus_D.get_modeled_cost(actx, sigma=sigma)
assert len(cost_S_plus_D) == 2
def apply_pressure(self, density_vec_sym, dir_vec_sym, mu_sym, qbx_forced_limit):
""" Symbolic expression for pressure field associated with the stresslet"""
import itertools
from pytential.symbolic.mappers import DerivativeTaker
kernel = LaplaceKernel(dim=self.dim)
factor = (2. * mu_sym)
for i, j in itertools.product(range(self.dim), range(self.dim)):
if i + j < 1:
sym_expr = factor * DerivativeTaker(i).map_int_g(
DerivativeTaker(j).map_int_g(
sym.S(kernel, density_vec_sym[i] * dir_vec_sym[j],
qbx_forced_limit=qbx_forced_limit)))
else:
sym_expr = sym_expr + (
factor * DerivativeTaker(i).map_int_g(
DerivativeTaker(j).map_int_g(
sym.S(kernel,
density_vec_sym[i] * dir_vec_sym[j],
qbx_forced_limit=qbx_forced_limit))))
return sym_expr
def representation(self,
sigma,
map_potentials=None,
qbx_forced_limit=None):
if map_potentials is None:
def map_potentials(x): # pylint:disable=function-redefined
return x
def dv(knl):
return DirectionalSourceDerivative(knl, "normal_dir")
def dt(knl):
return DirectionalSourceDerivative(knl, "tangent_dir")
normal_dir = sym.normal(2).as_vector()
tangent_dir = np.array([-normal_dir[1], normal_dir[0]])
k1 = map_potentials(sym.S(dv(dv(dv(self.knl))), sigma[0],
kernel_arguments={"normal_dir": normal_dir},
qbx_forced_limit=qbx_forced_limit)) + \
3*map_potentials(sym.S(dv(dt(dt(self.knl))), sigma[0],
kernel_arguments={"normal_dir": normal_dir,
"tangent_dir": tangent_dir},
qbx_forced_limit=qbx_forced_limit))
k2 = -map_potentials(sym.S(dv(dv(self.knl)), sigma[1],
kernel_arguments={"normal_dir": normal_dir},
qbx_forced_limit=qbx_forced_limit)) + \
map_potentials(sym.S(dt(dt(self.knl)), sigma[1],
kernel_arguments={"tangent_dir": tangent_dir},
qbx_forced_limit=qbx_forced_limit))
return k1 + k2
def representation(self, sigma,
map_potentials=None, qbx_forced_limit=None, **kwargs):
"""
:param sigma: symbolic variable for the density.
:param map_potentials: a callable that can be used to apply
additional transformations on all the layer potentials in the
representation, e.g. to take a target derivative.
:param kwargs: additional keyword arguments passed on to the layer
potential constructor.
"""
if map_potentials is None:
def map_potentials(x): # pylint:disable=function-redefined
return x
def dv(knl):
return DirectionalSourceDerivative(knl, "normal_dir")
def dt(knl):
return DirectionalSourceDerivative(knl, "tangent_dir")
normal_dir = sym.normal(self.dim).as_vector()
tangent_dir = np.array([-normal_dir[1], normal_dir[0]])
kwargs["qbx_forced_limit"] = qbx_forced_limit
k1 = (
map_potentials(
sym.S(dv(dv(dv(self.kernel))), sigma[0],
kernel_arguments={"normal_dir": normal_dir},
**kwargs)
)
+ 3 * map_potentials(
sym.S(dv(dt(dt(self.kernel))), sigma[0],
kernel_arguments={
"normal_dir": normal_dir, "tangent_dir": tangent_dir
},
**kwargs)
)
)
k2 = (
-map_potentials(
sym.S(dv(dv(self.kernel)), sigma[1],
kernel_arguments={"normal_dir": normal_dir},
**kwargs)
)
+ map_potentials(
sym.S(dt(dt(self.kernel)), sigma[1],
kernel_arguments={"tangent_dir": tangent_dir},
**kwargs)
)
)
return k1 + k2
def operator(self, u):
from sumpy.kernel import HelmholtzKernel, LaplaceKernel
sqrt_w = self.get_sqrt_weight()
inv_sqrt_w_u = cse(u / sqrt_w)
knl = self.kernel
lknl = self.laplace_kernel
knl_kwargs = {}
knl_kwargs["kernel_arguments"] = self.kernel_arguments
DpS0u = sym.Dp(
knl, # noqa
cse(sym.S(lknl, inv_sqrt_w_u)),
**knl_kwargs)
if self.use_improved_operator:
Dp0S0u = -0.25 * u + sym.Sp( # noqa
lknl, # noqa
sym.Sp(lknl, inv_sqrt_w_u, qbx_forced_limit="avg"),
qbx_forced_limit="avg")
if isinstance(self.kernel, HelmholtzKernel):
DpS0u = ( # noqa
sym.Dp(
knl - lknl, # noqa
cse(sym.S(lknl, inv_sqrt_w_u, qbx_forced_limit=+1)),
qbx_forced_limit=+1,
**knl_kwargs) + Dp0S0u)
elif isinstance(knl, LaplaceKernel):
DpS0u = Dp0S0u # noqa
else:
raise ValueError(
f"no improved operator for '{self.kernel}' known")
if self.is_unique_only_up_to_constant():
# The interior Neumann operator in this representation
# has a nullspace. The mean of the density must be matched
# to the desired solution separately. As is, this operator
# returns a mean that is not well-specified.
amb_dim = self.kernel.dim
ones_contribution = (sym.Ones() *
sym.mean(amb_dim, amb_dim - 1, inv_sqrt_w_u))
else:
ones_contribution = 0
return (
-self.loc_sign * 0.5 * u + sqrt_w *
(sym.Sp(knl, inv_sqrt_w_u, qbx_forced_limit="avg", **knl_kwargs) -
self.alpha * DpS0u + ones_contribution))
def operator(self, u):
sqrt_w = self.get_sqrt_weight()
inv_sqrt_w_u = cse(u / sqrt_w)
if self.is_unique_only_up_to_constant():
# The exterior Dirichlet operator in this representation
# has a nullspace. The mean of the density must be matched
# to the desired solution separately. As is, this operator
# returns a mean that is not well-specified.
#
# See Hackbusch, https://books.google.com/books?id=Ssnf7SZB0ZMC
# Theorem 8.2.18b
amb_dim = self.kernel.dim
ones_contribution = (sym.Ones() *
sym.mean(amb_dim, amb_dim - 1, inv_sqrt_w_u))
else:
ones_contribution = 0
return (
-self.loc_sign * 0.5 * u + sqrt_w *
(self.alpha * sym.S(self.kernel,
inv_sqrt_w_u,
qbx_forced_limit=+1,
kernel_arguments=self.kernel_arguments) -
sym.D(self.kernel,
inv_sqrt_w_u,
qbx_forced_limit="avg",
kernel_arguments=self.kernel_arguments) + ones_contribution)
)
def scattered_volume_field(self, Jt, rho, qbx_forced_limit=None):
"""
This will return an object array of six entries, the first three of which
represent the electric, and the second three of which represent the
magnetic field. This satisfies the time-domain Maxwell's equations
as verified by :func:`sumpy.point_calculus.frequency_domain_maxwell`.
"""
Jxyz = sym.cse(sym.tangential_to_xyz(Jt), "Jxyz")
A = sym.S(self.kernel, Jxyz, k=self.k, qbx_forced_limit=qbx_forced_limit)
phi = sym.S(self.kernel, rho, k=self.k, qbx_forced_limit=qbx_forced_limit)
E_scat = 1j*self.k*A - sym.grad(3, phi)
H_scat = sym.curl(A)
return sym.flat_obj_array(E_scat, H_scat)
def test_timing_data_gathering(ctx_factory):
"""Test that timing data gathering can execute succesfully."""
pytest.importorskip("pyfmmlib")
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
actx = PyOpenCLArrayContext(queue)
lpot_source = get_lpot_source(actx, 2)
places = GeometryCollection(lpot_source)
dofdesc = places.auto_source.to_stage1()
density_discr = places.get_discretization(dofdesc.geometry)
sigma = get_density(actx, density_discr)
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(lpot_source.ambient_dim)
sym_op_S = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
op_S = bind(places, sym_op_S)
timing_data = {}
op_S.eval(dict(sigma=sigma), timing_data=timing_data, array_context=actx)
assert timing_data
print(timing_data)
def representation(self, unknown, i_domain):
"""
:return: a symbolic expression for the representation of the PDE solution
in domain number *i_domain*.
"""
unk = self._structured_unknown(unknown, with_l2_weights=False)
result = []
for field_kind in self.field_kinds:
if not self.is_field_present(field_kind):
continue
field_result = 0
for i_interface, (i_domain_outer, i_domain_inner,
interface_id) in (enumerate(self.interfaces)):
if i_domain_outer == i_domain:
side = self.side_out
elif i_domain_inner == i_domain:
side = self.side_in
else:
continue
my_unk = unk[side, field_kind, i_interface]
if my_unk:
field_result += sym.S(self.kernel,
my_unk,
source=interface_id,
k=self.domain_K_exprs[i_domain])
result.append(field_result)
from pytools.obj_array import make_obj_array
return make_obj_array(result)
def get_bound_op(places):
from sumpy.kernel import LaplaceKernel
op = sym.S(LaplaceKernel(places.ambient_dim),
sym.var("sigma"),
qbx_forced_limit=+1)
return bind(places, op)
def nxcurlS(qbx_forced_limit):
return sym.n_cross(
sym.curl(
sym.S(knl,
sym.cse(sym.tangential_to_xyz(density_sym), "jxyz"),
qbx_forced_limit=qbx_forced_limit)))
def get_lpot_cost(queue, geometry_getter, kind):
lpot_source = geometry_getter(queue)
from pytential import sym, bind
sigma_sym = sym.var("sigma")
from sumpy.kernel import LaplaceKernel
k_sym = LaplaceKernel(lpot_source.ambient_dim)
op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
bound_op = bind(lpot_source, op)
density_discr = lpot_source.density_discr
nodes = density_discr.nodes().with_queue(queue)
sigma = cl.clmath.sin(10 * nodes[0])
from pytools import one
if kind == "actual":
timing_data = {}
result = bound_op.eval(queue, {"sigma": sigma},
timing_data=timing_data)
assert not np.isnan(result.get(queue)).any()
result = one(timing_data.values())
elif kind == "model":
perf_results = bound_op.get_modeled_performance(queue, sigma=sigma)
result = one(perf_results.values())
return result
def get_bound_op(lpot_source):
from sumpy.kernel import LaplaceKernel
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(lpot_source.ambient_dim)
op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
return bind(lpot_source, op)
def get_slp_wall_times(lpot_source, fmm_order, qbx_order):
queue = cl.CommandQueue(lpot_source.cl_context)
lpot_source = lpot_source.copy(
qbx_order=qbx_order,
fmm_level_to_order=(False if fmm_order is False else
lambda *args: fmm_order))
d = lpot_source.ambient_dim
u_sym = sym.var("u")
from sumpy.kernel import LaplaceKernel
S = sym.S(LaplaceKernel(d), u_sym, qbx_forced_limit=-1)
density_discr = lpot_source.density_discr
u = cl.array.empty(queue, density_discr.nnodes, np.float64)
u.fill(1)
op = bind(lpot_source, S)
# Warmup
op(queue, u=u)
times = []
from time import perf_counter as curr_time
for _ in range(WALL_TIME_EXPERIMENT_TIMING_ROUNDS):
t_start = curr_time()
op(queue, u=u)
t_end = curr_time()
times.append(t_end - t_start)
return times
def get_zero_op(self, kernel, **knl_kwargs):
u_sym = sym.var("u")
dn_u_sym = sym.var("dn_u")
return (sym.S(kernel, dn_u_sym, qbx_forced_limit=-1, **knl_kwargs) -
sym.D(kernel, u_sym, qbx_forced_limit="avg", **knl_kwargs) -
0.5 * u_sym)
def _build_op(lpot_id,
k=0,
ndim=2,
source=sym.DEFAULT_SOURCE,
target=sym.DEFAULT_TARGET,
qbx_forced_limit="avg"):
from sumpy.kernel import LaplaceKernel, HelmholtzKernel
if k:
knl = HelmholtzKernel(ndim)
knl_kwargs = {"k": k}
else:
knl = LaplaceKernel(ndim)
knl_kwargs = {}
lpot_kwargs = {
"qbx_forced_limit": qbx_forced_limit,
"source": source,
"target": target}
lpot_kwargs.update(knl_kwargs)
if lpot_id == 1:
# scalar single-layer potential
u_sym = sym.var("u")
op = sym.S(knl, u_sym, **lpot_kwargs)
elif lpot_id == 2:
# scalar combination of layer potentials
u_sym = sym.var("u")
op = sym.S(knl, 0.3 * u_sym, **lpot_kwargs) \
+ sym.D(knl, 0.5 * u_sym, **lpot_kwargs)
elif lpot_id == 3:
# vector potential
u_sym = sym.make_sym_vector("u", 2)
u0_sym, u1_sym = u_sym
op = make_obj_array([
sym.Sp(knl, u0_sym, **lpot_kwargs)
+ sym.D(knl, u1_sym, **lpot_kwargs),
sym.S(knl, 0.4 * u0_sym, **lpot_kwargs)
+ 0.3 * sym.D(knl, u0_sym, **lpot_kwargs)
])
else:
raise ValueError("Unknown lpot_id: {}".format(lpot_id))
op = 0.5 * u_sym + op
return op, u_sym, knl_kwargs
def rho_rhs(self, Jt, Einc_xyz):
Jxyz = cse(tangential_to_xyz(Jt), "Jxyz")
return (sym.n_dot(Einc_xyz)
+ 1j*self.k*sym.n_dot(sym.S(
self.kernel, Jxyz, k=self.k,
# continuous--qbx_forced_limit doesn't really matter
qbx_forced_limit="avg")))
def test_cost_model_order_varying_by_level(ctx_factory):
"""For FMM order varying by level, this checks to ensure that the costs are
different. The varying-level case should have larger cost.
"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
# {{{ constant level to order
def level_to_order_constant(kernel, kernel_args, tree, level):
return 1
lpot_source = get_lpot_source(actx, 2).copy(
cost_model=QBXCostModel(),
fmm_level_to_order=level_to_order_constant)
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(2)
sym_op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
sigma = get_density(actx, density_discr)
cost_constant, metadata = bind(places, sym_op).cost_per_stage(
"constant_one", sigma=sigma)
cost_constant = one(cost_constant.values())
metadata = one(metadata.values())
# }}}
# {{{ varying level to order
def level_to_order_varying(kernel, kernel_args, tree, level):
return metadata["nlevels"] - level
lpot_source = get_lpot_source(actx, 2).copy(
cost_model=QBXCostModel(),
fmm_level_to_order=level_to_order_varying)
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
sigma = get_density(actx, density_discr)
cost_varying, _ = bind(lpot_source, sym_op).cost_per_stage(
"constant_one", sigma=sigma)
cost_varying = one(cost_varying.values())
# }}}
assert sum(cost_varying.values()) > sum(cost_constant.values())
def apply_pressure(self, density_vec_sym, mu_sym, qbx_forced_limit):
""" Symbolic expression for pressure field associated with the Stokeslet"""
from pytential.symbolic.mappers import DerivativeTaker
kernel = LaplaceKernel(dim=self.dim)
for i in range(self.dim):
if i < 1:
sym_expr = DerivativeTaker(i).map_int_g(
sym.S(kernel, density_vec_sym[i],
qbx_forced_limit=qbx_forced_limit))
else:
sym_expr = sym_expr + (DerivativeTaker(i).map_int_g(
sym.S(kernel, density_vec_sym[i],
qbx_forced_limit=qbx_forced_limit)))
return sym_expr
def representation(self,
u,
map_potentials=None,
qbx_forced_limit=None,
**kwargs):
sqrt_w = self.get_sqrt_weight()
inv_sqrt_w_u = cse(u / sqrt_w)
if map_potentials is None:
def map_potentials(x): # pylint:disable=function-redefined
return x
kwargs["qbx_forced_limit"] = qbx_forced_limit
kwargs["kernel_arguments"] = self.kernel_arguments
return (map_potentials(sym.S(self.kernel, inv_sqrt_w_u, **kwargs)) -
self.alpha * map_potentials(
sym.D(self.kernel, sym.S(self.laplace_kernel,
inv_sqrt_w_u), **kwargs)))
def get_zero_op(self, kernel, **knl_kwargs):
d = kernel.dim
u_sym = sym.var("u")
grad_u_sym = sym.make_sym_mv("grad_u", d)
dn_u_sym = sym.var("dn_u")
return (d1.resolve(
d1.dnabla(d) *
d1(sym.S(kernel, dn_u_sym, qbx_forced_limit="avg", **knl_kwargs))
) - d2.resolve(
d2.dnabla(d) *
d2(sym.D(kernel, u_sym, qbx_forced_limit="avg", **knl_kwargs))) -
0.5 * grad_u_sym)
def test_cost_model(actx_factory, dim, use_target_specific_qbx, per_box):
"""Test that cost model gathering can execute successfully."""
actx = actx_factory()
lpot_source = get_lpot_source(actx, dim).copy(
_use_target_specific_qbx=use_target_specific_qbx,
cost_model=QBXCostModel())
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
sigma = get_density(actx, density_discr)
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(lpot_source.ambient_dim)
sym_op_S = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
op_S = bind(places, sym_op_S)
if per_box:
cost_S, _ = op_S.cost_per_box("constant_one", sigma=sigma)
else:
cost_S, _ = op_S.cost_per_stage("constant_one", sigma=sigma)
assert len(cost_S) == 1
sym_op_S_plus_D = (sym.S(k_sym, sigma_sym, qbx_forced_limit=+1) +
sym.D(k_sym, sigma_sym, qbx_forced_limit="avg"))
op_S_plus_D = bind(places, sym_op_S_plus_D)
if per_box:
cost_S_plus_D, _ = op_S_plus_D.cost_per_box("constant_one",
sigma=sigma)
else:
cost_S_plus_D, _ = op_S_plus_D.cost_per_stage("constant_one",
sigma=sigma)
assert len(cost_S_plus_D) == 2
def test_cost_model_order_varying_by_level(ctx_factory):
"""For FMM order varying by level, this checks to ensure that the costs are
different. The varying-level case should have larger cost.
"""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
# {{{ constant level to order
def level_to_order_constant(kernel, kernel_args, tree, level):
return 1
lpot_source = get_lpot_source(actx, 2).copy(
cost_model=CostModel(
calibration_params=CONSTANT_ONE_PARAMS),
fmm_level_to_order=level_to_order_constant)
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(2)
sym_op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
sigma = get_density(actx, density_discr)
cost_constant = one(
bind(places, sym_op)
.get_modeled_cost(actx, sigma=sigma).values())
# }}}
# {{{ varying level to order
varying_order_params = cost_constant.params.copy()
nlevels = cost_constant.params["nlevels"]
for level in range(nlevels):
varying_order_params["p_fmm_lev%d" % level] = nlevels - level
cost_varying = cost_constant.with_params(varying_order_params)
# }}}
assert (
sum(cost_varying.get_predicted_times().values())
> sum(cost_constant.get_predicted_times().values()))
def test_cost_model(ctx_getter, dim, use_target_specific_qbx):
"""Test that cost model gathering can execute successfully."""
cl_ctx = ctx_getter()
queue = cl.CommandQueue(cl_ctx)
lpot_source = (get_lpot_source(queue, dim).copy(
_use_target_specific_qbx=use_target_specific_qbx,
cost_model=CostModel()))
sigma = get_density(queue, lpot_source)
sigma_sym = sym.var("sigma")
k_sym = LaplaceKernel(lpot_source.ambient_dim)
sym_op_S = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
op_S = bind(lpot_source, sym_op_S)
cost_S = op_S.get_modeled_cost(queue, sigma=sigma)
assert len(cost_S) == 1
sym_op_S_plus_D = (sym.S(k_sym, sigma_sym, qbx_forced_limit=+1) +
sym.D(k_sym, sigma_sym))
op_S_plus_D = bind(lpot_source, sym_op_S_plus_D)
cost_S_plus_D = op_S_plus_D.get_modeled_cost(queue, sigma=sigma)
assert len(cost_S_plus_D) == 2
def get_qbx_center_neighborhood_sizes_direct(lpot_source, radius):
queue = cl.CommandQueue(lpot_source.cl_context)
def inspect_geo_data(insn, bound_expr, geo_data):
nonlocal sizes, nsources, ncenters
tree = geo_data.tree().with_queue(queue)
centers = np.array([axis.get(queue) for axis in geo_data.centers()])
search_radii = radius * geo_data.expansion_radii().get(queue)
sources = np.array([axis.get(queue) for axis in tree.sources])
ncenters = len(search_radii)
nsources = tree.nsources
assert nsources == lpot_source.quad_stage2_density_discr.nnodes
center_to_source_dists = (
la.norm(
(centers[:, np.newaxis, :] - sources[:, :, np.newaxis]).T,
ord=np.inf,
axis=-1))
sizes = np.count_nonzero(
center_to_source_dists <= search_radii[:, np.newaxis],
axis=1)
return False # no need to do the actual FMM
sizes = None
nsources = None
ncenters = None
lpot_source = lpot_source.copy(geometry_data_inspector=inspect_geo_data)
density_discr = lpot_source.density_discr
nodes = density_discr.nodes().with_queue(queue)
sigma = cl.clmath.sin(10 * nodes[0])
# The kernel doesn't really matter here
from sumpy.kernel import LaplaceKernel
sigma_sym = sym.var('sigma')
k_sym = LaplaceKernel(lpot_source.ambient_dim)
sym_op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1)
bound_op = bind(lpot_source, sym_op)
bound_op(queue, sigma=sigma)
return (sizes, nsources, ncenters)
def get_lpot_cost(which, helmholtz_k, geometry_getter, lpot_kwargs, kind):
"""
Parameters:
which: "D" or "S"
kind: "actual" or "model"
"""
context = cl.create_some_context(interactive=False)
queue = cl.CommandQueue(context)
lpot_source = geometry_getter(queue, lpot_kwargs)
from sumpy.kernel import LaplaceKernel, HelmholtzKernel
sigma_sym = sym.var("sigma")
if helmholtz_k == 0:
k_sym = LaplaceKernel(lpot_source.ambient_dim)
kernel_kwargs = {}
else:
k_sym = HelmholtzKernel(lpot_source.ambient_dim, "k")
kernel_kwargs = {"k": helmholtz_k}
if which == "S":
op = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1, **kernel_kwargs)
elif which == "D":
op = sym.D(k_sym, sigma_sym, qbx_forced_limit="avg", **kernel_kwargs)
else:
raise ValueError("unknown lpot symbol: '%s'" % which)
bound_op = bind(lpot_source, op)
density_discr = lpot_source.density_discr
nodes = density_discr.nodes().with_queue(queue)
sigma = cl.clmath.sin(10 * nodes[0])
if kind == "actual":
timing_data = {}
result = bound_op.eval(queue, {"sigma": sigma},
timing_data=timing_data)
assert not np.isnan(result.get(queue)).any()
result = one(timing_data.values())
elif kind == "model":
perf_results = bound_op.get_modeled_performance(queue, sigma=sigma)
result = one(perf_results.values())
return result
def test_cost_model_metadata_gathering(ctx_factory):
"""Test that the cost model correctly gathers metadata."""
cl_ctx = ctx_factory()
queue = cl.CommandQueue(cl_ctx)
actx = PyOpenCLArrayContext(queue)
from sumpy.expansion.level_to_order import SimpleExpansionOrderFinder
fmm_level_to_order = SimpleExpansionOrderFinder(tol=1e-5)
lpot_source = get_lpot_source(actx, 2).copy(
fmm_level_to_order=fmm_level_to_order)
places = GeometryCollection(lpot_source)
density_discr = places.get_discretization(places.auto_source.geometry)
sigma = get_density(actx, density_discr)
sigma_sym = sym.var("sigma")
k_sym = HelmholtzKernel(2, "k")
k = 2
sym_op_S = sym.S(k_sym, sigma_sym, qbx_forced_limit=+1, k=sym.var("k"))
op_S = bind(places, sym_op_S)
_, metadata = op_S.cost_per_stage(
"constant_one", sigma=sigma, k=k, return_metadata=True
)
metadata = one(metadata.values())
geo_data = lpot_source.qbx_fmm_geometry_data(
places,
places.auto_source,
target_discrs_and_qbx_sides=((density_discr, 1),))
tree = geo_data.tree()
assert metadata["p_qbx"] == QBX_ORDER
assert metadata["nlevels"] == tree.nlevels
assert metadata["nsources"] == tree.nsources
assert metadata["ntargets"] == tree.ntargets
assert metadata["ncenters"] == geo_data.ncenters
for level in range(tree.nlevels):
assert (
metadata["p_fmm_lev%d" % level]
== fmm_level_to_order(k_sym, {"k": 2}, tree, level))
