Software development

Parallel Programming With Python’s Multiprocessing Library

How you do this will depend a lot on the tools you are already using and the way your model trains. If you are iterating e.g. over a grid of parameters to search, it can be trivial to parallelize. The above command will be executed individually by each engine. Using the get method you can get the result in the form of an AsyncResult object.

Parallel Computing And Multiprocessing In Python

I need the return value of row id to work further on the results. This is almost the same as the previous one, with the exception that we now have a new class, DownloadWorker, which is a descendent of the Python Thread class. The run method has been overridden, which runs an infinite loop.

Then these two results will merge together into one result. How can such calculations be organized in several lines of code? Multiprocessing is the process of using two or more central processing units in one physical computer. There are many options for multiprocessing; for example, several cores on one chip, several crystals in one package, several packages in one system unit, etc. A multiprocessor system is a computer device that has two or more processor units , each of which shares the main memory and peripheral devices for simultaneous processing of programs. It is possible to use apply_async() without providing a callback function. Only that, if you don’t provide a callback, then you get a list of pool.ApplyResult objects which contains the computed output values from each process.

Modes Of Parallelism

You’re probably reading this article in a browser, which probably has multiple tabs open. You might also be listening to music through the Spotify desktop app at the same time. The browser and the Spotify application are different processes; each of them can use multiple processes or Computer science threads to achieve parallelism. Different tabs in your browser might be run in different threads. Spotify can play music in one thread, download music from the internet in another, and use a third to display the GUI. The same can be done with multiprocessing—multiple processes—too.

This is a commercial product but the source code is distributed free for academic use. One thing to be aware of is that you really must use multiple processes to get real parallelism in Python. Using a thread in python does not speed things up for the actual computation. Threads are typically only useful in scenarios where you think you will be waiting for responses e.g. in networking/web programming . Using the standard multiprocessing module, we can efficiently parallelize simple tasks by creating child processes.

In fact, most modern browsers like Chrome and Firefox use multiprocessing, not multithreading, to handle multiple tabs. If you haven’t read it yet, I suggest you take a look at Eqbal Quran’s article on concurrency and parallelism in Ruby here on the Toptal Engineering Blog. Multi-processing is one way to execute tasks in parallel on a multi-core CPU, or across multiple computers in a computing cluster. In multi-processing, each task runs in its own process; each program running on your computer is represented by one or more processes.

  • However, if you really do need to use some shared data thenmultiprocessing provides a couple of ways of doing so.
  • There a lot more approaches you can use for parallel processing.
  • Also, I’m timing the part of the code that does the core work of fitting the model.
  • The thread objects have a start method that starts the thread asynchronously.

This recipeprovides a thread Pool() interface with the same API as that found for processes which might also be of interest. There is also the ThreadPo olmodule which is quite similar. Shared MemoryIn shared memory, the sub-units can communicate with each other through the same memory space.

Trove Classifiers

Worker processes within a Pool typically live for the complete duration of the Pool’s work queue. The maxtasksperchildargument to the Pool exposes this ability to the end user. ¶Create a shared threading.Semaphore object and return a proxy for it. ¶Create a shared threading.BoundedSemaphore object and return a proxy for it.

Parallel Computing And Multiprocessing In Python

Even so it is probably good practice to explicitly join all the processes that you start. Waterfall model Listener objects now support the context management protocol – seeContext Manager Types.

Use Parallel Primitives¶

When multiprocessing is initialized the main process is assigned a random string using os.urandom(). Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. Additionally, these are notUnix daemons or services, they are normal processes that will be terminated if non-daemonic processes have exited. It arranges for the object’s run() method to be invoked in a separate process. Note that the methods of a pool should only ever be used by the process which created it.

Parallel Computing And Multiprocessing In Python

Each connection object has two methods one is send() and another one is recv() method. When we work with Multiprocessing,at first we create process object. In this post, I tried to mention some simple examples of parallel processing implementation in R and python. Software Engineering Body of Knowledge There a lot more approaches you can use for parallel processing. There is no absolute way to do this but I think the abovementioned approaches are easy to implement. We should define and register the number of cores to be used in the process same as python.

Use Cases For Threading

Usually message passing between processes is done using queues or by usingConnection objects returned byPipe(). Will raise multiprocessing.TimeoutError if the result cannot be returned within timeout seconds. One can create a pool of processes which will carry out tasks submitted to it with the Pool class. ¶Create a shared queue.Queue object and return a proxy for it.

Parallel Computing And Multiprocessing In Python

There are certain guidelines and idioms which should be adhered to when usingmultiprocessing. A connection or socket object is ready when there is data available to be read from it, or the other end has been closed. If the listener object uses a socket then backlog is passed to the listen() method of the socket once it has been bound. Address is the address to be used by the bound socket or named pipe of the listener object.


Threads are lighter than processes, and share the same memory space. Thus, it is very well evident that by deploying a suitable method from the multiprocessing library, we can achieve a significant reduction in computation time.

¶Return an object sent from the other end of the connection usingsend(). ¶Send an object to the other end of the connection which should be read using recv(). ValueError is raised if the specified start method is not available. Calling this has the side effect of “joining” any processes which have already finished. ¶Block until all items in the queue have been gotten and processed. If a process is killed using Process.terminate() or os.kill()while it is trying to use a Queue, then the data in the queue is likely to become corrupted.

Run Python Code In Parallel Using Multiprocessing

Despite the fundamental difference between them, the two libraries offer a very similar API (as of Python 3.7). Multiprocessing outshines threading in cases where the program is CPU intensive and doesn’t have to do any IO or user interaction. For example, any program that just crunches numbers will see a massive speedup from multiprocessing; parallel computing and multiprocessing in python in fact, threading will probably slow it down. An interesting real world example is Pytorch Dataloader, which uses multiple subprocesses to load the data into GPU. This deep dive on Python parallelization libraries – multiprocessing and threading – will explain which to use when for different data scientist problem sets.


Simply add the following code directly below the serial code for comparison. A gist with the full Python script is included at the end of this article for clarity. For the sake of brevity, this article is going to focus solely on asynchronous parallelization because that is the method that will likely boost performance the most. Also, if you structure code for asynchronous parallelization on your laptop, it is much easier to scale up to a super computer. Another common MapReduce use case involving a word-document index is presented in the README for the mr4mp library. Consider for example a webserver that is waiting for incoming network connections.

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