尽管设置了workers参数，但Scipy的differential_evolution没有在多个核中运行

Question

我想使用differential_evolution参数在多个核中运行workers函数。

我遵循了文档的指示，并开发了以下脚本来检查函数的运行时间，无论是否使用了workers参数：

import time

if __name__ == '__main__':
    from scipy.optimize import differential_evolution, rosen
    bounds = [( 0, 2 ), ( 0, 2 ), ( 0, 2 ), ( 0, 2 ), ( 0, 2 )]

    print( 'Beginning optimisation without parallelisation' )
    start = time.time()
    result = differential_evolution( rosen, bounds,
                                     workers=1,
                                     popsize=200,
                                     maxiter=1000,
                                     tol=0.001,
                                     seed=1 )
    end = time.time()
    print( 'Optimisation finished after {} seconds'.format( round( end - start, 2 ) ) )

    print( '\nBeginning optimisation with workers parameter set to 10' )
    start = time.time()
    result = differential_evolution( rosen, bounds,
                                     updating='deferred',
                                     workers=10,
                                     popsize=200,
                                     maxiter=1000,
                                     tol=0.001,
                                     seed=1 )
    end = time.time()
    print( 'Optimisation finished after {} seconds'.format( round( end - start, 2 ) ) )

运行脚本后，以下是每个调用返回的运行时间：

Beginning optimisation without parallelisation
Optimisation finished after 59.0 seconds

Beginning optimisation with workers parameter set to 10
Optimisation finished after 60.43 seconds

正如您所看到的，将workers参数设置为10的执行速度甚至比第一个要慢。你能帮我做这个吗？为了在differential_evolution函数中实现并行化，还有什么需要做的吗？

我目前正在使用Python3.8，我的scipy版本是1.8.1。

Answer 1

@9769953是正确的。multiprocessing引入了一些开销:创建进程，将计算划分到子进程，等等。只有当计算是CPU密集型的，而rosen不需要这种开销，这种开销才能被克服。

如果你的目标函数可以向量化，那么向量化可以提供一个速度增益。如果您可以消除矢量化中的中间计算(这是整个计算中常见的)，则收益将更大：

import time
import numpy as np
from scipy.optimize import differential_evolution, rosen

bounds = [( 0, 2 ), ( 0, 2 ), ( 0, 2 ), ( 0, 2 ), ( 0, 2 )]


print( 'Beginning optimisation without parallelisation' )
start = time.time()
result = differential_evolution( rosen, bounds,
                                 workers=1,
                                 popsize=200,
                                 updating='deferred',
                                 seed=1 )
end = time.time()
print( f'Optimisation finished after {end - start} seconds')
   
print( '\nBeginning optimisation with vectorisation' )
start = time.time()
result = differential_evolution( rosen, bounds,
                                 updating='deferred',
                                 popsize=200,
                                 seed=1,
                                 vectorized=True
                               )
end = time.time()
print( f'Optimisation finished after {end-start} seconds')

给予：

Beginning optimisation without parallelisation
Optimisation finished after 46.94838500022888 seconds

Beginning optimisation with vectorisation
Optimisation finished after 40.34918713569641 seconds