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import pyopencl as cl
from sympy.core.cache import clear_cache
import numpy as np
from meshmode.discretization import Discretization
from pytential import bind, sym, norm
from sumpy.kernel import LaplaceKernel
from pytential.qbx import QBXLayerPotentialSource
from pytential.qbx.distributed import DistributedQBXLayerPotentialSource
from mpi4py import MPI
import logging
import os
# Configure logging
logging.basicConfig(level=os.environ.get("LOGLEVEL", "WARNING"))
logging.getLogger("pytential.qbx.distributed").setLevel(logging.INFO)
warnings.filterwarnings("ignore", category=UserWarning)
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# Disable sympy cache
clear_cache()
# Setup PyOpenCL
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
class GreenExpr(object):
zero_op_name = "green"
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)
order_drop = 0
def get_sphere_mesh(refinement_increment, target_order):
from meshmode.mesh.generation import generate_icosphere
mesh = generate_icosphere(1, target_order)
from meshmode.mesh.refinement import Refiner
refiner = Refiner(mesh)
for i in range(refinement_increment):
flags = np.ones(mesh.nelements, dtype=bool)
refiner.refine(flags)
mesh = refiner.get_current_mesh()
return mesh
# Parameters
k = 0
qbx_order = 3
fmm_order = 10
_expansion_stick_out_factor = 0.5
expr = GreenExpr()
def generate_expr(refinement_increment, target_order, lp_source_contructor):
mesh = get_sphere_mesh(refinement_increment, target_order)
d = mesh.ambient_dim
lap_k_sym = LaplaceKernel(d)
k_sym = lap_k_sym
knl_kwargs = {}
from meshmode.discretization import Discretization
from meshmode.discretization.poly_element import \
InterpolatoryQuadratureSimplexGroupFactory
pre_density_discr = Discretization(
ctx, mesh,
InterpolatoryQuadratureSimplexGroupFactory(target_order))
refiner_extra_kwargs = {}
qbx, _ = lp_source_contructor(
pre_density_discr, 4 * target_order,
qbx_order,
fmm_order=fmm_order,
_expansions_in_tree_have_extent=True,
_expansion_stick_out_factor=_expansion_stick_out_factor
).with_refinement(**refiner_extra_kwargs)
density_discr = qbx.density_discr
# {{{ compute values of a solution to the PDE
nodes_host = density_discr.nodes().get(queue)
normal = bind(density_discr, sym.normal(d))(queue).as_vector(np.object)
normal_host = [normal[j].get() for j in range(d)]
center = np.array([3, 1, 2])[:d]
diff = nodes_host - center[:, np.newaxis]
dist_squared = np.sum(diff ** 2, axis=0)
dist = np.sqrt(dist_squared)
if d == 2:
u = np.log(dist)
grad_u = diff / dist_squared
elif d == 3:
u = 1 / dist
grad_u = -diff / dist ** 3
else:
assert False
dn_u = 0
for i in range(d):
dn_u = dn_u + normal_host[i] * grad_u[i]
# }}}
u_dev = cl.array.to_device(queue, u)
dn_u_dev = cl.array.to_device(queue, dn_u)
grad_u_dev = cl.array.to_device(queue, grad_u)
bound_op = bind(qbx, expr.get_zero_op(k_sym, **knl_kwargs))
return bound_op, {'u': u_dev, 'dn_u': dn_u_dev, 'grad_u': grad_u_dev, 'k': k}
def train():
from pytential.qbx.perf_model import QBXPerformanceModel
model = QBXPerformanceModel(ctx, True)
model.loadjson('model.json')
training_cases = [
(0, 6),
(0, 6),
(0, 8),
(1, 8),
(1, 10),
(1, 10)
]
for refinement_increment, target_order in training_cases:
expr, context = generate_expr(refinement_increment, target_order, QBXLayerPotentialSource)
model.time_qbx_performance(queue, expr, context)
model.savejson('model.json')
def eval():
# MPI information
comm = MPI.COMM_WORLD
current_rank = comm.Get_rank()
if current_rank == 0:
import functools
lp_source_contructor = functools.partial(DistributedQBXLayerPotentialSource, comm)
bound_op, context = generate_expr(3, 10, lp_source_contructor)
bound_op.eval(queue, context=context)
else:
lp_source = DistributedQBXLayerPotentialSource(comm, None, None)
distribute_geo_data = lp_source.distibuted_geo_data(None, queue, None)
from pytential.qbx.distributed import drive_dfmm
weights = None
drive_dfmm(queue, weights, distribute_geo_data, comm=comm)
if __name__ == '__main__':
import sys
if len(sys.argv) != 2:
raise RuntimeError("Please specify train or eval")
if sys.argv[1] == 'train':
train()
elif sys.argv[1] == 'eval':
eval()
else:
raise RuntimeError("Unrecognized command line argument. Please specify train or eval.")