threads | could find the code C11
kandi X-RAY | threads Summary
kandi X-RAY | threads Summary
Here you could find the code for "C++11 thread tutorial", for more informations visit the project webpage:. for the second part of this tutorial. In order to use the image_processing code you will need a picture in ppm format, you can easily find examples on the Internet, or you could use Gimp to save your own picture in ppm format.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of threads
threads Key Features
threads Examples and Code Snippets
Lines of Code : 25License : Permissive (MIT License)
Copypublic int countWaits() {
CyclicBarrier cyclicBarrier = new CyclicBarrier(count);
ExecutorService es = Executors.newFixedThreadPool(threadCount);
for (int i = 0; i < threadCount; i++) {
es.execute(() -> {
public static void testThreads() {
Thread thread = new Thread(new Runnable() {
@Override
public void run() {
LOG.info("inside run");
try {
Thread.sleep(10000);
public void start() {
CyclicBarrier cyclicBarrier = new CyclicBarrier(3, () -> {
// Task
System.out.println("All previous tasks are completed");
});
Thread t1 = new Thread(new Task(cyclicBarrier), " Community Discussions
Trending Discussions on threads
QUESTION
Giving a bit of context. I'm using c++17. I'm using pointer T* data because this will interop with cuda code. I'm trying write a parallel version (on CPU) of a histogram creator. The sequential version:
ANSWER
Answered 2021-Jun-16 at 00:46The issue you are having has nothing to do with templates. You cannot invoke std::async() on a member function without binding it to an instance. Wrapping the call in a lambda does the trick.
Here's an example:
QUESTION
I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results. The way I'm trying to merge the results is something like this:
...ANSWER
Answered 2021-Jun-15 at 01:58I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results.
Ok, but yours is an unnecessarily difficult approach. At each step of the merge process, you want half of your threads to wait for the other half to finish, and the most natural way for one thread to wait for another to finish is to use pthread_join(). If you wanted all of your threads to continue with more work after synchronizing then that would be different, but in this case, those that are not responsible for any more merges have nothing at all left to do.
This is what I've tried:
QUESTION
I have a generator object, that loads quite big amount of data and hogs the I/O of the system. The data is too big to fit into memory all at once, hence the use of generator. And I have a consumer that all of the CPU to process the data yielded by generator. It does not consume much of other resources. Is it possible to interleave these tasks using threads?
For example I'd guess it is possible to run the simplified code below in 11 seconds.
...ANSWER
Answered 2021-Jun-15 at 16:02Send your data to separate processes. I used concurrent.futures because I like the simple interface.
This runs in about 11 seconds on my computer.
QUESTION
I created a new Quarkus app using the following command:
...ANSWER
Answered 2021-Jun-15 at 15:18Please enable the quarkus-smallrye-jwt TRACE logging to see why the tokens are rejected.
And indeed, as you have also found out, https protocol needs to be enabled in the native image, which can be done, as you have shown :-), by adding --enable-url-protocols=https to the native profile's properties in pom.xml.
This PR will ensure adding it manually won't be required.
thanks
QUESTION
I am trying to run a simple parallel program on a SLURM cluster (4x raspberry Pi 3) but I have no success. I have been reading about it, but I just cannot get it to work. The problem is as follows:
I have a Python program named remove_duplicates_in_scraped_data.py. This program is executed on a single node (node=1xraspberry pi) and inside the program there is a multiprocessing loop section that looks something like:
...ANSWER
Answered 2021-Jun-15 at 06:17Pythons multiprocessing package is limited to shared memory parallelization. It spawns new processes that all have access to the main memory of a single machine.
You cannot simply scale out such a software onto multiple nodes. As the different machines do not have a shared memory that they can access.
To run your program on multiple nodes at once, you should have a look into MPI (Message Passing Interface). There is also a python package for that.
Depending on your task, it may also be suitable to run the program 4 times (so one job per node) and have it work on a subset of the data. It is often the simpler approach, but not always possible.
QUESTION
I am serving dash content inside a Flask app which uses blueprint for registering the routes. App setup:
- Dash is initialised with
route_pathname_prefix=/dashapp/
ANSWER
Answered 2021-Jun-15 at 10:22I was able to fix this by removing sub_filter directive from nginx conf and updating url_prefixes in flask app. The steps I took are posted on this dash forum
QUESTION
I need to push messages to external rabbitmq. My java configuration successfully declares queue to push, but every time I try to push, I have next exception:
...ANSWER
Answered 2021-Jun-15 at 07:19I'm struggling to understand how that code fits together, but this part strikes me as definitely wrong:
QUESTION
In this video, he shows how multithreading runs on physical(Intel or AMD) processor cores.
and
All these links basically say:
Python threads cannot take advantage of many physical cores. This is due to an internal implementation detail called the GIL (global interpreter lock) and if we want to utilize multiple physical cores of the CPU
we must use true parallel multiprocessing module
But when I ran this below code on my laptop
...ANSWER
Answered 2021-Jun-15 at 08:06https://docs.python.org/3/library/math.html
The math module consists mostly of thin wrappers around the platform C math library functions.
While python itself can only execute a single instruction at a time, a low level c function that is called by python does not have this limitation.
So it's not python that is using multiple cores but your system's well optimized math library that is wrapped by python's math module.
That basically answers both your questions.
Regarding the usefulness of multiprocessing: It is still useful for those cases, where you're trying to parallelize pure python code or code that does not call libraries that already use multiple cores.
However, it comes with inter process communication (IPC) overhead that may or may not be larger than the performance gain that you get from using multiple cores. Tuning IPC is therefore often crucial for multiprocessing in python.
QUESTION
i am working in jave, spring, mysql, hibernate environment
I have the following hql it gives me the correct out put
ANSWER
Answered 2021-Jun-15 at 07:06Instead of
QUESTION
Another branch was created on the upstream repo. Let's call it features/demo. Three branches now exist, Master, Develop and features/demo.
My forked repo only has Master and Develop. The forked repo is set as the origin and is my local cloned copy.
How do I pull the upstream branch into my local? Every time I try it wants to merge into Develop or Master because that's what any new branch I make is checked out from.
Edit:
...ANSWER
Answered 2021-Jun-14 at 21:54How do I pull the upstream branch into my local? Every time I try it wants to merge
That's the definition of pull as delivered (with factory-default options): fetch and merge.
You just want to fetch. At the factory default settings,
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install threads
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
