def test_initmpi():
core.initmpi([2, 2], block_shape=[5, 5])
# Test grid shape is correct
assert core._context.grid_shape == (2, 2)
# Test we have the correct positions
assert core._context.grid_position == poslist[rank]
# Test the blockshape is set correctly
assert core._block_shape == (5, 5)
import numpy as np
from mpi4py import MPI
from scalapy import core
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
if size != 4:
raise Exception("Test needs 4 processes.")
core.initmpi([2, 2], block_shape=[3, 3])
def test_dm_init():
dm = core.DistributedMatrix([5, 5])
# Check global shape
assert dm.global_shape == (5, 5)
# Check local shape
shapelist = [(3, 3), (3, 2), (2, 3), (2, 2)]
assert dm.local_shape == shapelist[rank]
def test_dm_load_5x5():
## Test that a 5x5 DistributedMatrix is loaded correctly.
# Generate matrix
import numpy as np
from mpi4py import MPI
from scalapy import core
import scalapy.routines as rt
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
if size != 4:
raise Exception("Test needs 4 processes.")
core.initmpi([2, 2], block_shape=[3, 3])
allclose = lambda a, b: np.allclose(a, b, rtol=1e-4, atol=1e-6)
def test_np2self_D():
## Test copy a numpy array to a section of the distributed matrix and vice versa
am, an = 13, 5
Am, An = 39, 23
srow, scol = 3, 12
a = np.arange(am * an, dtype=np.float64).reshape(am, an)
a = np.asfortranarray(a)
gA = np.random.standard_normal((Am, An)).astype(np.float64)
gA = np.asfortranarray(gA)
from mpi4py import MPI
from scalapy import core
import scalapy.routines as rt
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
if size != 4:
raise Exception("Test needs 4 processes.")
core.initmpi([2, 2], block_shape=[16, 16])
allclose = lambda a, b: np.allclose(a, b, rtol=1e-4, atol=1e-6)
def test_cholesky_D():
## Test the Cholesky decomposition of a double precision matrix (use the
## default, upper half)
ns = 317
gA = np.random.standard_normal((ns, ns)).astype(np.float64)
gA = np.dot(gA, gA.T) # Make positive definite
gA = np.asfortranarray(gA)
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
from r_pca import R_pca
from mpi4py import MPI
from scalapy import core
import scalapy.routines as rt
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
if size != 4:
raise Exception("Test needs 4 processes.")
core.initmpi([2, 2], block_shape=[32, 32])
ps_name = 'sim_pointsource_256_700_800_256.hdf5'
ga_name = 'sim_galaxy_256_700_800_256.hdf5'
cm_name = 'sim_21cm_256_700_800_256.hdf5'
with h5py.File(ps_name, 'r') as f:
ps_map = f['map'][:, 0, :]
with h5py.File(ga_name, 'r') as f:
ga_map = f['map'][:, 0, :]
with h5py.File(cm_name, 'r') as f:
cm_map = f['map'][:, 0, :]
fg_map = ps_map + ga_map
tt_map = fg_map + cm_map # total signal
npix = ps_map.shape[-1]
import scalapy.lowlevel.pblas as pblas_ll
import scalapy.lowlevel as ll
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
allclose = lambda a, b: np.allclose(a, b, rtol=1e-4, atol=1e-6)
if size != 4:
raise Exception("Test needs 4 processes.")
core.initmpi([2, 2], block_shape=[8, 8])
def pdgemm_iter_NN(n, m, k):
gA = np.asfortranarray(np.random.standard_normal((n, k)))
gB = np.asfortranarray(np.random.standard_normal((k, m)))
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
dB = core.DistributedMatrix.from_global_array(gB, rank=0)
dC = core.DistributedMatrix([n, m], dtype=np.float64)
ll.pdgemm('N', 'N', n, m, k, 1.0, dA, dB, 0.0, dC)
gCd = dC.to_global_array(rank=0)
gC = np.asfortranarray(np.dot(gA, gB))
from scalapy import core
import scalapy.lowlevel.pblas as pblas_ll
import scalapy.lowlevel as ll
comm = MPI.COMM_WORLD
rank = comm.rank
size = comm.size
allclose = lambda a, b: np.allclose(a, b, rtol=1e-4, atol=1e-6)
if size != 4:
raise Exception("Test needs 4 processes.")
core.initmpi([2, 2], block_shape=[8, 8])
def pdgemm_iter_NN(n, m, k):
gA = np.asfortranarray(np.random.standard_normal((n, k)))
gB = np.asfortranarray(np.random.standard_normal((k, m)))
dA = core.DistributedMatrix.from_global_array(gA, rank=0)
dB = core.DistributedMatrix.from_global_array(gB, rank=0)
dC = core.DistributedMatrix([n, m], dtype=np.float64)
ll.pdgemm('N', 'N', n, m, k, 1.0, dA, dB, 0.0, dC)
gCd = dC.to_global_array(rank=0)
gC = np.asfortranarray(np.dot(gA, gB))
