我有一个LP列表，我想并行解决。
到目前为止，我已经尝试了multiprocessing和joblib。但两者都只使用1个CPU（8个）。
import subprocess
from multiprocessing import Pool, cpu_count
from scipy.optimize import linprog
import numpy as np
from joblib import Parallel, delayed
def is_in_convex_hull(arg):
A,v = arg
res = linprog(np.zeros(A.shape[1]),A_eq = A,b_eq = v)
return res['success']
def convex_hull_LP(A):
pool = Pool(processes = cpu_count())
res = pool.map(is_in_convex_hull,[(np.delete(A,i,axis=1),A[:,i]) for i in range(A.shape[1])])
pool.close()
pool.join()
return [i for i in range(A.shape[1]) if not res[i]]
现在我在IPyton中运行
A = np.random.randint(0,60,size = (40,300))
%time l1 = convex_hull_LP(A)
%time l2 = Parallel(n_jobs=8)(delayed(is_in_convex_hull)((np.delete(A,i,axis=1),A[:,i])) for i in range(A.shape[1]))
虽然显示了8个不同的进程ID，但它们都导致大约7秒，但只使用一个CPU。