Question

我正在尝试在python 3中使用多线程。加快代码执行速度。

基本上，我必须在迭代器上运行相同的功能

channels=range(1,8)

到目前为止，我已经为我所用的东西做了一个实际的例子。我正在针对正常执行情况对其进行测试

我看不出任何重大差异。也许我做错了。

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()

Answer 1

对于此CPU密集型任务，您可以使用multiprocessing.pool.Pool获得并行性。这是一个简化的示例，它使系统中的所有四个内核都饱和：

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()

如何在python中使用多线程并加速代码

1 个答案: