def test_scatter_gather(self):
"""
Test scattering and gathering of sparse data from and to a single MPI rank.
The initial data distribution looks like:
rank0 rank1 rank2 rank3
[0, 1, 2, 3] [] [] []
Logically (i.e., given point coordinates and domain decomposition), 0 belongs
to rank0, 1 belongs to rank1, etc. Thus, after scattering, the data distribution
is expected to be:
rank0 rank1 rank2 rank3
[0] [1] [2] [3]
Then, locally on each rank, some trivial computation is performed, and we obtain:
rank0 rank1 rank2 rank3
[0] [2] [4] [6]
Finally, we gather the data values and we get:
rank0 rank1 rank2 rank3
[0, 2, 4, 6] [] [] []
"""
grid = Grid(shape=(4, 4), extent=(4.0, 4.0))
# Initialization
if grid.distributor.myrank == 0:
coords = [(1., 1.), (1., 3.), (3., 1.), (3., 3.)]
else:
coords = []
sf = SparseFunction(name='sf',
grid=grid,
npoint=len(coords),
coordinates=coords)
sf.data[:] = list(range(len(coords)))
# Scatter
data = sf._dist_scatter()[sf]
assert len(data) == 1
assert data[0] == grid.distributor.myrank
# Do some local computation
data = data * 2
# Gather
sf._dist_gather(data)
if grid.distributor.myrank == 0:
assert np.all(sf.data == [0, 2, 4, 6])
else:
assert not sf.data
def test_scatter_gather(self):
"""
Test scattering and gathering of sparse data from and to a single MPI rank.
The initial data distribution (A, B, C, and D are generic values) looks like:
rank0 rank1 rank2 rank3
[D] [C] [B] [A]
Logically (i.e., given point coordinates and domain decomposition), A belongs
to rank0, B belongs to rank1, etc. Thus, after scattering, the data distribution
is expected to be:
rank0 rank1 rank2 rank3
[A] [B] [C] [D]
Then, locally on each rank, a trivial *2 multiplication is performed:
rank0 rank1 rank2 rank3
[A*2] [B*2] [C*2] [D*2]
Finally, we gather the data values and we get:
rank0 rank1 rank2 rank3
[D*2] [C*2] [B*2] [A*2]
"""
grid = Grid(shape=(4, 4), extent=(4.0, 4.0))
# Initialization
data = np.array([3, 2, 1, 0])
coords = np.array([(3., 3.), (3., 1.), (1., 3.), (1., 1.)])
sf = SparseFunction(name='sf',
grid=grid,
npoint=len(coords),
coordinates=coords)
sf.data[:] = data
# Scatter
loc_data = sf._dist_scatter()[sf]
assert len(loc_data) == 1
assert loc_data[0] == grid.distributor.myrank
# Do some local computation
loc_data = loc_data * 2
# Gather
sf._dist_gather(loc_data)
assert len(sf.data) == 1
assert np.all(sf.data == data[sf.local_indices] * 2)