Python多处理和共享变量

我不是python的专家,但我已经设法写下了一个多处理代码,它在我的PC中使用了我所有的cpus和内核.我的代码加载了一个非常大的数组,大约1.6 GB,我需要在每个进程中更新数组.幸运的是,更新包括在图像中添加一些人造恒星,每个过程都有一组不同的图像位置,可以添加人造恒星.

图像太大,每次调用一个进程时我都无法创建一个新图像.我的解决方案是在共享内存中创建一个变量,我节省了大量内存.由于某种原因,它适用于90％的图像,但有些区域是我的代码在我之前发送到流程的某些位置添加随机数.它与我创建共享变量的方式有关吗？在我的代码执行过程中,这些进程是否相互干扰？

奇怪的是,当使用单个cpu和单核时,图像是100％完美的,并且图像中没有添加随机数.你建议我在多个进程之间共享一个大型数组吗？这是我的代码的相关部分.请在定义变量im_data时读取行.

import warnings
warnings.filterwarnings("ignore")

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import sys,os
import subprocess
import numpy as np
import time
import cv2 as cv
import pyfits
from pyfits import getheader
import multiprocessing,Queue
import ctypes

class Worker(multiprocessing.Process):


def __init__(self,work_queue,result_queue):

    # base class initialization
    multiprocessing.Process.__init__(self)

    # job management stuff
    self.work_queue = work_queue
    self.result_queue = result_queue
    self.kill_received = False

def run(self):
    while not self.kill_received:

        # get a task
        try:
            i_range,psf_file = self.work_queue.get_Nowait()
        except Queue.Empty:
            break

        # the actual processing
        print "Adding artificial stars - index range=",i_range

        radius=16
        x_c,y_c=( (psf_size[1]-1)/2,(psf_size[2]-1)/2 )
        x,y=np.meshgrid(np.arange(psf_size[1])-x_c,np.arange(psf_size[2])-y_c)
        distance = np.sqrt(x**2 + y**2)

        for i in range(i_range[0],i_range[1]):
            psf_xy=np.zeros(psf_size[1:3],dtype=float)
            j=0
            for i_order in range(psf_order+1):
                j_order=0
                while (i_order+j_order < psf_order+1):
                    psf_xy += psf_data[j,:,:] * ((mock_y[i]-psf_offset[1])/psf_scale[1])**i_order * ((mock_x[i]-psf_offset[0])/psf_scale[0])**j_order
                    j_order+=1
                    j+=1


            psf_factor=10.**( (30.-mock_mag[i])/2.5)/np.sum(psf_xy)
            psf_xy *= psf_factor

            npsf_xy=cv.resize(psf_xy,(npsf_size[0],npsf_size[1]),interpolation=cv.INTER_lanczos4)
            npsf_factor=10.**( (30.-mock_mag[i])/2.5)/np.sum(npsf_xy)
            npsf_xy *= npsf_factor

            im_rangex=[max(mock_x[i]-npsf_size[1]/2,0),min(mock_x[i]-npsf_size[1]/2+npsf_size[1],im_size[1])]
            im_rangey=[max(mock_y[i]-npsf_size[0]/2,min(mock_y[i]-npsf_size[0]/2+npsf_size[0],im_size[0])]
            npsf_rangex=[max(-1*(mock_x[i]-npsf_size[1]/2),min(-1*(mock_x[i]-npsf_size[1]/2-im_size[1]),npsf_size[1])]
            npsf_rangey=[max(-1*(mock_y[i]-npsf_size[0]/2),min(-1*(mock_y[i]-npsf_size[0]/2-im_size[0]),npsf_size[0])]

            im_data[im_rangey[0]:im_rangey[1],im_rangex[0]:im_rangex[1]] = 10.


        self.result_queue.put(id)

if __name__ == "__main__":

  n_cpu=2
  n_core=6
  n_processes=n_cpu*n_core*1
  input_mock_file=sys.argv[1]

  print "Reading file ",im_file[i]
  hdu=pyfits.open(im_file[i])
  data=hdu[0].data
  im_size=data.shape

  im_data_base = multiprocessing.Array(ctypes.c_float,im_size[0]*im_size[1])
  im_data = np.ctypeslib.as_array(im_data_base.get_obj())
  im_data = im_data.reshape(im_size[0],im_size[1])
  im_data[:] = data
  data=0
  assert im_data.base.base is im_data_base.get_obj()

  # run
  # load up work queue
  tic=time.time()
  j_step=np.int(np.ceil( mock_n*1./n_processes ))
  j_range=range(0,mock_n,j_step)
  j_range.append(mock_n)


  work_queue = multiprocessing.Queue()
  for j in range(np.size(j_range)-1):
    if work_queue.full():
      print "Oh no! Queue is full after only %d iterations" % j
    work_queue.put( (j_range[j:j+2],psf_file[i]) )

  # create a queue to pass to workers to store the results
  result_queue = multiprocessing.Queue()

  # spawn workers
  for j in range(n_processes):
    worker = Worker(work_queue,result_queue)
    worker.start()

  # collect the results off the queue
  while not work_queue.empty():
    result_queue.get()

  print "Writing file ",mock_im_file[i]
  hdu[0].data=im_data
  hdu.writeto(mock_im_file[i])
  print "%f s for parallel computation." % (time.time() - tic)

最佳答案

我认为问题(正如你在你的问题中建议的那样)来自于你从多个线程编写相同数组的事实.

im_data_base = multiprocessing.Array(ctypes.c_float,im_size[0]*im_size[1])
im_data = np.ctypeslib.as_array(im_data_base.get_obj())
im_data = im_data.reshape(im_size[0],im_size[1])
im_data[:] = data

虽然我很确定你可以用“进程安全”方式写入im_data_base(python使用隐式锁来同步对数组的访问),但我不确定你是否可以以过程安全的方式写入im_data .

因此我会(尽管我不确定我会解决你的问题)建议你创建一个围绕im_data的显式锁

# disable python implicit lock,we are going to use our own
im_data_base = multiprocessing.Array(ctypes.c_float,im_size[0]*im_size[1],lock=False)
im_data = np.ctypeslib.as_array(im_data_base.get_obj())
im_data = im_data.reshape(im_size[0],im_size[1])
im_data[:] = data
# Create our own lock
im_data_lock = Lock()

然后在进程中,每次需要修改im_data时获取锁

self.im_data_lock.acquire()
im_data[im_rangey[0]:im_rangey[1],im_rangex[0]:im_rangex[1]] = 10
self.im_data_lock.release()

为了简洁起见,我省略了将锁传递给进程的构造函数并将其存储为成员字段(self.im_data_lock)的代码.您还应该将im_data数组传递给进程的构造函数,并将其存储为成员字段.

Python多处理和共享变量

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