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Optimize build_cartesian_ncc_matrix #331

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Optimize build_cartesian_ncc_matrix #331

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21 changes: 17 additions & 4 deletions dedalus/core/arithmetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -430,18 +430,31 @@ def build_cartesian_ncc_matrix(self, subproblem, ncc_cutoff, max_ncc_terms):
Gamma = Gamma.transpose((2, 0, 1))
# Loop over NCC modes
shape = (subproblem.field_size(out), subproblem.field_size(arg))
matrix = sparse.csr_matrix(shape, dtype=self.dtype)
subproblem_shape = subproblem.coeff_shape(out.domain)
ncc_rank = len(ncc.tensorsig)
select_all_comps = tuple(slice(None) for i in range(ncc_rank))
# Optimization: batch accumulate matrices instead of sequential addition
all_rows = []
all_cols = []
all_data = []
if np.any(self._ncc_data):
for ncc_mode in np.ndindex(self._ncc_data.shape[ncc_rank:]):
ncc_coeffs = self._ncc_data[select_all_comps + ncc_mode]
if np.max(np.abs(ncc_coeffs)) > ncc_cutoff:
mode_matrix = self.cartesian_mode_matrix(subproblem_shape, ncc.domain, arg.domain, out.domain, ncc_mode)
mode_matrix = sparse.kron(np.dot(Gamma, ncc_coeffs.ravel()), mode_matrix, format='csr')
matrix = matrix + mode_matrix
return matrix
mode_matrix = sparse.kron(np.dot(Gamma, ncc_coeffs.ravel()), mode_matrix, format='coo')
all_rows.append(mode_matrix.row)
all_cols.append(mode_matrix.col)
all_data.append(mode_matrix.data)
# Batch merge all mode matrices
if all_rows:
combined_row = np.concatenate(all_rows)
combined_col = np.concatenate(all_cols)
combined_data = np.concatenate(all_data)
matrix = sparse.coo_matrix((combined_data, (combined_row, combined_col)), shape=shape)
return matrix.tocsr()
else:
return sparse.csr_matrix(shape, dtype=self.dtype)

@classmethod
def cartesian_mode_matrix(cls, subproblem_shape, ncc_domain, arg_domain, out_domain, ncc_mode):
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