def test_from_global_dim_data_bc(self):
""" Test creation of a block-cyclic array. """
rows, cols = 5, 9
global_dim_data = (
# dim 0
{
'dist_type': 'c',
'proc_grid_size': 2,
'size': rows,
'block_size': 2,
},
# dim 1
{
'dist_type': 'c',
'proc_grid_size': 2,
'size': cols,
'block_size': 2,
},
)
distribution = Distribution.from_global_dim_data(
self.context, global_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
las = distarr.get_localarrays()
local_shapes = [la.local_shape for la in las]
self.assertSequenceEqual(local_shapes, [(3, 5), (3, 4), (2, 5),
(2, 4)])
def test___init__(self):
shape = (5, 5)
distribution = Distribution(self.context, shape, ('b', 'c'))
da = DistArray(distribution, dtype=int)
da.fill(42)
nda = numpy.empty(shape, dtype=int)
nda.fill(42)
assert_array_equal(da.tondarray(), nda)
def test_from_global_dim_data_irregular_block(self):
bounds = (0, 2, 3, 4, 10)
glb_dim_data = ({'dist_type': 'b', 'bounds': bounds}, )
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test_irregular_block_assignment(self):
global_dim_data = ({
'dist_type': 'b',
'bounds': (0, 5),
}, {
'dist_type': 'b',
'bounds': (0, 2, 6, 7, 9),
})
distribution = Distribution.from_global_dim_data(
self.context, global_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test_from_global_dim_data_1d(self):
total_size = 40
list_of_indices = [
[29, 38, 18, 19, 11, 33, 10, 1, 22, 25],
[5, 15, 34, 12, 16, 24, 23, 39, 6, 36],
[0, 7, 27, 4, 32, 37, 21, 26, 9, 17],
[35, 14, 20, 13, 3, 30, 2, 8, 28, 31],
]
glb_dim_data = ({
'dist_type': 'u',
'indices': list_of_indices,
}, )
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
for i in range(total_size):
distarr[i] = i
localarrays = distarr.get_localarrays()
for i, arr in enumerate(localarrays):
assert_allclose(arr, list_of_indices[i])
def test_from_global_dim_data_uu(self):
rows = 6
cols = 20
row_ixs = numpy.random.permutation(range(rows))
col_ixs = numpy.random.permutation(range(cols))
row_indices = [row_ixs[:rows // 2], row_ixs[rows // 2:]]
col_indices = [col_ixs[:cols // 4], col_ixs[cols // 4:]]
glb_dim_data = (
{
'dist_type': 'u',
'indices': row_indices
},
{
'dist_type': 'u',
'indices': col_indices
},
)
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
def test_from_global_dim_data_bu(self):
rows = 9
row_break_point = rows // 2
cols = 10
col_indices = numpy.random.permutation(range(cols))
col_break_point = len(col_indices) // 3
indices = [
col_indices[:col_break_point], col_indices[col_break_point:]
]
glb_dim_data = (
{
'dist_type': 'b',
'bounds': (0, row_break_point, rows)
},
{
'dist_type': 'u',
'indices': indices
},
)
distribution = Distribution.from_global_dim_data(
self.context, glb_dim_data)
distarr = DistArray(distribution, dtype=int)
distarr.toarray()
