def test_sumpy_fmm_timing_data_collection(ctx_getter):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(
ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
nsources = 500
dtype = np.float64
from boxtree.tools import (
make_normal_particle_array as p_normal)
knl = LaplaceKernel(2)
local_expn_class = VolumeTaylorLocalExpansion
mpole_expn_class = VolumeTaylorMultipoleExpansion
order = 1
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources,
max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx)
weights = rng.uniform(queue, nsources, dtype=np.float64)
out_kernels = [knl]
from functools import partial
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx,
partial(mpole_expn_class, knl),
partial(local_expn_class, knl),
out_kernels)
wrangler = wcc.get_wrangler(queue, tree, dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order)
from boxtree.fmm import drive_fmm
timing_data = {}
pot, = drive_fmm(trav, wrangler, weights, timing_data=timing_data)
print(timing_data)
assert timing_data
def test_sumpy_fmm_timing_data_collection(ctx_factory):
logging.basicConfig(level=logging.INFO)
ctx = ctx_factory()
queue = cl.CommandQueue(
ctx,
properties=cl.command_queue_properties.PROFILING_ENABLE)
nsources = 500
dtype = np.float64
from boxtree.tools import (
make_normal_particle_array as p_normal)
knl = LaplaceKernel(2)
local_expn_class = VolumeTaylorLocalExpansion
mpole_expn_class = VolumeTaylorMultipoleExpansion
order = 1
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources,
max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx)
weights = rng.uniform(queue, nsources, dtype=np.float64)
out_kernels = [knl]
from functools import partial
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx,
partial(mpole_expn_class, knl),
partial(local_expn_class, knl),
out_kernels)
wrangler = wcc.get_wrangler(queue, tree, dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order)
from boxtree.fmm import drive_fmm
timing_data = {}
pot, = drive_fmm(trav, wrangler, (weights,), timing_data=timing_data)
print(timing_data)
assert timing_data
def test_sumpy_fmm_exclude_self(ctx_getter):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 500
dtype = np.float64
from boxtree.tools import (
make_normal_particle_array as p_normal)
knl = LaplaceKernel(2)
local_expn_class = VolumeTaylorLocalExpansion
mpole_expn_class = VolumeTaylorMultipoleExpansion
order = 10
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources,
max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx)
weights = rng.uniform(queue, nsources, dtype=np.float64)
target_to_source = np.arange(tree.ntargets, dtype=np.int32)
self_extra_kwargs = {"target_to_source": target_to_source}
out_kernels = [knl]
from functools import partial
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx,
partial(mpole_expn_class, knl),
partial(local_expn_class, knl),
out_kernels,
exclude_self=True)
wrangler = wcc.get_wrangler(queue, tree, dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order,
self_extra_kwargs=self_extra_kwargs)
from boxtree.fmm import drive_fmm
pot, = drive_fmm(trav, wrangler, weights)
from sumpy import P2P
p2p = P2P(ctx, out_kernels, exclude_self=True)
evt, (ref_pot,) = p2p(queue, sources, sources, (weights,),
**self_extra_kwargs)
pot = pot.get()
ref_pot = ref_pot.get()
rel_err = la.norm(pot - ref_pot) / la.norm(ref_pot)
logger.info("order %d -> relative l2 error: %g" % (order, rel_err))
assert np.isclose(rel_err, 0, atol=1e-7)
def test_sumpy_fmm(ctx_getter, knl, local_expn_class, mpole_expn_class):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 1000
ntargets = 300
dtype = np.float64
from boxtree.tools import (
make_normal_particle_array as p_normal)
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
if 1:
offset = np.zeros(knl.dim)
offset[0] = 0.1
targets = (
p_normal(queue, ntargets, knl.dim, dtype, seed=18)
+ offset)
del offset
else:
from sumpy.visualization import FieldPlotter
fp = FieldPlotter(np.array([0.5, 0]), extent=3, npoints=200)
from pytools.obj_array import make_obj_array
targets = make_obj_array(
[fp.points[i] for i in range(knl.dim)])
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources, targets=targets,
max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
# {{{ plot tree
if 0:
host_tree = tree.get()
host_trav = trav.get()
if 1:
print("src_box", host_tree.find_box_nr_for_source(403))
print("tgt_box", host_tree.find_box_nr_for_target(28))
print(list(host_trav.target_or_target_parent_boxes).index(37))
print(host_trav.get_box_list("sep_bigger", 22))
from boxtree.visualization import TreePlotter
plotter = TreePlotter(host_tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.set_bounding_box()
plotter.draw_box_numbers()
import matplotlib.pyplot as pt
pt.show()
# }}}
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx, seed=44)
weights = rng.uniform(queue, nsources, dtype=np.float64)
logger.info("computing direct (reference) result")
from pytools.convergence import PConvergenceVerifier
pconv_verifier = PConvergenceVerifier()
extra_kwargs = {}
dtype = np.float64
order_values = [1, 2, 3]
if isinstance(knl, HelmholtzKernel):
extra_kwargs["k"] = 0.05
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, H2DLocalExpansion):
order_values = [10, 12]
elif isinstance(knl, YukawaKernel):
extra_kwargs["lam"] = 2
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, Y2DLocalExpansion):
order_values = [10, 12]
from functools import partial
for order in order_values:
out_kernels = [knl]
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx,
partial(mpole_expn_class, knl),
partial(local_expn_class, knl),
out_kernels)
wrangler = wcc.get_wrangler(queue, tree, dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order,
kernel_extra_kwargs=extra_kwargs)
from boxtree.fmm import drive_fmm
pot, = drive_fmm(trav, wrangler, weights)
from sumpy import P2P
p2p = P2P(ctx, out_kernels, exclude_self=False)
evt, (ref_pot,) = p2p(queue, targets, sources, (weights,),
**extra_kwargs)
pot = pot.get()
ref_pot = ref_pot.get()
rel_err = la.norm(pot - ref_pot, np.inf) / la.norm(ref_pot, np.inf)
logger.info("order %d -> relative l2 error: %g" % (order, rel_err))
pconv_verifier.add_data_point(order, rel_err)
print(pconv_verifier)
pconv_verifier()
def test_sumpy_fmm(ctx_getter, knl, local_expn_class, mpole_expn_class):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 1000
ntargets = 300
dtype = np.float64
from boxtree.tools import (make_normal_particle_array as p_normal)
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
if 1:
offset = np.zeros(knl.dim)
offset[0] = 0.1
targets = (p_normal(queue, ntargets, knl.dim, dtype, seed=18) + offset)
del offset
else:
from sumpy.visualization import FieldPlotter
fp = FieldPlotter(np.array([0.5, 0]), extent=3, npoints=200)
from pytools.obj_array import make_obj_array
targets = make_obj_array([fp.points[i] for i in range(knl.dim)])
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue,
sources,
targets=targets,
max_particles_in_box=30,
debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
# {{{ plot tree
if 0:
host_tree = tree.get()
host_trav = trav.get()
if 1:
print("src_box", host_tree.find_box_nr_for_source(403))
print("tgt_box", host_tree.find_box_nr_for_target(28))
print(list(host_trav.target_or_target_parent_boxes).index(37))
print(host_trav.get_box_list("sep_bigger", 22))
from boxtree.visualization import TreePlotter
plotter = TreePlotter(host_tree)
plotter.draw_tree(fill=False, edgecolor="black", zorder=10)
plotter.set_bounding_box()
plotter.draw_box_numbers()
import matplotlib.pyplot as pt
pt.show()
# }}}
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx, seed=44)
weights = rng.uniform(queue, nsources, dtype=np.float64)
logger.info("computing direct (reference) result")
from pytools.convergence import PConvergenceVerifier
pconv_verifier = PConvergenceVerifier()
extra_kwargs = {}
dtype = np.float64
order_values = [1, 2, 3]
if isinstance(knl, HelmholtzKernel):
extra_kwargs["k"] = 0.05
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, H2DLocalExpansion):
order_values = [10, 12]
elif isinstance(knl, YukawaKernel):
extra_kwargs["lam"] = 2
dtype = np.complex128
if knl.dim == 3:
order_values = [1, 2]
elif knl.dim == 2 and issubclass(local_expn_class, Y2DLocalExpansion):
order_values = [10, 12]
from functools import partial
for order in order_values:
out_kernels = [knl]
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(
ctx, partial(mpole_expn_class, knl),
partial(local_expn_class, knl), out_kernels)
wrangler = wcc.get_wrangler(
queue,
tree,
dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order,
kernel_extra_kwargs=extra_kwargs)
from boxtree.fmm import drive_fmm
pot, = drive_fmm(trav, wrangler, weights)
from sumpy import P2P
p2p = P2P(ctx, out_kernels, exclude_self=False)
evt, (ref_pot, ) = p2p(queue, targets, sources, (weights, ),
**extra_kwargs)
pot = pot.get()
ref_pot = ref_pot.get()
rel_err = la.norm(pot - ref_pot, np.inf) / la.norm(ref_pot, np.inf)
logger.info("order %d -> relative l2 error: %g" % (order, rel_err))
pconv_verifier.add_data_point(order, rel_err)
print(pconv_verifier)
pconv_verifier()
def test_sumpy_fmm_exclude_self(ctx_getter):
logging.basicConfig(level=logging.INFO)
ctx = ctx_getter()
queue = cl.CommandQueue(ctx)
nsources = 500
dtype = np.float64
from boxtree.tools import (make_normal_particle_array as p_normal)
knl = LaplaceKernel(2)
local_expn_class = VolumeTaylorLocalExpansion
mpole_expn_class = VolumeTaylorMultipoleExpansion
order = 10
sources = p_normal(queue, nsources, knl.dim, dtype, seed=15)
from boxtree import TreeBuilder
tb = TreeBuilder(ctx)
tree, _ = tb(queue, sources, max_particles_in_box=30, debug=True)
from boxtree.traversal import FMMTraversalBuilder
tbuild = FMMTraversalBuilder(ctx)
trav, _ = tbuild(queue, tree, debug=True)
from pyopencl.clrandom import PhiloxGenerator
rng = PhiloxGenerator(ctx)
weights = rng.uniform(queue, nsources, dtype=np.float64)
target_to_source = np.arange(tree.ntargets, dtype=np.int32)
self_extra_kwargs = {"target_to_source": target_to_source}
out_kernels = [knl]
from functools import partial
from sumpy.fmm import SumpyExpansionWranglerCodeContainer
wcc = SumpyExpansionWranglerCodeContainer(ctx,
partial(mpole_expn_class, knl),
partial(local_expn_class, knl),
out_kernels,
exclude_self=True)
wrangler = wcc.get_wrangler(
queue,
tree,
dtype,
fmm_level_to_order=lambda kernel, kernel_args, tree, lev: order,
self_extra_kwargs=self_extra_kwargs)
from boxtree.fmm import drive_fmm
pot, = drive_fmm(trav, wrangler, weights)
from sumpy import P2P
p2p = P2P(ctx, out_kernels, exclude_self=True)
evt, (ref_pot, ) = p2p(queue, sources, sources, (weights, ),
**self_extra_kwargs)
pot = pot.get()
ref_pot = ref_pot.get()
rel_err = la.norm(pot - ref_pot) / la.norm(ref_pot)
logger.info("order %d -> relative l2 error: %g" % (order, rel_err))
assert np.isclose(rel_err, 0, atol=1e-7)