如何在Python中使用多线程并加速代码

我正在尝试在 python 3 中使用多线程。 加快某些代码的执行速度。

基本上我必须在可迭代上运行相同的函数

channels=range(1,8)

我已经制作了一个到目前为止我正在使用的工作示例。 我正在针对正常执行对其进行测试

我没有看到任何显着差异。 也许我做错了什么。

如果有一点帮助，我们将不胜感激

#!/usr/bin/env python


from threading import Thread

import matplotlib.pyplot as plt
import pdb
# from multiprocessing.dummy import Pool as ThreadPool
from multiprocessing.pool import ThreadPool
import threading
import argparse
import logging
from types import SimpleNamespace
import numpy as np
import time
import inspect
import logging

logger = logging.getLogger(__name__)

myself = lambda: inspect.stack()[1][3]
logger = logging.getLogger(__name__)
pool = ThreadPool(processes=8)

class ThreadWithReturnValue(Thread):
    def __init__(self, group=None, target=None, name=None,
                 args=(), kwargs={}, Verbose=None):
        Thread.__init__(self, group, target, name, args, kwargs)
        self._return = None
    def run(self):
        print(type(self._target))
        if self._target is not None:
            self._return = self._target(*self._args,
                                                **self._kwargs)
    def join(self, *args):
        Thread.join(self, *args)
        return self._return




#--------
def map_kg1_efit(data,chan):


    density = np.zeros(968)


    for it in range(0,data.ntefit):
        density[it] = it
        for jj in range(0,data.ntkg1v):
            density[it]=density[it]+jj

    data.KG1LH_data.lid[chan] = density

# ----------------------------

def main():
    data = SimpleNamespace()
    data.KG1LH_data = SimpleNamespace()
    data.ntkg1v = 30039
    data.ntefit = 968

    data.KG1LH_data.lid = [ [],[],[],[],[],[],[],[]]

    channels=range(1,8)



    # chan =1
    for chan in channels:
        logger.info('computing channel {}'.format(chan))
        start_time = time.time()
        twrv = ThreadWithReturnValue(target=map_kg1_efit, args=(data,chan))
        # pdb.set_trace()
        twrv.start()
        twrv.join()
        logger.info("--- {}s seconds ---".format((time.time() - start_time)))
        plt.figure()
        plt.plot(range(0,data.ntefit), data.KG1LH_data.lid[chan])
        plt.show()




        logger.info('computing channel {}'.format(chan))
        start_time = time.time()
        map_kg1_efit(data,chan)
        logger.info("--- {}s seconds ---".format((time.time() - start_time)))

        plt.figure()
        plt.plot(range(0,data.ntefit), data.KG1LH_data.lid[chan])
        plt.show()



    logger.info("\n             Finished.\n")

if __name__ == "__main__":
    debug_map = {0: logging.ERROR,
                 1: logging.WARNING,
                 2: logging.INFO,
                 3: logging.DEBUG,
                 4: 5}

    logging.basicConfig(level=debug_map[2])

    logging.addLevelName(5, "DEBUG_PLUS")

    logger = logging.getLogger(__name__)



    # Call the main code
    main()

对于这个 CPU 密集型任务，您可以使用multiprocessing.pool.Pool以获得并行性。这是一个简化的示例，它使我的系统上的所有四个核心都饱和：

import matplotlib.pyplot as plt          
from multiprocessing.pool import Pool    
from types import SimpleNamespace        
import numpy as np                       

def map_kg1_efit(arg):             
    data = arg[0]               
    chan = arg[1]    
    density = np.zeros(968)    
    for it in range(0,data.ntefit):    
        density[it] = it                   
        for jj in range(0,data.ntkg1v):    
            density[it]=density[it]+jj     
    data.KG1LH_data.lid[chan] = density                                     
    return (data, chan)    

if __name__ == "__main__":    
    data = SimpleNamespace()    
    data.KG1LH_data = SimpleNamespace()    
    data.ntkg1v = 30039    
    data.ntefit = 968      
    data.KG1LH_data.lid = [ [],[],[],[],[],[],[],[]]    
    with Pool(4) as pool:    
        results = pool.map(map_kg1_efit, [(data, chan) for chan in range(1, 8)])    
    for r in results:    
        plt.figure()     
        plt.plot(range(0,r[0].ntefit), r[0].KG1LH_data.lid[r[1]])    
    plt.show()