Commit 8a042144 authored by Michael Kuron's avatar Michael Kuron
Browse files

Implement new create_staggered_kernel_2

It takes a list of assignments to staggered fields and otherwise behaves like create_kernel.
The old create_staggered_kernel was not removed because all the optimizations still need to be ported over.
parent a7460abb
......@@ -8,6 +8,7 @@ from pystencils.astnodes import Block, Conditional, LoopOverCoordinate, SympyAss
from pystencils.cpu.vectorization import vectorize
from pystencils.gpucuda.indexing import indexing_creator_from_params
from pystencils.simp.assignment_collection import AssignmentCollection
from pystencils.stencil import direction_string_to_offset, inverse_direction_string
from pystencils.transformations import (
loop_blocking, move_constants_before_loop, remove_conditionals_in_staggered_kernel)
......@@ -287,3 +288,64 @@ def create_staggered_kernel(staggered_field, expressions, subexpressions=(), tar
elif isinstance(cpu_vectorize_info, dict):
vectorize(ast, **cpu_vectorize_info)
return ast
def create_staggered_kernel_2(assignments, **kwargs):
"""Kernel that updates a staggered field.
.. image:: /img/staggered_grid.svg
For a staggered field, the first index coordinate defines the location of the staggered value.
Further index coordinates can be used to store vectors/tensors at each point.
Args:
assignments: a sequence of assignments or AssignmentCollection with one item for each staggered grid point.
When storing vectors/tensors, the number of items expected is multiplied with the number of
components.
kwargs: passed directly to create_kernel
"""
assert 'ghost_layers' not in kwargs
subexpressions = ()
if isinstance(assignments, AssignmentCollection):
assignments = assignments.main_assignments
subexpressions = assignments.subexpressions
if len(set([a.lhs.field for a in assignments])) != 1:
raise ValueError("All assignments need to be made to the same staggered field")
staggered_field = assignments[0].lhs.field
dim = staggered_field.spatial_dimensions
points = staggered_field.index_shape[0]
values_per_point = sp.Mul(*staggered_field.index_shape[1:])
assert len(assignments) == points * values_per_point
counters = [LoopOverCoordinate.get_loop_counter_symbol(i) for i in range(dim)]
final_assignments = []
def condition(direction):
"""exclude those staggered points that correspond to fluxes between ghost cells"""
exclusions = set(["E", "W", "N", "S"])
if dim == 3:
exclusions.update("T", "B")
for elementary_direction in direction:
exclusions.remove(inverse_direction_string(elementary_direction))
conditions = []
for e in exclusions:
offset = direction_string_to_offset(e)
for i, o in enumerate(offset):
if o == 1:
conditions.append(counters[i] < staggered_field.shape[i] - 1)
elif o == -1:
conditions.append(counters[i] > 0)
return sp.And(*conditions)
for d, direction in zip(range(points), staggered_field.staggered_stencil):
sp_assignments = [SympyAssignment(assignments[d].lhs, assignments[d].rhs)] + \
[SympyAssignment(s.lhs, s.rhs) for s in subexpressions]
last_conditional = Conditional(condition(direction), Block(sp_assignments))
final_assignments.append(last_conditional)
ghost_layers = [(1, 0)] * dim
ast = create_kernel(final_assignments, ghost_layers=ghost_layers, **kwargs)
return ast
......@@ -16,6 +16,11 @@ def inverse_direction(direction):
return tuple([-i for i in direction])
def inverse_direction_string(direction):
"""Returns inverse of given direction string"""
return offset_to_direction_string(inverse_direction(direction_string_to_offset(direction)))
def is_valid(stencil, max_neighborhood=None):
"""
Tests if a nested sequence is a valid stencil i.e. all the inner sequences have the same length.
......
import pystencils as ps
import numpy as np
class TestDiffusion:
def _run(self, num_neighbors):
L = (40, 40)
D = 0.066
dt = 1
T = 100
dh = ps.create_data_handling(L, periodicity=True, default_target='cpu')
c = dh.add_array('c', values_per_cell=1)
j = dh.add_array('j', values_per_cell=num_neighbors, field_type=ps.FieldType.STAGGERED_FLUX)
x_staggered = - c[-1, 0] + c[0, 0]
y_staggered = - c[0, -1] + c[0, 0]
xy_staggered = - c[-1, -1] + c[0, 0]
xY_staggered = - c[-1, 1] + c[0, 0]
jj = j.staggered_access
divergence = -1 * D / (1 + np.sqrt(2) if j.index_shape[0] == 4 else 1) * \
sum([jj(d) for d in j.staggered_stencil +
[ps.stencil.inverse_direction_string(d) for d in j.staggered_stencil]])
update = [ps.Assignment(c.center, c.center + dt * divergence)]
flux = [ps.Assignment(j.staggered_access("W"), x_staggered),
ps.Assignment(j.staggered_access("S"), y_staggered)]
if j.index_shape[0] == 4:
flux += [ps.Assignment(j.staggered_access("SW"), xy_staggered),
ps.Assignment(j.staggered_access("NW"), xY_staggered)]
staggered_kernel = ps.kernelcreation.create_staggered_kernel_2(flux, target=dh.default_target).compile()
div_kernel = ps.create_kernel(update, target=dh.default_target).compile()
def time_loop(steps):
sync = dh.synchronization_function([c.name])
dh.all_to_gpu()
for i in range(steps):
sync()
dh.run_kernel(staggered_kernel)
dh.run_kernel(div_kernel)
dh.all_to_cpu()
def init():
dh.fill(c.name, 0)
dh.fill(j.name, np.nan)
dh.cpu_arrays[c.name][L[0] // 2:L[0] // 2 + 2, L[1] // 2:L[1] // 2 + 2] = 1.0
init()
time_loop(T)
reference = np.empty(L)
for x in range(L[0]):
for y in range(L[1]):
r = np.array([x, y]) - L[0] / 2 + 0.5
reference[x, y] = (4 * np.pi * D * T)**(-dh.dim / 2) * np.exp(-np.dot(r, r) / (4 * D * T)) * (2**dh.dim)
if num_neighbors == 2:
assert np.abs(dh.gather_array(c.name) - reference).max() < 1e-3
else:
assert np.abs(dh.gather_array(c.name) - reference).max() < 1e-2
def test_diffusion_2(self):
self._run(2)
def test_diffusion_4(self):
self._run(4)
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