multithreading

问题 Can someone please share the implementation of a background thread with a Looper that i can pass to the subscribeOn(AndroidScheduler.from(/ backgroundThreadWithLooper /)). I need this because i am trying to implement a DBService class that runs all of its operations in the background while still getting live objects updates. So when i apply an addChangeListener, an exception is thrown: java.lang.IllegalStateException: Your Realm is opened from a thread without a Looper. Async queries need a

问题 I'm trying to understand why Parallel.For is able to outperform a number of threads in the following scenario: consider a batch of jobs that can be processed in parallel. While processing these jobs, new work may be added, which then needs to be processed as well. The Parallel.For solution would look as follows: var jobs = new List<Job> { firstJob }; int startIdx = 0, endIdx = jobs.Count; while (startIdx < endIdx) { Parallel.For(startIdx, endIdx, i => WorkJob(jobs[i])); startIdx = endIdx;

问题 I'm trying to understand why Parallel.For is able to outperform a number of threads in the following scenario: consider a batch of jobs that can be processed in parallel. While processing these jobs, new work may be added, which then needs to be processed as well. The Parallel.For solution would look as follows: var jobs = new List<Job> { firstJob }; int startIdx = 0, endIdx = jobs.Count; while (startIdx < endIdx) { Parallel.For(startIdx, endIdx, i => WorkJob(jobs[i])); startIdx = endIdx;

问题 Is multi-thread memory access faster than single threaded memory access? Assume we are in C language. A simple example is as follows. If I have a gigantic array A and I want to copy A to array B with the same size as A . Is using multithreading to do memory copy faster than it with a single thread? How many threads are suitable to do this kind of memory operation? EDIT: Let me put the question more narrow. First of all, we do not consider the GPU case. The memory access optimization is very

问题 Python 3.x renamed the low-level module 'thread' to '_thread' -- I don't see why in the documentation. Does anyone know? 回答1: It looks like the thread module became obsolete in 3.x in favor of the threading module. See PEP 3108. 回答2: It's been quite a long time since the low-level thread module was informally deprecated, with all users heartily encouraged to use the higher-level threading module instead; now with the ability to introduce backwards incompatibilities in Python 3, we've made

问题 Python 3.x renamed the low-level module 'thread' to '_thread' -- I don't see why in the documentation. Does anyone know? 回答1: It looks like the thread module became obsolete in 3.x in favor of the threading module. See PEP 3108. 回答2: It's been quite a long time since the low-level thread module was informally deprecated, with all users heartily encouraged to use the higher-level threading module instead; now with the ability to introduce backwards incompatibilities in Python 3, we've made

问题 Python 3.x renamed the low-level module 'thread' to '_thread' -- I don't see why in the documentation. Does anyone know? 回答1: It looks like the thread module became obsolete in 3.x in favor of the threading module. See PEP 3108. 回答2: It's been quite a long time since the low-level thread module was informally deprecated, with all users heartily encouraged to use the higher-level threading module instead; now with the ability to introduce backwards incompatibilities in Python 3, we've made

问题 I'm currently implementing Tensorflow custom op(for custom data fetcher) using C++ in order to speed up my Tensorflow model. Since my Tensorflow model doesn't use GPU a lot, I believe I can achieve maximal performance using multiple worker threads concurrently. The problem is, even though I have enough workers, my program doesn't utilize all CPU. In my development machine, (4 physical core) it uses about 90% of user time, 4% of sys time with 4 worker threads and tf.ConfigProto(inter_op

问题 I'm currently implementing Tensorflow custom op(for custom data fetcher) using C++ in order to speed up my Tensorflow model. Since my Tensorflow model doesn't use GPU a lot, I believe I can achieve maximal performance using multiple worker threads concurrently. The problem is, even though I have enough workers, my program doesn't utilize all CPU. In my development machine, (4 physical core) it uses about 90% of user time, 4% of sys time with 4 worker threads and tf.ConfigProto(inter_op

问题 I have a script in Python 3 and I'm trying to make a GUI for it using tkinter. Here is a complete example of working code: #!/usr/bin/python # coding: utf-8 import pickle import openpyxl from tkinter import * import threading import queue class Worker(): def __init__(self): self.one_name_list = [] self.dic = {} self.root = Tk() self.root.title("GUI Python") self.root.geometry("820x350") self.thread_queue = queue.Queue() self.btn1 = Button(text="start counting", width = '20', height = '1',