并行化Python代码

Question

我已经编写了一个函数，它返回一个Pandas数据帧(作为行的样本和作为列的描述符)，并将输入作为肽的列表(作为字符串数据的生物序列)。"my_function( pep_list )“以pep_list作为参数，返回数据帧。它迭代来自pep_list的每个肽序列，计算描述符，并将所有数据组合为pandas数据帧并返回df：

pep_list = [DAAAAEF,DAAAREF,DAAANEF,DAAADEF,DAAACEF,DAAAEEF,DAAAQEF,DAAAGEF,DAAAHEF,DAAAIEF,DAAALEF,DAAAKEF]

示例：

我想用下面给出的算法并行化这段代码：

1. get the number of processor available as .
    n = multiprocessing.cpu_count()

2. split the pep_list  as 
     sub_list_of_pep_list = pep_list/n 

     sub_list_of_pep_list = [[DAAAAEF,DAAAREF,DAAANEF],[DAAADEF,DAAACEF,DAAAEEF],[DAAAQEF,DAAAGEF,DAAAHEF],[DAAAIEF,DAAALEF,DAAAKEF]]

4. run "my_function()" for each core as (example if 4 cores )

     df0 = my_function(sub_list_of_pep_list[0])
     df1 = my_function(sub_list_of_pep_list[1])
     df2 = my_functonn(sub_list_of_pep_list[2])
     df3 = my_functonn(sub_list_of_pep_list[4])

5. join all df = concat[df0,df1,df2,df3] 

6. returns df with nX speed. 

请给我推荐最合适的库来实现这个方法。

谢谢并致以问候。

Updated 

通过一些阅读，我能够写出一个代码，它按照我的期望工作，如1.没有并行的情况下，10肽序列2.两次处理12肽3. 4次处理12肽需要4秒

from multiprocessing import Process

def func1():
    structure_gen(pep_seq = ["DAAAAEF","DAAAREF","DAAANEF"])

def func2():
    structure_gen(pep_seq = ["DAAAQEF","DAAAGEF","DAAAHEF"])


def func3():
    structure_gen(pep_seq = ["DAAADEF","DAAALEF"])

def func4():
    structure_gen(pep_seq = ["DAAAIEF","DAAALEF"])

if __name__ == '__main__':
  p1 = Process(target=func1)
  p1.start()
  p2 = Process(target=func2)
  p2.start()
  p3 = Process(target=func1)
  p3.start()
  p4 = Process(target=func2)
  p4.start()
  p1.join()
  p2.join()
  p3.join()
  p4.join()

但是这个代码很容易与10肽一起工作，但是不能实现包含100万肽的PEP_list

谢谢

Answer 1

multiprocessing.Pool.map就是你要找的东西。

试试这个：

from multiprocessing import Pool

# I recommend using more partitions than processes,
# this way the work can be balanced.
# Of course this only makes sense if pep_list is bigger than
# the one you provide. If not, change this to 8 or so.
n = 50

# create indices for the partitions
ix = np.linspace(0, len(pep_list), n+1, endpoint=True, dtype=int)

# create partitions using the indices
sub_lists = [pep_list[i1:i2] for i1, i2 in zip(ix[:-1], ix[1:])]

p = Pool()
try:
    # p.map will return a list of dataframes which are to be
    # concatenated
    df = concat(p.map(my_function, sub_lists))
finally:
    p.close()

该池将自动包含与可用核心数一样多的进程。但是如果你愿意，你可以改写这个数字，看看文档就知道了。