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import numpy as np
import waLBerla as wlb
from pystencils import make_slice
from tempfile import TemporaryDirectory
from pathlib import Path
from pystencils.boundaries import BoundaryHandling, Neumann
from pystencils.slicing import slice_from_direction
from pystencils.datahandling.parallel_datahandling import ParallelDataHandling
from pystencils.datahandling import create_data_handling
from pystencils_tests.test_datahandling import (
access_and_gather, kernel_execution_jacobi, reduction, synchronization, vtk_output)
SCRIPT_FOLDER = Path(__file__).parent.absolute()
INPUT_FOLDER = SCRIPT_FOLDER / "test_data"
try:
import pytest
except ImportError:
import unittest.mock
pytest = unittest.mock.MagicMock()
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def test_access_and_gather():
block_size = (4, 7, 1)
num_blocks = (3, 2, 1)
cells = tuple(a * b for a, b in zip(block_size, num_blocks))
blocks = wlb.createUniformBlockGrid(blocks=num_blocks, cellsPerBlock=block_size, oneBlockPerProcess=False,
periodic=(1, 1, 1))
dh = ParallelDataHandling(blocks, default_ghost_layers=2)
access_and_gather(dh, cells)
synchronization(dh, test_gpu=False)
if hasattr(wlb, 'cuda'):
synchronization(dh, test_gpu=True)
def test_gpu():
if not hasattr(wlb, 'cuda'):
print("Skip GPU tests because walberla was built without CUDA")
return
block_size = (4, 7, 1)
num_blocks = (3, 2, 1)
blocks = wlb.createUniformBlockGrid(blocks=num_blocks, cellsPerBlock=block_size, oneBlockPerProcess=False)
dh = ParallelDataHandling(blocks, default_ghost_layers=2)
dh.add_array('v', values_per_cell=3, dtype=np.int64, ghost_layers=2, gpu=True)
for b in dh.iterate():
b['v'].fill(42)
dh.all_to_gpu()
for b in dh.iterate():
b['v'].fill(0)
dh.to_cpu('v')
for b in dh.iterate():
np.testing.assert_equal(b['v'], 42)
def test_kernel():
for gpu in (True, False):
if gpu and not hasattr(wlb, 'cuda'):
print("Skipping CUDA tests because walberla was built without GPU support")
continue
# 3D
blocks = wlb.createUniformBlockGrid(blocks=(3, 2, 4), cellsPerBlock=(3, 2, 5), oneBlockPerProcess=False)
dh = ParallelDataHandling(blocks)
kernel_execution_jacobi(dh, 'gpu')
reduction(dh)
# 2D
blocks = wlb.createUniformBlockGrid(blocks=(3, 2, 1), cellsPerBlock=(3, 2, 1), oneBlockPerProcess=False)
dh = ParallelDataHandling(blocks, dim=2)
kernel_execution_jacobi(dh, 'gpu')
reduction(dh)
def test_vtk_output():
blocks = wlb.createUniformBlockGrid(blocks=(3, 2, 4), cellsPerBlock=(3, 2, 5), oneBlockPerProcess=False)
dh = ParallelDataHandling(blocks)
vtk_output(dh)
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def test_block_iteration():
block_size = (16, 16, 16)
num_blocks = (2, 2, 2)
blocks = wlb.createUniformBlockGrid(blocks=num_blocks, cellsPerBlock=block_size, oneBlockPerProcess=False)
dh = ParallelDataHandling(blocks, default_ghost_layers=2)
dh.add_array('v', values_per_cell=1, dtype=np.int64, ghost_layers=2, gpu=True)
for b in dh.iterate():
b['v'].fill(1)
s = 0
for b in dh.iterate():
s += np.sum(b['v'])
assert s == 40*40*40
sl = make_slice[0:18, 0:18, 0:18]
for b in dh.iterate(slice_obj=sl):
b['v'].fill(0)
s = 0
for b in dh.iterate():
s += np.sum(b['v'])
assert s == 40*40*40 - 20*20*20
def test_getter_setter():
block_size = (2, 2, 2)
num_blocks = (2, 2, 2)
blocks = wlb.createUniformBlockGrid(blocks=num_blocks, cellsPerBlock=block_size, oneBlockPerProcess=False)
dh = ParallelDataHandling(blocks, default_ghost_layers=2)
dh.add_array('v', values_per_cell=1, dtype=np.int64, ghost_layers=2, gpu=True)
assert dh.shape == (4, 4, 4)
assert dh.periodicity == (False, False, False)
assert dh.values_per_cell('v') == 1
assert dh.has_data('v') is True
assert 'v' in dh.array_names
dh.log_on_root()
assert dh.is_root is True
assert dh.world_rank == 0
dh.to_gpu('v')
assert dh.is_on_gpu('v') is True
dh.all_to_cpu()
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def test_parallel_datahandling_boundary_conditions():
pytest.importorskip('waLBerla.cuda')
dh = create_data_handling(domain_size=(7, 7), periodicity=True, parallel=True, default_target="gpu")
src = dh.add_array('src')
src2 = dh.add_array('src2')
dh.fill("src", 0.0, ghost_layers=True)
dh.fill("src", 1.0, ghost_layers=False)
src_cpu = dh.add_array('src_cpu', gpu=False)
dh.fill("src_cpu", 0.0, ghost_layers=True)
dh.fill("src_cpu", 1.0, ghost_layers=False)
boundary_stencil = [(1, 0), (-1, 0), (0, 1), (0, -1)]
boundary_handling_cpu = BoundaryHandling(dh, src_cpu.name, boundary_stencil,
name="boundary_handling_cpu", target='cpu')
boundary_handling = BoundaryHandling(dh, src.name, boundary_stencil,
name="boundary_handling_gpu", target='gpu')
neumann = Neumann()
for d in ('N', 'S', 'W', 'E'):
boundary_handling.set_boundary(neumann, slice_from_direction(d, dim=2))
boundary_handling_cpu.set_boundary(neumann, slice_from_direction(d, dim=2))
boundary_handling.prepare()
boundary_handling_cpu.prepare()
boundary_handling_cpu()
dh.all_to_gpu()
boundary_handling()
dh.all_to_cpu()
for block in dh.iterate():
np.testing.assert_almost_equal(block["src_cpu"], block["src"])
assert dh.custom_data_names == ('boundary_handling_cpuIndexArrays', 'boundary_handling_gpuIndexArrays')
dh.swap("src", "src2", gpu=True)
def test_save_data():
domain_shape = (2, 2)
dh = create_data_handling(domain_size=domain_shape, default_ghost_layers=1, parallel=True)
dh.add_array("src", values_per_cell=9)
dh.fill("src", 1.0, ghost_layers=True)
dh.add_array("dst", values_per_cell=9)
dh.fill("dst", 1.0, ghost_layers=True)
dh.save_all(str(INPUT_FOLDER) + '/datahandling_parallel_save_test')
def test_load_data():
domain_shape = (2, 2)
dh = create_data_handling(domain_size=domain_shape, default_ghost_layers=1, parallel=True)
dh.add_array("src", values_per_cell=9)
dh.fill("src", 0.0, ghost_layers=True)
dh.add_array("dst", values_per_cell=9)
dh.fill("dst", 0.0, ghost_layers=True)
dh.load_all(str(INPUT_FOLDER) + '/datahandling_parallel_load_test')
assert np.all(dh.gather_array('src')) == 1
assert np.all(dh.gather_array('src')) == 1