def __init__(self, tree_indep, queue, geo_data, use_target_specific_qbx):
from pytential.qbx.utils import ToHostTransferredGeoDataWrapper
geo_data = ToHostTransferredGeoDataWrapper(geo_data)
self.geo_data = geo_data
self.trav = geo_data.traversal()
self.using_tsqbx = (
use_target_specific_qbx
# None means use by default if possible
or use_target_specific_qbx is None)
super().__init__(tree_indep, geo_data.traversal())
def __init__(self, queue, geo_data, use_target_specific_qbx):
from pytential.qbx.utils import ToHostTransferredGeoDataWrapper
geo_data = ToHostTransferredGeoDataWrapper(queue, geo_data)
self.geo_data = geo_data
self.trav = geo_data.traversal()
self.using_tsqbx = (
use_target_specific_qbx
# None means use by default if possible
or use_target_specific_qbx is None)
ConstantOneExpansionWrangler.__init__(self, geo_data.tree())
def __init__(self, tree_indep, geo_data, dtype,
qbx_order, fmm_level_to_order,
source_extra_kwargs,
kernel_extra_kwargs,
_use_target_specific_qbx=None):
# FMMLib is CPU-only. This wrapper gets the geometry out of
# OpenCL-land.
from pytential.qbx.utils import ToHostTransferredGeoDataWrapper
geo_data = ToHostTransferredGeoDataWrapper(geo_data)
self.geo_data = geo_data
self.qbx_order = qbx_order
if tree_indep.k_name is None:
helmholtz_k = 0
else:
helmholtz_k = kernel_extra_kwargs[tree_indep.k_name]
dipole_vec = None
if tree_indep.source_deriv_name is not None:
with cl.CommandQueue(tree_indep.cl_context) as queue:
dipole_vec = np.array([
d_i.get(queue=queue)
for d_i in source_extra_kwargs[
tree_indep.source_deriv_name]],
order="F")
def inner_fmm_level_to_nterms(tree, level):
if helmholtz_k == 0:
return fmm_level_to_order(
LaplaceKernel(tree.dimensions),
frozenset(), tree, level)
else:
return fmm_level_to_order(
HelmholtzKernel(tree.dimensions),
frozenset([("k", helmholtz_k)]), tree, level)
super().__init__(
tree_indep,
geo_data.traversal(),
helmholtz_k=helmholtz_k,
dipole_vec=dipole_vec,
dipoles_already_reordered=True,
fmm_level_to_nterms=inner_fmm_level_to_nterms,
rotation_data=geo_data)
def test_compare_cl_and_py_cost_model(ctx_factory):
nelements = 3600
target_order = 16
fmm_order = 5
qbx_order = fmm_order
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
actx = PyOpenCLArrayContext(queue)
# {{{ Construct geometry
from meshmode.mesh.generation import make_curve_mesh, starfish
mesh = make_curve_mesh(starfish, np.linspace(0, 1, nelements), target_order)
from meshmode.discretization import Discretization
from meshmode.discretization.poly_element import \
InterpolatoryQuadratureSimplexGroupFactory
pre_density_discr = Discretization(
actx, mesh,
InterpolatoryQuadratureSimplexGroupFactory(target_order)
)
qbx = QBXLayerPotentialSource(
pre_density_discr, 4 * target_order,
qbx_order,
fmm_order=fmm_order
)
places = GeometryCollection(qbx)
from pytential.qbx.refinement import refine_geometry_collection
places = refine_geometry_collection(places)
target_discrs_and_qbx_sides = tuple([(qbx.density_discr, 0)])
geo_data_dev = qbx.qbx_fmm_geometry_data(
places, places.auto_source.geometry, target_discrs_and_qbx_sides
)
from pytential.qbx.utils import ToHostTransferredGeoDataWrapper
geo_data = ToHostTransferredGeoDataWrapper(queue, geo_data_dev)
# }}}
# {{{ Construct cost models
cl_cost_model = QBXCostModel()
python_cost_model = _PythonQBXCostModel()
tree = geo_data.tree()
xlat_cost = make_pde_aware_translation_cost_model(
tree.targets.shape[0], tree.nlevels
)
constant_one_params = QBXCostModel.get_unit_calibration_params()
constant_one_params["p_qbx"] = 5
for ilevel in range(tree.nlevels):
constant_one_params["p_fmm_lev%d" % ilevel] = 10
cl_cost_factors = cl_cost_model.qbx_cost_factors_for_kernels_from_model(
queue, tree.nlevels, xlat_cost, constant_one_params
)
python_cost_factors = python_cost_model.qbx_cost_factors_for_kernels_from_model(
None, tree.nlevels, xlat_cost, constant_one_params
)
# }}}
# {{{ Test process_form_qbxl
cl_ndirect_sources_per_target_box = (
cl_cost_model.get_ndirect_sources_per_target_box(
queue, geo_data_dev.traversal()
)
)
queue.finish()
start_time = time.time()
cl_p2qbxl = cl_cost_model.process_form_qbxl(
queue, geo_data_dev, cl_cost_factors["p2qbxl_cost"],
cl_ndirect_sources_per_target_box
)
queue.finish()
logger.info("OpenCL time for process_form_qbxl: {}".format(
str(time.time() - start_time)
))
python_ndirect_sources_per_target_box = (
python_cost_model.get_ndirect_sources_per_target_box(
queue, geo_data.traversal()
)
)
start_time = time.time()
python_p2qbxl = python_cost_model.process_form_qbxl(
queue, geo_data, python_cost_factors["p2qbxl_cost"],
python_ndirect_sources_per_target_box
)
logger.info("Python time for process_form_qbxl: {}".format(
str(time.time() - start_time)
))
assert np.array_equal(cl_p2qbxl.get(), python_p2qbxl)
# }}}
# {{{ Test process_m2qbxl
queue.finish()
start_time = time.time()
cl_m2qbxl = cl_cost_model.process_m2qbxl(
queue, geo_data_dev, cl_cost_factors["m2qbxl_cost"]
)
queue.finish()
logger.info("OpenCL time for process_m2qbxl: {}".format(
str(time.time() - start_time)
))
start_time = time.time()
python_m2qbxl = python_cost_model.process_m2qbxl(
queue, geo_data, python_cost_factors["m2qbxl_cost"]
)
logger.info("Python time for process_m2qbxl: {}".format(
str(time.time() - start_time)
))
assert np.array_equal(cl_m2qbxl.get(), python_m2qbxl)
# }}}
# {{{ Test process_l2qbxl
queue.finish()
start_time = time.time()
cl_l2qbxl = cl_cost_model.process_l2qbxl(
queue, geo_data_dev, cl_cost_factors["l2qbxl_cost"]
)
queue.finish()
logger.info("OpenCL time for process_l2qbxl: {}".format(
str(time.time() - start_time)
))
start_time = time.time()
python_l2qbxl = python_cost_model.process_l2qbxl(
queue, geo_data, python_cost_factors["l2qbxl_cost"]
)
logger.info("Python time for process_l2qbxl: {}".format(
str(time.time() - start_time)
))
assert np.array_equal(cl_l2qbxl.get(), python_l2qbxl)
# }}}
# {{{ Test process_eval_qbxl
queue.finish()
start_time = time.time()
cl_eval_qbxl = cl_cost_model.process_eval_qbxl(
queue, geo_data_dev, cl_cost_factors["qbxl2p_cost"]
)
queue.finish()
logger.info("OpenCL time for process_eval_qbxl: {}".format(
str(time.time() - start_time)
))
start_time = time.time()
python_eval_qbxl = python_cost_model.process_eval_qbxl(
queue, geo_data, python_cost_factors["qbxl2p_cost"]
)
logger.info("Python time for process_eval_qbxl: {}".format(
str(time.time() - start_time)
))
assert np.array_equal(cl_eval_qbxl.get(), python_eval_qbxl)
# }}}
# {{{ Test eval_target_specific_qbxl
queue.finish()
start_time = time.time()
cl_eval_target_specific_qbxl = cl_cost_model.process_eval_target_specific_qbxl(
queue, geo_data_dev, cl_cost_factors["p2p_tsqbx_cost"],
cl_ndirect_sources_per_target_box
)
queue.finish()
logger.info("OpenCL time for eval_target_specific_qbxl: {}".format(
str(time.time() - start_time)
))
start_time = time.time()
python_eval_target_specific_qbxl = \
python_cost_model.process_eval_target_specific_qbxl(
queue, geo_data, python_cost_factors["p2p_tsqbx_cost"],
python_ndirect_sources_per_target_box
)
logger.info("Python time for eval_target_specific_qbxl: {}".format(
str(time.time() - start_time)
))
assert np.array_equal(
cl_eval_target_specific_qbxl.get(), python_eval_target_specific_qbxl
)