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 multiprocessingimport numpy as npimport threadclass Sum: #again, this class is from ParallelPython's example pre (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 array1def 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
