def test_processor_layout():
from pyelpa import ProcessorLayout
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
assert (comm.Get_size() == layout_p.np_cols * layout_p.np_rows)
assert (layout_p.my_prow >= 0)
assert (layout_p.my_pcol >= 0)
assert (layout_p.my_prow <= comm.Get_size())
assert (layout_p.my_pcol <= comm.Get_size())
def test_distributed_matrix_from_processor_layout(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.float32, np.complex64, np.complex128]:
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
assert (a.data.dtype == dtype)
assert (a.data.shape == (a.na_rows, a.na_cols))
def test_distributed_matrix_like_other_matrix(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.float32, np.complex64, np.complex128]:
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
b = DistributedMatrix.like(a)
assert (a.na == b.na)
assert (a.nev == b.nev)
assert (a.nblk == b.nblk)
assert (a.data.dtype == b.data.dtype)
assert (a.data.shape == b.data.shape)
def test_call_eigenvalues(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix, Elpa
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.complex128]:
# create arrays
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
a.data[:, :] = np.random.rand(a.na_rows, a.na_cols).astype(dtype)
ev = np.zeros(na, dtype=np.float64)
e = Elpa.from_distributed_matrix(a)
e.eigenvalues(a.data, ev)
def test_distributed_matrix_global_index(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.complex128]:
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
for local_row in range(a.na_rows):
for local_col in range(a.na_cols):
global_row, global_col = a.get_global_index(
local_row, local_col)
l_row, l_col = a.get_local_index(global_row, global_col)
assert (global_row >= 0 and global_row < a.na)
assert (global_col >= 0 and global_col < a.na)
assert (local_row == l_row and local_col == l_col)
def test_setting_global_matrix(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.complex128]:
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
# get global matrix that is equal on all cores
matrix = get_random_vector(na * na).reshape(na, na).astype(dtype)
a.set_data_from_global_matrix(matrix)
# check data
for global_row in range(a.na):
for global_col in range(a.na):
if not a.is_local_index(global_row, global_col):
continue
local_row, local_col = a.get_local_index(
global_row, global_col)
assert (a.data[local_row, local_col] == matrix[global_row,
global_col])
def test_distributed_matrix_indexing_loop(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.complex128]:
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
for local_row in range(a.na_rows):
for local_col in range(a.na_cols):
global_row, global_col = a.get_global_index(
local_row, local_col)
a.data[local_row, local_col] = global_row * 10 + global_col
for global_row in range(a.na):
for global_col in range(a.na):
if not a.is_local_index(global_row, global_col):
continue
local_row, local_col = a.get_local_index(
global_row, global_col)
assert (a.data[local_row,
local_col] == global_row * 10 + global_col)
def test_compare_eigenvalues_to_those_from_eigenvectors(na, nev, nblk):
import numpy as np
from pyelpa import ProcessorLayout, DistributedMatrix, Elpa
from mpi4py import MPI
comm = MPI.COMM_WORLD
layout_p = ProcessorLayout(comm)
for dtype in [np.float64, np.complex128]:
# create arrays
a = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
random_matrix = np.random.rand(a.na_rows, a.na_cols).astype(dtype)
a.data[:, :] = random_matrix
q = DistributedMatrix(layout_p, na, nev, nblk, dtype=dtype)
ev = np.zeros(na, dtype=np.float64)
ev2 = np.zeros(na, dtype=np.float64)
e = Elpa.from_distributed_matrix(a)
e.eigenvectors(a.data, ev, q.data)
a.data[:, :] = random_matrix
e.eigenvalues(a.data, ev2)
assert (np.allclose(ev, ev2))