def compute_grid_score_wrapper(corr_matrix, grid_matrix):
"""
Split the grid matrix and use compute matrix score
function on each splitted block using multiprocessing
-> x 10 acceleration on 119*119 grid compared to linear
approach
-> return the grid score (actually sum of the splitted grid's score)
"""
## importation
import numpy as np
from pathos.multiprocessing import ProcessingPool as Pool
from operator import add
import functools
## split the grid
half_split = np.array_split(grid_matrix, 2)
res = map(lambda x: np.array_split(x, 2, axis=1), half_split)
## python 2
#splitted_grids = reduce(add, res)
## python 3
splitted_grids = functools.reduce(add, res)
## Multiprocessing, call score function on each
## part of the splitted grid
score = 0
inputs = []
## create inputs
for grid in splitted_grids:
inputs.append((corr_matrix, grid))
## run process
try:
res = Pool().amap(compute_matrix_score, inputs)
results = res.get()
except:
## DEBUG : pool lib mihgt break memory on big grids
## trying to split the inputs to solve the problem
print("[WARNINGS] : Grid dcore computint : BREAKING MEMORY")
print(
"[WARNINGS] : Attempt to reduce charge on CPU : splitting multiprocessing"
)
inputs_1 = inputs[0:int(len(inputs) / 2)]
inputs_2 = inputs[int(len(inputs) / 2):]
res_1 = Pool().amap(compute_matrix_score, inputs_1)
results_1 = res.get()
res_2 = Pool().amap(compute_matrix_score, inputs_2)
results_2 = res.get()
results = results_1 + results_2
## compute results
for scalar in results:
score += scalar
return score