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Parallelise eigen{vec,val} calculations #199

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Parallelise eigen{vec,val} calculations #199

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ac5ebe8
Parallelise eigen{vec,val} calculations
dstansby Dec 11, 2022
82d2d6a
Only parallel for large number of matrices
dstansby Dec 11, 2022
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16 changes: 15 additions & 1 deletion fiasco/ions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,9 @@
"""
import astropy.constants as const
import astropy.units as u
import multiprocessing as mp
import numpy as np
import os

from functools import cached_property
from scipy.interpolate import interp1d, splev, splrep
Expand Down Expand Up @@ -454,8 +456,20 @@ def level_populations(self,
c_matrix[:, level-1, level-1] -= d_p*(ex_diagonal_p + dex_diagonal_p)
c_matrix[:, lower_level_p-1, upper_level_p-1] += d_p * dex_rate_p
c_matrix[:, upper_level_p-1, lower_level_p-1] += d_p * ex_rate_p

# Invert matrix
val, vec = np.linalg.eig(c_matrix.value)
if c_matrix.shape[0] > 12:
# Overheads of multiprocessing mean this is only worth
# parallelising for many matrices to solve
os.environ['OMP_NUM_THREADS'] = "1"
with mp.Pool() as p:
val_vecs = p.map(np.linalg.eig, c_matrix.value)

val = np.stack([v[0] for v in val_vecs])
vec = np.stack([v[1] for v in val_vecs])
else:
val, vec = np.linalg.eig(c_matrix.value)

# Eigenvectors with eigenvalues closest to zero are the solutions to the homogeneous
# system of linear equations
# NOTE: Sometimes eigenvalues may have complex component due to numerical stability.
Expand Down