multiprocessing

问题 I'm trying to parallelize a for loop to speed-up my code, since the loop processing operations are all independent. Following online tutorials, it seems the standard multiprocessing library in Python is a good start, and I've got this working for basic examples. However, for my actual use case, I find that parallel processing (using a dual core machine) is actually a little (<5%) slower, when run on Windows. Running the same code on Linux, however, results in a parallel processing speed-up of

问题 I have to create and fill huge ( e.g. 96 Go, 72000 rows * 72000 columns) array with floats in each case that come from mathematical formulas. The array will be computed after. import itertools, operator, time, copy, os, sys import numpy from multiprocessing import Pool def f2(x): # more complex mathematical formulas that change according to values in *i* and *x* temp=[] for i in combine: temp.append(0.2*x[1]*i[1]/64.23) return temp def combinations_with_replacement_counts(n, r): #provide all

问题 I'm trying to run a django development server from within a Kivy application. This did work out quite well so far. Now i want to allow the user to continue working with the program while the server is running. My idea was to create a multiprocessing.Process for the httpd.serve_forever() to avoid a complete lock of the main program. Did work well. This is the code in my internal_django module: import multiprocessing import os import time from wsgiref.simple_server import make_server def django

问题 If I want to analyze a string using dozens of regular-expressions, could either the threading or multiprocessing module improve performance? In other words, would analyzing the string on multiple threads or processes be faster than: match = re.search(regex1, string) if match: afunction(match) else: match = re.search(regex2, string) if match: bfunction(match) else: match = re.search(regex3, string) if match: cfunction(match) ... No more than one regular expression would ever match, so that's

问题 If I want to analyze a string using dozens of regular-expressions, could either the threading or multiprocessing module improve performance? In other words, would analyzing the string on multiple threads or processes be faster than: match = re.search(regex1, string) if match: afunction(match) else: match = re.search(regex2, string) if match: bfunction(match) else: match = re.search(regex3, string) if match: cfunction(match) ... No more than one regular expression would ever match, so that's

问题 If I want to analyze a string using dozens of regular-expressions, could either the threading or multiprocessing module improve performance? In other words, would analyzing the string on multiple threads or processes be faster than: match = re.search(regex1, string) if match: afunction(match) else: match = re.search(regex2, string) if match: bfunction(match) else: match = re.search(regex3, string) if match: cfunction(match) ... No more than one regular expression would ever match, so that's

问题 Python guru I need your help. I faced quite strange behavior: empty python Process hangs on joining . Looks like it forks some locked resource. Env: Python version: 3.5.3 OS: Ubuntu 16.04.2 LTS Kernel: 4.4.0-75-generic Problem description: 1) I have a logger with thread to handle messages in background and queue for this thread. Logger source code (a little bit simplified). 2) And I have a simple script which uses my logger (just code to display my problem): import os from multiprocessing

问题 Python guru I need your help. I faced quite strange behavior: empty python Process hangs on joining . Looks like it forks some locked resource. Env: Python version: 3.5.3 OS: Ubuntu 16.04.2 LTS Kernel: 4.4.0-75-generic Problem description: 1) I have a logger with thread to handle messages in background and queue for this thread. Logger source code (a little bit simplified). 2) And I have a simple script which uses my logger (just code to display my problem): import os from multiprocessing

问题 I don't understand why Pipes are said unsafe when there are multiple senders and receivers. How the following code can be turned into code using Queues if this is the case ? Queues don't throw EOFError when closed, so my processes can't stop. Should I send endlessly 'Poison' messages to tell them to stop (this way, i'm sure all my processes receive at least one poison) ? I would like to keep the pipe p1 open until I decide otherwise (here it's when I have sent the 10 messages). from

问题 Being new to using concurrency, I am confused about when to use the different python concurrency libraries. To my understanding, multiprocessing, multithreading and asynchronous programming are part of concurrency, while multiprocessing is part of a subset of concurrency called parallelism. I searched around on the web about different ways to approach concurrency in python, and I came across the multiprocessing library, concurrenct.futures' ProcessPoolExecutor() and ThreadPoolExecutor(), and