multiprocessing

问题 I worte a multiprocessing program in python. I use multiprocessing.Manager().list() to share list within subprocess. At first, I add some tasks in main process. And then, start some subprocesses to do tasks which in the shared list, the subprocesses also add tasks to the shared list. But I got a exception as follow: Traceback (most recent call last): File "/usr/lib64/python2.6/multiprocessing/process.py", line 232, in _bootstrap self.run() File "/usr/lib64/python2.6/multiprocessing/process.py

问题 In PHP we normally do coding without considering what the server is capable of. Now a days even PCs have multiple cores and process 64 bit data also. As far as I know the PHP engine itself is optimised to take advantage of multiple cores. How can we programmers can optimize further the code to take advantage of multiple cores. In other words I want to know the techniques that will teach me to write code which will be more likely to be considered by php engine to process parallely. I'm not

问题 I want to use multiprocessing on a large dataset to find the distance between two gps points. I constructed a test set, but I have been unable to get multiprocessing to work on this set. import pandas as pd from geopy.distance import vincenty from itertools import combinations import multiprocessing as mp df = pd.DataFrame({'ser_no': [1, 2, 3, 4, 5, 6, 7, 8, 9, 0], 'co_nm': ['aa', 'aa', 'aa', 'bb', 'bb', 'bb', 'bb', 'cc', 'cc', 'cc'], 'lat': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'lon': [21, 22, 23

问题 I was learning about multi-processing and multi-threading. From what I understand, threads run on the same core, so I was wondering if I create multiple processes inside a child thread will they be limited to that single core too? I'm using python, so this is a question about that specific language but I would like to know if it is the same thing with other languages? 回答1: I'm not a pyhton expert but I expect this is like in other languages, because it's an OS feature in general. Process A

问题 I am trying to use multiprocessing to read 100 csv files in parallel (and subsequently process them separately in parallel). Here is my code running in Jupyter hosted on my EMR master node in AWS. (Eventually it will be 100k csv files hence the need for distributed reading). import findspark import boto3 from multiprocessing.pool import ThreadPool import logging import sys findspark.init() from pyspark import SparkContext, SparkConf, sql conf = SparkConf().setMaster("local[*]") conf.set(

问题 I am learning to use HTML5 WebSockets and as part of that I am writing a server in Python so I can know the nitty gritty of how they work. I created one the other day that worked pretty well, but I wanted to expand it so it would support multiple endpoints with each endpoint being a different "service" which can handle websocket clients. At the moment, my implementation works with spawning processes and such (I am using multiprocessing instead of threading since I read that threading isn't

问题 For now I have 2 spiders, what I would like to do is Spider 1 goes to url1 and if url2 appears, call spider 2 with url2 . Also saves the content of url1 by using pipeline. Spider 2 goes to url2 and do something. Due to the complexities of both spiders I would like to have them separated. What I have tried using scrapy crawl : def parse(self, response): p = multiprocessing.Process( target=self.testfunc()) p.join() p.start() def testfunc(self): settings = get_project_settings() crawler =

问题 I'm trying to learn about multiprocessing in python (2.7). My CPU has 4 cores. In the following code I test speed of parallel Vs serial execution of the same basic instruction. I find that the time taken using the 4 cores is only 0.67 the one taken by only one core, while naively I'd expect ~0.25. Is overhead the reason? where does it come from? Are not the 4 processes independent? I also tried pool.map and pool.map_async , with very similar results in terms of speed. from multiprocessing

问题 I am populating a queue with a set of jobs that I want to run in parallel and using python's multiprocessing module for doing that. Code snippet below: import multiprocessing from multiprocessing import Queue queue = Queue() jobs = [['a', 'b'], ['c', 'd']] for job in jobs: queue.put(job) When I do queue.get() I get the following: ['a', 'b'] Why is the queue not getting populated with all the jobs? 回答1: The queue is actually geting populated. You need to call queue.get() for each time you put

问题 I am using Redis as my queue for a producer/consumer relationship in a multiprocessing setup. My problem is that my producers are overloading my consumer then stealing it's CPU. My question, can I allocate an entire processor to specific function/process (IE: the consumer) in this setup. 回答1: It's not something Python does out of the box. Is also somewhat OS-specific. See this answer on doing under Linux: https://stackoverflow.com/a/9079117/4822566 回答2: I just had the similar problem on the