将串行任务转换为并行映射输入和输出

Question

我有成千上万的模拟运行在具有多个内核的系统上。目前，它是以串行方式完成的，我知道我的输入参数，并将结果存储在字典中。

串行版本

import time 
import random 

class MyModel(object): 
    input = None 
    output = None 

    def run(self): 
     time.sleep(random.random()) # simulate a complex task 
     self.output = self.input * 10 


# Run serial tasks and store results for each parameter 

parameters = range(10) 
results = {} 

for p in parameters: 
    m = MyModel() 
    m.input = p 
    m.run() 
    results[p] = m.output 

print('results: ' + str(results)) 

这需要< 10秒，并显示正确的结果：

我尝试并行这个过程是基于在该示例

results: {0: 0, 1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60, 7: 70, 8: 80, 9: 90} 

并行版本multiprocessing模块附近的文字"An example showing how to use queues to feed tasks to a collection of worker processes and collect the results"（抱歉，没有可用的URL锚点）。

以下基础上的串行版本的上半部分：

from multiprocessing import Process, Queue 
NUMBER_OF_PROCESSES = 4 

def worker(input, output): 
    for args in iter(input.get, 'STOP'): 
     m = MyModel() 
     m.input = args[0] 
     m.run() 
     output.put(m.output) 


# Run parallel tasks and store results for each parameter 

parameters = range(10) 
results = {} 

# Create queues 
task_queue = Queue() 
done_queue = Queue() 

# Submit tasks 
tasks = [(t,) for t in parameters] 
for task in tasks: 
    task_queue.put(task) 

# Start worker processes 
for i in range(NUMBER_OF_PROCESSES): 
    Process(target=worker, args=(task_queue, done_queue)).start() 

# Get unordered results 
for i in range(len(tasks)): 
    results[i] = done_queue.get() 

# Tell child processes to stop 
for i in range(NUMBER_OF_PROCESSES): 
    task_queue.put('STOP') 

print('results: ' + str(results)) 

现在只需要几秒钟，但投入和结果之间的映射命令混淆在一起。

results: {0: 10, 1: 0, 2: 60, 3: 40, 4: 20, 5: 80, 6: 30, 7: 90, 8: 70, 9: 50} 

我意识到，我基于一个无序done_queue.get()填充results，但我不知道怎么去正确的映射到task_queue。有任何想法吗？任何其他方式来使这种方式更清洁？

Answer 1

A-ha！工作人员需要嵌入某种ID，例如用于返回到输出队列的输入参数，该输入参数可用于识别返回的进程。这里有必要的修改：

def worker(input, output): 
    for args in iter(input.get, 'STOP'): 
     m = MyModel() 
     m.input = args[0] 
     m.run() 
     # Return a tuple of an ID (the input parameter), and the model output 
     return_obj = (m.input, m.output) 
     output.put(return_obj) 

和

# Get unordered results 
for i in range(len(tasks)): 
    # Unravel output tuple, which has the input parameter 'p' used as an ID 
    p, result = done_queue.get() 
    results[p] = result