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_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_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_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_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_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()
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_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()
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()
