Python 使嵌套for循环的cpu使用率达到最大的最简单方法是什么?
我有一些代码,可以生成元素的独特组合。有6种类型,每种大约有100种。所以有100^6个组合。必须计算每个组合,检查其相关性,然后丢弃或保存 代码的相关部分如下所示:Python 使嵌套for循环的cpu使用率达到最大的最简单方法是什么?,python,multithreading,optimization,multiprocessing,nested-loops,Python,Multithreading,Optimization,Multiprocessing,Nested Loops,我有一些代码,可以生成元素的独特组合。有6种类型,每种大约有100种。所以有100^6个组合。必须计算每个组合,检查其相关性,然后丢弃或保存 代码的相关部分如下所示: def modconffactory(): for transmitter in totaltransmitterdict.values(): for reciever in totalrecieverdict.values(): for processor in totalprocessordict.va
def modconffactory():
for transmitter in totaltransmitterdict.values():
for reciever in totalrecieverdict.values():
for processor in totalprocessordict.values():
for holoarray in totalholoarraydict.values():
for databus in totaldatabusdict.values():
for multiplexer in totalmultiplexerdict.values():
newconfiguration = [transmitter, reciever, processor, holoarray, databus, multiplexer]
data_I_need = dosomethingwith(newconfiguration)
saveforlateruse_if_useful(data_I_need)
现在这需要很长时间,这很好,但现在我意识到这个过程(进行配置,然后计算以供以后使用)一次只使用8个处理器内核中的1个
我一直在阅读有关多线程和多处理的内容,但我只看到了不同进程的示例,而没有看到如何多线程处理一个进程。在我的代码中,我调用了两个函数:“dosomethingwith()”和“saveforlateruse_if_usable()”。我可以把它们分成单独的进程,让它们与for循环并发运行,对吗
但是for循环本身呢?我能加快这个过程吗?因为这就是时间消耗的地方。(您可以通过以下方式运行函数:
from multiprocessing import Pool
def f(x):
return x*x
if __name__ == '__main__':
p = Pool(5)
print(p.map(f, [1, 2, 3]))
我只看到不同进程的示例,而没有看到如何对一个进程执行多线程
Python中有多线程,但由于GIL(全局解释器锁)的存在,它的效率非常低。因此,如果您想要使用所有处理器内核,如果您想要并发,那么除了使用多个进程之外别无选择,这可以通过多处理
模块完成(好吧,你也可以使用另一种语言而不会出现这样的问题)
您的案例的多处理使用的近似示例:
import multiprocessing
WORKERS_NUMBER = 8
def modconffactoryProcess(generator, step, offset, conn):
"""
Function to be invoked by every worker process.
generator: iterable object, the very top one of all you are iterating over,
in your case, totalrecieverdict.values()
We are passing a whole iterable object to every worker, they all will iterate
over it. To ensure they will not waste time by doing the same things
concurrently, we will assume this: each worker will process only each stepTH
item, starting with offsetTH one. step must be equal to the WORKERS_NUMBER,
and offset must be a unique number for each worker, varying from 0 to
WORKERS_NUMBER - 1
conn: a multiprocessing.Connection object, allowing the worker to communicate
with the main process
"""
for i, transmitter in enumerate(generator):
if i % step == offset:
for reciever in totalrecieverdict.values():
for processor in totalprocessordict.values():
for holoarray in totalholoarraydict.values():
for databus in totaldatabusdict.values():
for multiplexer in totalmultiplexerdict.values():
newconfiguration = [transmitter, reciever, processor, holoarray, databus, multiplexer]
data_I_need = dosomethingwith(newconfiguration)
saveforlateruse_if_useful(data_I_need)
conn.send('done')
def modconffactory():
"""
Function to launch all the worker processes and wait until they all complete
their tasks
"""
processes = []
generator = totaltransmitterdict.values()
for i in range(WORKERS_NUMBER):
conn, childConn = multiprocessing.Pipe()
process = multiprocessing.Process(target=modconffactoryProcess, args=(generator, WORKERS_NUMBER, i, childConn))
process.start()
processes.append((process, conn))
# Here we have created, started and saved to a list all the worker processes
working = True
finishedProcessesNumber = 0
try:
while working:
for process, conn in processes:
if conn.poll(): # Check if any messages have arrived from a worker
message = conn.recv()
if message == 'done':
finishedProcessesNumber += 1
if finishedProcessesNumber == WORKERS_NUMBER:
working = False
except KeyboardInterrupt:
print('Aborted')
您可以根据您的需要调整WORKERS\u NUMBER
与多处理池相同:
import multiprocessing
WORKERS_NUMBER = 8
def modconffactoryProcess(transmitter):
for reciever in totalrecieverdict.values():
for processor in totalprocessordict.values():
for holoarray in totalholoarraydict.values():
for databus in totaldatabusdict.values():
for multiplexer in totalmultiplexerdict.values():
newconfiguration = [transmitter, reciever, processor, holoarray, databus, multiplexer]
data_I_need = dosomethingwith(newconfiguration)
saveforlateruse_if_useful(data_I_need)
def modconffactory():
pool = multiprocessing.Pool(WORKERS_NUMBER)
pool.map(modconffactoryProcess, totaltransmitterdict.values())
您可能希望使用.map\u async
而不是.map
这两个代码段的作用相同,但我想说的是,在第一个代码段中,您对程序有更多的控制
不过,我想第二个是最简单的:)
但是第一个应该让你知道第二个发生了什么
multiprocessing
docs:您可以在这里找到您的解决方案:我不知道如何将for循环放入管道中,就像您所指的anwser中所做的那样。我已经读过了,但我不知道这对我有什么帮助?您能解释一下吗?请阅读multiprocessing docs(python的标准库)或者库joblib的文档。由于第一个循环的大小为100,您有8个循环,您是否对代码进行了分析,以查看它在哪里花费了大部分时间?我认为它应该位于dosomethingwith(新配置)中
调用。如果是这种情况,您可以将其作为单独的进程运行,并让这些进程将其结果放入与主进程共享的队列中。