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Reduce memory usage for timeseries jac computation #1001

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merged 5 commits into from
Oct 16, 2023
+54 −38
Merged

Reduce memory usage for timeseries jac computation #1001

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06607da
jac fix for timeseries derivs
johnjasa Oct 12, 2023
074bde7
Also updated analytic phase deriv calc
johnjasa Oct 12, 2023
04530a9
Removed use_sparse flag
johnjasa Oct 16, 2023
8bdf903
Merging
johnjasa Oct 16, 2023
fba061d
pycodestyle updates
johnjasa Oct 16, 2023
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pycodestyle updates
@johnjasa
johnjasa committed Oct 16, 2023
commit fba061dc70e6b9b6c00ec5fba5a143251cee2133
5 changes: 3 additions & 2 deletions dymos/transcriptions/analytic/analytic_timeseries_output_comp.py
View file
Original file line number Diff line number Diff line change
@@ -159,7 +159,7 @@ def _add_output_configure(self, name, units, shape, desc='', src=None, rate=Fals
# Extend the data and indices using the CSR attributes of mat
jac_data.extend(mat.data)
jac_indices.extend(mat.indices + s * input_num_nodes)

# For every non-zero row in mat, update jac's indptr
new_indptr = mat.indptr[1:] + s * len(mat.data)
jac_indptr.extend(new_indptr)
@@ -168,7 +168,8 @@ def _add_output_configure(self, name, units, shape, desc='', src=None, rate=Fals
jac_indptr[-1] = len(jac_data)

# Construct the sparse jac matrix in CSR format
jac = sp.csr_matrix((jac_data, jac_indices, jac_indptr), shape=(output_num_nodes * size, input_num_nodes * size))
jac = sp.csr_matrix((jac_data, jac_indices, jac_indptr),
shape=(output_num_nodes * size, input_num_nodes * size))

# Now, if you want to get the row and column indices of the non-zero entries in the jac matrix:
jac_rows, jac_cols = jac.nonzero()
5 changes: 3 additions & 2 deletions dymos/transcriptions/pseudospectral/components/pseudospectral_timeseries_output_comp.py
View file
Original file line number Diff line number Diff line change
@@ -159,7 +159,7 @@ def _add_output_configure(self, name, units, shape, desc='', src=None, rate=Fals
# Extend the data and indices using the CSR attributes of mat
jac_data.extend(mat.data)
jac_indices.extend(mat.indices + s * input_num_nodes)

# For every non-zero row in mat, update jac's indptr
new_indptr = mat.indptr[1:] + s * len(mat.data)
jac_indptr.extend(new_indptr)
@@ -168,7 +168,8 @@ def _add_output_configure(self, name, units, shape, desc='', src=None, rate=Fals
jac_indptr[-1] = len(jac_data)

# Construct the sparse jac matrix in CSR format
jac = sp.csr_matrix((jac_data, jac_indices, jac_indptr), shape=(output_num_nodes * size, input_num_nodes * size))
jac = sp.csr_matrix((jac_data, jac_indices, jac_indptr),
shape=(output_num_nodes * size, input_num_nodes * size))

# Now, if you want to get the row and column indices of the non-zero entries in the jac matrix:
jac_rows, jac_cols = jac.nonzero()