We would like to perform our tasks and make full use of multiple CPU cores in modern hardware. It is a lock in the sense that it uses synchronization to ensure that only one thread of execution can execute instructions at a time within a Python process. The Threading and Multiprocessing modules are also quite different, let’s review some of the most important differences. Concurrency primitives are mechanisms for synchronizing and coordinating threads and processes.

• Firstly, let’s see how threading compares against multiprocessing for the code sample I showed you above.
• Although this data serialization is performed automatically under the covers, it adds a computational expense to the task.
• Process method is similar to the multithreading method above, where each process is tagged to a function with its arguments.

We moved on to explaining how Python threading works, followed by how multiprocessing works. Finally, we discussed in which all ways they are different, i.e., Python Threading vs. Multiprocessing. Starting a thread is considered to be faster than starting a process. This example demonstrates how you can create a process in Python. It has the ability to use processes but not threads to carry out the functionalities of threading API. Step 1 involves reading data from disk, so clearly disk IO is going to be the bottleneck for this step.

Threads have a very low overhead compared to processes or mutexes (they don’t need anything but a memory). Fortunately, sklearn has already implemented multiprocessing into this algorithm and we won’t have to write it from scratch. As you can see in the code below, we just have to provide a parameter n_jobs—the python multiprocessing vs threading number of processes it should use—to enable multiprocessing. I have created a classification dataset using the helper function sklearn.datasets.make_classification, then trained a RandomForestClassifier on that. Also, I’m timing the part of the code that does the core work of fitting the model.

Use Threading for for IO-Bound Tasks

CPython’s interpreters are intended to be strictly isolated from each
other. That means interpreters never share objects (except in very
specific cases with immortal, immutable builtin objects). Each
interpreter has its own modules (sys.modules), classes, functions,
and variables. Even where two interpreters define the same class,
each will have its own copy. When a Python process starts, it creates a single interpreter state
(the “main” interpreter) with a single thread state for the current
OS thread. A Python process will have one or more OS threads running Python code
(or otherwise interacting with the C API).

The rest of this answer provides some explanations as to why using asyncio to run an Executor may be advantageous. Many of the answers suggest how to choose only 1 option, but why not be able to use all 3? In this answer I explain how you can use asyncio to manage combining all 3 forms of concurrency instead as well as easily swap between them later if need be. First of all, let’s look at the differences between a thread and a process at a general level. In particular, selenium threads are more than twice as slow, problably because of resource contention with 10 selenium processes spinning up at once.

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When performing IO-operations, we very likely will need to move data between worker processes back to the main process. This may be costly if there is a lot of data as the data must be pickled at one end and unpickled at the other end. Although this data serialization is performed automatically under the covers, it adds a computational expense to the task. The GIL is used within each Python process, but not across processes. This means that multiple child processes can execute at the same time and are not subject to the GIL. Typically sharing data between processes requires explicit mechanisms, such as the use of a multiprocessing.Pipe or a multiprocessing.Queue.

When performing tasks with IO, we may require hundreds or even thousands of concurrent workers (e.g. each managing a network connection), and this may not be feasible or possible with processes. Now that we are familiar with the types of tasks suited to the threading module, let’s look at the types of tasks suited to the multiprocessing module specifically. The figure below provides a helpful side-by-side comparison of the key differences between the threading and multiprocessing modules in Python.

What is Multiprocessing? How is it different from threading?

This allows the event loop to loop through downloading the different images as each one has new data available during the download. Multiple threads are subject to the global interpreter lock (GIL), whereas multiple child processes are not subject to the GIL. The multiprocessing API provides a suite of concurrency primitives for synchronizing and coordinating processes, as process-based counterparts to the threading concurrency primitives.

As we’ve discussed, threads are the best option for parallelizing this kind of operation. Similarly, step 3 is also an ideal candidate for the introduction of threading. The function is simply fetching a webpage and saving that to a local file, multiple times in a loop. Useless but straightforward and thus a good fit for demonstration.

Pitfalls of Parallel Computing

Central to the threading module is the threading.Thread class that provides a Python handle on a native thread (managed by the underlying operating system). Each program is a process and has at least one thread that executes instructions for that process. This module constructs higher-level threading interfaces on top of the lower level _thread module. You can use a print lock to ensure that only one thread can print at a time.

For CPU bound tasks and truly parallel execution, we can use the multiprocessing module. Other answers have focused more on the multithreading vs multiprocessing aspect, but in python Global Interpreter Lock (GIL) has to be taken into account. When more number (say k) of threads are created, generally they will not increase the performance by k times, as it will still be running as a single threaded application. GIL is a global lock which locks everything out and allows only single thread execution utilizing only a single core. The performance does increase in places where C extensions like numpy, Network, I/O are being used, where a lot of background work is done and GIL is released. If the CPU runs at maximum capacity, you may want to switch to multiprocessing.

If we want to wait for them to terminate and return, we have to call the join method, and that’s what we have done above. 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; 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. The download_link function had to be changed pretty significantly. Previously, we were relying on urllib to do the brunt of the work of reading the image for us.

Parallel Loops in Python

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. Imgur’s API requires HTTP requests to bear the Authorization header with the client ID.

But not every problem may be effectively split into [almost independent] pieces, so there may be a need in heavy interprocess communications. That’s why multiprocessing may not be preferred over threading in general. There was no significant performance difference between using threading vs multiprocessing. The performance between multithreading and multiprocessing are extremely similar and the exact performance details are likely to depend on your specific application. There can be multiple threads in a process, and they share the same memory space, i.e. the memory space of the parent process. This would mean the code to be executed as well as all the variables declared in the program would be shared by all threads.