我想出了如何使用多处理，apply_async和回调将数组的总和并行化，所以我将其发布在这里供其他人使用。我使用的示例页面并行的Python的总和回调类，虽然我没有真正使用该程序包实施。不过，它给了我使用回调的想法。这是我最终使用的简化代码，它可以完成我想要的操作。
import multiprocessing
import numpy as np
import thread
class Sum: #again, this class is from ParallelPython's example code (I modified for an array and added comments)
def __init__(self):
self.value = np.zeros((1,512*512)) #this is the initialization of the sum
self.lock = thread.allocate_lock()
self.count = 0
def add(self,value):
self.count += 1
self.lock.acquire() #lock so sum is correct if two processes return at same time
self.value += value #the actual summation
self.lock.release()
def computation(index):
array1 = np.ones((1,512*512))*index #this is where the array-returning computation goes
return array1
def summers(num_iters):
pool = multiprocessing.Pool(processes=8)
sumArr = Sum() #create an instance of callback class and zero the sum
for index in range(num_iters):
singlepoolresult = pool.apply_async(computation,(index,),callback=sumArr.add)
pool.close()
pool.join() #waits for all the processes to finish
return sumArr.value
我还可以使用并行映射来完成此工作，这在另一个答案中建议。我已经尝试过了，但是没有正确实现。两种方法都有效，我认为这个答案很好地说明了使用哪种方法（映射或apply.async）的问题。对于地图版本，您无需定义Sum类，summers函数将变为
def summers(num_iters):
pool = multiprocessing.Pool(processes=8)
outputArr = np.zeros((num_iters,1,512*512)) #you wouldn't have to initialize these
sumArr = np.zeros((1,512*512)) #but I do to make sure I have the memory
outputArr = np.array(pool.map(computation, range(num_iters)))
sumArr = outputArr.sum(0)
pool.close() #not sure if this is still needed since map waits for all iterations
return sumArr
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