Parallel how many threads

While this is much faster, it is worth mentioning that only one thread was executing at a time throughout this process due to the GIL. Therefore, this code is concurrent but not parallel. The reason it is still faster is because this is an IO bound task. The processor is hardly breaking a sweat while downloading these images, and the majority of the time is spent waiting for the network.

This is why Python multithreading can provide a large speed increase. The processor can switch between the threads whenever one of them is ready to do some work. Using the threading module in Python or any other interpreted language with a GIL can actually result in reduced performance.

If your code is performing a CPU bound task, such as decompressing gzip files, using the threading module will result in a slower execution time. For CPU bound tasks and truly parallel execution, we can use the multiprocessing module.

For example, IronPython, a Python implementation using the. You can find a list of working Python implementations here. The only changes we need to make are in the main function. To use multiple processes, we create a multiprocessing Pool. With the map method it provides, we will pass the list of URLs to the pool, which in turn will spawn eight new processes and use each one to download the images in parallel.

This is true parallelism, but it comes with a cost. The entire memory of the script is copied into each subprocess that is spawned. While the threading and multiprocessing modules are great for scripts that are running on your personal computer, what should you do if you want the work to be done on a different machine, or you need to scale up to more than the CPU on one machine can handle? A great use case for this is long-running back-end tasks for web applications. This will degrade the performance of your application for all of your users.

What would be great is to be able to run these jobs on another machine, or many other machines. A great Python library for this task is RQ , a very simple yet powerful library.

You first enqueue a function and its arguments using the library. This pickles the function call representation, which is then appended to a Redis list. Enqueueing the job is the first step, but will not do anything yet. We also need at least one worker to listen on that job queue. The first step is to install and run a Redis server on your computer, or have access to a running Redis server. After that, there are only a few small changes made to the existing code. We first create an instance of an RQ Queue and pass it an instance of a Redis server from the redis-py library.

The enqueue method takes a function as its first argument, then any other arguments or keyword arguments are passed along to that function when the job is actually executed. One last step we need to do is to start up some workers. RQ provides a handy script to run workers on the default queue. Just run rqworker in a terminal window and it will start a worker listening on the default queue. Please make sure your current working directory is the same as where the scripts reside in.

So, data produced by one thread is immediately available to all the other threads in the process. However, this sharing of data leads to a different set of challenges for the programmer. Care must be taken to synchronize threads to protect data from being modified by more than one thread at once, or from being read by some threads while being modified by another thread at the same time.

See Thread Synchronization for more information. Threads are the primary programming interface in multithreaded programming. Threads are visible only from within the process, where the threads share all process resources like address space, open files, and so on. Register state, including program counter PC and stack pointer. Threads share the process instructions and most of the process data.

For that reason, a change in shared data by one thread can be seen by the other threads in the process. When a thread needs to interact with other threads in the same process, the thread can do so without involving the operating environment. User-level threads are so named to distinguish them from kernel-level threads, which are the concern of systems programmers only. Because this book is for application programmers, kernel-level threads are not discussed. Threads executing with these policies are in the Solaris Real-Time RT scheduling class, normally requiring special privilege.

Improve this question. Add a comment. Active Oldest Votes. Welcome to ServerFault! Improve this answer. That is a very helpful explanation; thanks. I have 2 tasks: 1 I will be getting the size of a file on UNIX system and 2 writing something to disk. For both, my plan was to run multiple processes i. Am I clear? If so, do you think this approach is reasonable? But you are mistaken in thinking that threads happen within a single CPU.

A classic single-threaded process can only use one CPU at a time; a multithreaded process is more complex, but can execute on several CPUs in parallel, up to the number of CPUs it has. If a process is multithreaded, its threads will "naturally" share the memory space of the process, so passing data from one thread to another within a process will be easy.

Processes are more strictly separated from each other: they'll need the OS's Inter-Process Communication services to share memory. Let me try to repeat what you said: There is no need to run multiple processes and then, multiple threads within them.

How many threads can run in parallel? Asked 3 years, 1 month ago. Active 3 years, 1 month ago. Viewed 3k times.

Improve this question. Elena Elena 1 1 silver badge 11 11 bronze badges. ElliottFrisch, Sir I have seen that post. But am curious to know does it matter to know how many cores am having in processor to run multiple threads in parallel?

That is the number of threads that can run in total on the machine. Note that modern operating systems also preemptively time slice. Java threads are implemented as native threads.