Python Parallel Processing

3 Methods with Examples

What Is Parallel Processing in Python?

Parallel processing is a computational method that allows multiple tasks to be executed concurrently. In traditional serial computing, a single task is executed at a time. However, with parallel processing, multiple tasks are executed simultaneously, resulting in faster execution times.

Python parallel processing is a technique that allows Python code to be executed in parallel, which can significantly speed up the processing time of the code. Python's standard library includes several modules that support parallel processing, such as the threading and multiprocessing modules. Other modules, like the concurrent.futures module, offer higher-level interfaces for managing parallel tasks.

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Benefits of Parallel Processing in Python

Improved Performance and Efficiency

When tasks are executed sequentially, the program has to wait for one task to complete before moving on to the next. This can lead to a waste of valuable processing time, especially if some tasks are independent and don't need to wait for others to complete.

On the other hand, with parallel processing, tasks are divided and allocated to different processors. Each processor works on its assigned task simultaneously, thereby reducing the overall execution time. This is particularly beneficial in programs where tasks are independent and can be processed concurrently.

Handling Large Data Sets

Handling large data sets is a common requirement for many modern applications. These could range from data analysis and machine learning applications to big data processing and more. Sequential processing can be quite inefficient and time-consuming when dealing with such large volumes of data.

By leveraging the power of parallel processing, Python can handle large data sets more effectively. Tasks can be divided into smaller chunks, each of which can be processed concurrently. This results in faster data processing and reduced execution time. Additionally, parallel processing can also help in managing memory efficiently when dealing with large data sets.


Parallel processing in Python also leads to cost-effectiveness. With parallel processing, tasks are completed faster, requiring less computational resources. The faster processing time means that resources are freed up sooner for other tasks. This improved efficiency can translate into cost savings in the long run.

Furthermore, parallel processing can lead to better utilization of existing resources. By distributing the computational load across multiple processors, each processor is used to its maximum potential. This can reduce the need for additional resources, leading to cost savings.

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Parallel Processing Methods in Python

There are several ways to implement parallel processing in Python. In this section, we'll explore three of them: multi-threading, multiprocessing, and asynchronous programming.

Python Multi-Threading

Multi-threading is a form of parallelism that allows a program to perform multiple tasks concurrently. In Python, the threading module provides a way to create and manage threads. Each thread can run a specific function or method, and all threads run independently of each other.

However, due to the Global Interpreter Lock (GIL) in Python, multi-threading doesn't always lead to a performance boost. The GIL is a mechanism that prevents multiple native threads from executing Python bytecodes at once. This means that even on a multiprocessor system, Python threads won't run in true parallel. However, multi-threading can still be beneficial for IO-bound tasks, where the program spends most of its time waiting for input/output operations.

Python Multiprocessing

Multiprocessing is another form of parallelism that involves running multiple processes simultaneously. In Python, the multiprocessing module provides a way to create and manage processes. Unlike threads, each process runs in its own Python interpreter, which means that they can run in true parallel on a multiprocessor system.

The multiprocessing module also provides a way to share data between processes, although this can be more complex than sharing data between threads. However, multiprocessing can be a great way to boost performance for CPU-bound tasks, where the program spends most of its time performing computations.

Python Asynchronous Programming

Asynchronous programming is a form of concurrent programming that involves executing tasks in a non-blocking manner. In Python, the asyncio module provides a way to write asynchronous code. With asyncio, you can write code that performs IO-bound tasks without blocking the execution of the rest of the program.

Asynchronous programming can be a bit more complex than multi-threading or multiprocessing, as it requires a different way of thinking about the program's flow. However, it can be a powerful tool for writing efficient, high-performance code, particularly for IO-bound tasks.

Python Parallel Processing Examples

Python Multi-Threading Example

Multi-threading is a technique for decomposing tasks into sub-tasks that can be processed simultaneously. This is particularly useful in applications that require heavy I/O operations, such as web scraping or reading from a database.

Here's an example of how multi-threading works in Python:

import threading

def print_numbers():

   for i in range(10):

       print(f”  {i}”)

def print_letters():

   for letter in 'abcdefghij':

       print(f” {letter}”)

t1 = threading.Thread(target=print_numbers)t2 = threading.Thread(target=print_letters)



The output should look like this:

In this example, two threads t1 and t2 are created to execute the print_numbers and print_letters functions concurrently. The start() method initiates the threads, and the join() method ensures that the main program waits for the threads to complete before proceeding.

Despite its convenience, multi-threading in Python has its limitations. The Global Interpreter Lock (GIL) in Python restricts one thread to execute Python bytecodes at a time, even in a multi-core processor environment. Therefore, multi-threading in Python is not suitable for CPU-bound tasks. For such tasks, Python offers another method of parallel processing: multiprocessing.

Python Multiprocessing Example

Multiprocessing overcomes the limitations of multi-threading by creating multiple processes, each having its own Python interpreter and memory space. This makes multiprocessing ideal for CPU-bound tasks, as it allows for true parallel execution.

Here's an example of multiprocessing in Python:

from multiprocessing import Process

def print_numbers():

   for i in range(10):

       print(f”  {i}”)

def print_letters():

   for letter in 'abcdefghij':

       print(f”  {letter}”)

p1 = Process(target=print_numbers)p2 = Process(target=print_letters)



The output should look like this:

This example is similar to the multi-threading example. However, instead of creating threads, we create processes using the Process class. The start() and join() methods function the same way as they do in multi-threading.While multiprocessing eliminates the constraints of the GIL, it introduces a new set of challenges. Inter-process communication is more complex than inter-thread communication, and creating a process is generally more resource-intensive than creating a thread.

Python Asynchronous Programming Example

Unlike multi-threading and multiprocessing, which are concurrent execution models, asynchronous programming is a form of parallel execution model. This method is based on the concept of 'event loop' and 'coroutines', allowing the program to handle multiple I/O-bound tasks concurrently without the need for threads or processes.Here's an example of asynchronous programming in Python:

import asyncio

async def print_numbers():

   for i in range(10):

       print(f”  {i}”)

       await asyncio.sleep(1)

async def print_letters():

   for letter in 'abcdefghij':

       print(f”  {letter}”)

       await asyncio.sleep(1)

async def main():

   task1 = asyncio.create_task(print_numbers())

   task2 = asyncio.create_task(print_letters())

   await task1
   await task2

The output should look something like this:

In this example, two coroutines print_numbers and print_letters are created. The asyncio.create_task() function is used to schedule the execution of these coroutines. The await keyword is used to pause the execution of the coroutine until the awaited task is completed. The function is used to execute the main coroutine, which waits for task1 and task2 to complete.

Asynchronous programming is a powerful tool for improving the efficiency of I/O-bound tasks. However, it introduces a new level of complexity and requires a good understanding of the event loop and the async/await syntax. Therefore, it might not be the best choice for beginners or for applications that do not require a high level of concurrency.

Parallel Computing and Run:ai

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