def _init_block(self):
self._blocks = [[self]]
rset, cset = self.sparsity.dsets
if (isinstance(rset, GlobalDataSet)
or isinstance(cset, GlobalDataSet)):
self._init_global_block()
return
mat = PETSc.Mat()
row_lg = rset.lgmap
col_lg = cset.lgmap
rdim, cdim = self.dims[0][0]
if rdim == cdim and rdim > 1 and self.sparsity._block_sparse:
# Size is total number of rows and columns, but the
# /sparsity/ is the block sparsity.
block_sparse = True
create = mat.createBAIJ
else:
# Size is total number of rows and columns, sparsity is
# the /dof/ sparsity.
block_sparse = False
create = mat.createAIJ
create(size=((self.nrows, None), (self.ncols, None)),
nnz=(self.sparsity.nnz, self.sparsity.onnz),
bsize=(rdim, cdim),
comm=self.comm)
mat.setLGMap(rmap=row_lg, cmap=col_lg)
# Stash entries destined for other processors
mat.setOption(mat.Option.IGNORE_OFF_PROC_ENTRIES, False)
# Any add or insertion that would generate a new entry that has not
# been preallocated will raise an error
mat.setOption(mat.Option.NEW_NONZERO_ALLOCATION_ERR, True)
# Do not ignore zeros while we fill the initial matrix so that
# petsc doesn't compress things out.
if not block_sparse:
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, False)
# When zeroing rows (e.g. for enforcing Dirichlet bcs), keep those in
# the nonzero structure of the matrix. Otherwise PETSc would compact
# the sparsity and render our sparsity caching useless.
mat.setOption(mat.Option.KEEP_NONZERO_PATTERN, True)
# We completely fill the allocated matrix when zeroing the
# entries, so raise an error if we "missed" one.
mat.setOption(mat.Option.UNUSED_NONZERO_LOCATION_ERR, True)
# Put zeros in all the places we might eventually put a value.
with timed_region("MatZeroInitial"):
sparsity.fill_with_zeros(mat,
self.sparsity.dims[0][0],
self.sparsity.maps,
self.sparsity.iteration_regions,
set_diag=self.sparsity._has_diagonal)
mat.assemble()
mat.setOption(mat.Option.NEW_NONZERO_LOCATION_ERR, True)
# Now we've filled up our matrix, so the sparsity is
# "complete", we can ignore subsequent zero entries.
if not block_sparse:
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, True)
self.handle = mat
def _init_block(self):
self._blocks = [[self]]
rset, cset = self.sparsity.dsets
if (isinstance(rset, GlobalDataSet) or isinstance(cset, GlobalDataSet)):
self._init_global_block()
return
mat = PETSc.Mat()
row_lg = rset.lgmap
col_lg = cset.lgmap
rdim, cdim = self.dims[0][0]
if rdim == cdim and rdim > 1 and self.sparsity._block_sparse:
# Size is total number of rows and columns, but the
# /sparsity/ is the block sparsity.
block_sparse = True
create = mat.createBAIJ
else:
# Size is total number of rows and columns, sparsity is
# the /dof/ sparsity.
block_sparse = False
create = mat.createAIJ
create(size=((self.nrows, None),
(self.ncols, None)),
nnz=(self.sparsity.nnz, self.sparsity.onnz),
bsize=(rdim, cdim),
comm=self.comm)
mat.setLGMap(rmap=row_lg, cmap=col_lg)
# Stash entries destined for other processors
mat.setOption(mat.Option.IGNORE_OFF_PROC_ENTRIES, False)
# Any add or insertion that would generate a new entry that has not
# been preallocated will raise an error
mat.setOption(mat.Option.NEW_NONZERO_ALLOCATION_ERR, True)
# Do not ignore zeros while we fill the initial matrix so that
# petsc doesn't compress things out.
if not block_sparse:
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, False)
# When zeroing rows (e.g. for enforcing Dirichlet bcs), keep those in
# the nonzero structure of the matrix. Otherwise PETSc would compact
# the sparsity and render our sparsity caching useless.
mat.setOption(mat.Option.KEEP_NONZERO_PATTERN, True)
# We completely fill the allocated matrix when zeroing the
# entries, so raise an error if we "missed" one.
mat.setOption(mat.Option.UNUSED_NONZERO_LOCATION_ERR, True)
# Put zeros in all the places we might eventually put a value.
with timed_region("MatZeroInitial"):
sparsity.fill_with_zeros(mat, self.sparsity.dims[0][0],
self.sparsity.maps, self.sparsity.iteration_regions,
set_diag=self.sparsity._has_diagonal)
mat.assemble()
mat.setOption(mat.Option.NEW_NONZERO_LOCATION_ERR, True)
# Now we've filled up our matrix, so the sparsity is
# "complete", we can ignore subsequent zero entries.
if not block_sparse:
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, True)
self.handle = mat
def _init_monolithic(self):
mat = PETSc.Mat()
rset, cset = self.sparsity.dsets
if rset.cdim != 1:
rlgmap = rset.unblocked_lgmap
else:
rlgmap = rset.lgmap
if cset.cdim != 1:
clgmap = cset.unblocked_lgmap
else:
clgmap = cset.lgmap
mat.createAIJ(size=((self.nrows, None), (self.ncols, None)),
nnz=(self.sparsity.nnz, self.sparsity.onnz),
bsize=1,
comm=self.comm)
mat.setLGMap(rmap=rlgmap, cmap=clgmap)
self.handle = mat
self._blocks = []
rows, cols = self.sparsity.shape
for i in range(rows):
row = []
for j in range(cols):
row.append(MatBlock(self, i, j))
self._blocks.append(row)
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, False)
mat.setOption(mat.Option.KEEP_NONZERO_PATTERN, True)
# We completely fill the allocated matrix when zeroing the
# entries, so raise an error if we "missed" one.
mat.setOption(mat.Option.UNUSED_NONZERO_LOCATION_ERR, True)
mat.setOption(mat.Option.IGNORE_OFF_PROC_ENTRIES, False)
mat.setOption(mat.Option.NEW_NONZERO_ALLOCATION_ERR, True)
# The first assembly (filling with zeros) sets all possible entries.
mat.setOption(mat.Option.SUBSET_OFF_PROC_ENTRIES, True)
# Put zeros in all the places we might eventually put a value.
with timed_region("MatZeroInitial"):
for i in range(rows):
for j in range(cols):
sparsity.fill_with_zeros(
self[i, j].handle,
self[i, j].sparsity.dims[0][0],
self[i, j].sparsity.maps,
set_diag=self[i, j].sparsity._has_diagonal)
self[i, j].handle.assemble()
mat.assemble()
mat.setOption(mat.Option.NEW_NONZERO_LOCATION_ERR, True)
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, True)
def _init_monolithic(self):
mat = PETSc.Mat()
rset, cset = self.sparsity.dsets
if rset.cdim != 1:
rlgmap = rset.unblocked_lgmap
else:
rlgmap = rset.lgmap
if cset.cdim != 1:
clgmap = cset.unblocked_lgmap
else:
clgmap = cset.lgmap
mat.createAIJ(size=((self.nrows, None), (self.ncols, None)),
nnz=(self.sparsity.nnz, self.sparsity.onnz),
bsize=1,
comm=self.comm)
mat.setLGMap(rmap=rlgmap, cmap=clgmap)
self.handle = mat
self._blocks = []
rows, cols = self.sparsity.shape
for i in range(rows):
row = []
for j in range(cols):
row.append(MatBlock(self, i, j))
self._blocks.append(row)
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, False)
mat.setOption(mat.Option.KEEP_NONZERO_PATTERN, True)
# We completely fill the allocated matrix when zeroing the
# entries, so raise an error if we "missed" one.
mat.setOption(mat.Option.UNUSED_NONZERO_LOCATION_ERR, True)
mat.setOption(mat.Option.IGNORE_OFF_PROC_ENTRIES, True)
mat.setOption(mat.Option.NEW_NONZERO_ALLOCATION_ERR, True)
# Put zeros in all the places we might eventually put a value.
with timed_region("MatZeroInitial"):
for i in range(rows):
for j in range(cols):
sparsity.fill_with_zeros(self[i, j].handle,
self[i, j].sparsity.dims[0][0],
self[i, j].sparsity.maps,
set_diag=self[i, j].sparsity._has_diagonal)
mat.assemble()
mat.setOption(mat.Option.IGNORE_ZERO_ENTRIES, True)