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Concurrency and Parallel Execution in Python


Concurrency is a key component of computer programming that helps enhance applications’ speed and responsiveness. Multithreading is a potent method for creating concurrency in Python. Multiple threads can run concurrently within a single process using multithreading, enabling parallel execution and effective use of system resources. We shall delve further into Python multithreading in this tutorial. We shall look at its ideas, benefits, and difficulties. We’ll learn how to establish and control threads, share data among them, and guarantee thread safety.

We will also go through typical traps to avoid and the recommended practices for developing and implementing multithreaded programmes. Understanding multithreading is an asset whether you are developing applications that include network activities, I/O-bound tasks, or are just trying to make your programme more responsive. You can unleash the potential for enhanced performance and a seamless user experience by making the most of concurrent execution. Join us on this voyage as we delve into the depths of Python’s multithreading and discover how to harness its potential to create concurrent and effective applications.

Learning Objectives

Some of the learning objectives from the topic are as follows:

1. Learn the fundamentals of multithreading, including what threads are, how they work within a single process, and how they achieve concurrency. Understand the benefits and limitations of multithreading in Python, including the impact of the Global Interpreter Lock (GIL) on CPU-bound tasks.

2. Explore thread synchronization techniques like locks, semaphores, and condition variables to manage shared resources and avoid race conditions. Learn how to ensure thread safety and design concurrent programs that handle shared data efficiently and securely.

3. Gain hands-on experience creating and managing threads using Python’s threading module. Learn how to start, join, and terminate threads, and explore common patterns for multithreading, such as thread pools and producer-consumer models.

This article was published as a part of the Data Science Blogathon.

Concurrency – Basics

A key idea in computer science is concurrency, which refers to the execution of several tasks or processes simultaneously. It enables programmes to work on several tasks simultaneously, enhancing responsiveness and overall performance. Concurrency is crucial for improving programme performance because it allows programmes to effectively utilize system resources like CPU cores, I/O devices, and network connections. A programme can efficiently use these resources and decrease idle time by running many activities simultaneously, which speeds up execution and improves efficiency.

Difference Between Concurrency and Parallelism

Concurrency and parallelism are related concepts but have distinct differences:

Concurrency: “Concurrency” describes a system’s capacity to carry out many activities concurrently. While tasks may not run simultaneously in a concurrent system, they can advance interleaved. Even when they run on a single processing unit, coordinating several tasks concurrently is the main goal.

Parallelism: On the other hand, parallelism entails carrying out numerous tasks concurrently, each assigned to a different processing unit or core. In a parallel system, it carries out tasks concurrently and in parallel. The emphasis is on breaking a difficulty into more manageable actions that it can carry out concurrently to produce quicker outcomes.

Managing the execution of many tasks so they can overlap and advance simultaneously is called concurrency. To achieve maximum performance, parallelism, on the other hand, entails carrying out numerous tasks concurrently using different processing units. Using multithreading and multiprocessing techniques, concurrent and parallel programming is possible in Python. Using many processes running simultaneously with multiprocessing enables parallelism while enabling numerous threads within a single process enables concurrency via multithreading.


Concurrency with Multithreading

import threading
import time
def task(name):
    print(f"Task {name} started")
    time.sleep(2)  # Simulating some time-consuming task
    print(f"Task {name} completed")
# Creating multiple threads
threads = []
for i in range(5):
    t = threading.Thread(target=task, args=(i,))
# Waiting for all threads to complete
for t in threads:
print("All tasks completed")

In this example, we define a task function that takes a name as an argument. Each task simulates a time-consuming operation by sleeping for 2 seconds. We create five threads and assign each to execute the task function with a different name. Parallelism is enabled by using many processes running simultaneously with multiprocessing, while concurrency is enabled via multithreading by enabling numerous threads within a single process. The output may vary, but you’ll observe that the tasks start and complete in an interleaved manner, indicating concurrent execution.

Parallelism with Multiprocessing

import multiprocessing
import time
def task(name):
    print(f"Task {name} started")
    time.sleep(2)  # Simulating some time-consuming task
    print(f"Task {name} completed")
# Creating multiple processes
processes = []
for i in range(5):
    p = multiprocessing.Process(target=task, args=(i,))
# Waiting for all processes to complete
for p in processes:
print("All tasks completed")

In this example, we define the same task function as before. However, instead of creating threads, we make five processes using multiprocessing. Process class. Each process is assigned to execute the task function with a different name. The processes are started and then joined to wait for their completion. When you run this code, you’ll see that the tasks are executed in parallel. Each process runs independently, utilizing separate CPU cores. As a result, the tasks may be completed in any order, and you’ll observe a significant reduction in the execution time compared to the multithreading example.

By contrasting these two examples, you can see how concurrency (multithreading) and parallelism (multiprocessing) differ in Python. While parallelism permits tasks to perform concurrently using different processing units, concurrency allows tasks to advance concurrently but not necessarily in parallel.

Introduction to Multithreading

A programming method known as multithreading enables numerous threads of execution to run simultaneously within a single process. A thread is a compact unit of execution that symbolizes a separate control flow within a programme. A programme can use multithreading to divide its tasks into smaller threads that can run concurrently, enabling concurrent execution and possibly enhancing performance. Multithreading is helpful when a programme must handle numerous separate activities or do multiple tasks simultaneously. It permits parallelism at the thread level within a process, allowing work to be done concurrently across tasks.


Advantages of Multithreading

Improved Responsiveness: By enabling processes to perform concurrently, multithreading can improve how responsive a programme is. It lets the programme carry out laborious tasks in the background while being interactive and sensitive to user interaction.

Efficient Resource Utilization: Utilizing system resources wisely includes efficiently using CPU and memory time. A programme can better utilize resources by running numerous threads concurrently, reducing idle time and maximizing resource utilization.

Simplified Design and Modularity: Multithreading can simplify programme design by dividing complicated processes into smaller, more manageable threads. It encourages modularity, which makes it simpler to maintain and reason about the code. Each thread can concentrate on a distinct subtask, making clearer and easier-to-maintain code.

Shared Memory Access: Direct access to shared memory by threads running in the same process enables efficient data sharing and communication between them. This can be advantageous when threads must cooperate, exchange information, or work on a common data structure.

Disadvantages of Multithreading

Synchronization and Race Conditions: To coordinate access to shared resources, synchronization techniques are required by multithreading. A lack of synchronization allows many threads to access shared data concurrently, resulting in race situations, corrupted data, and unpredictable behaviour. Synchronization might result in performance overhead and increases the complexity of the code.

Increased Complexity and Debugging Difficulty: Programmes using many threads are typically more sophisticated than those with a single thread. It can be difficult to manage shared resources, ensure thread safety, and coordinate the execution of several threads. Due to non-deterministic behaviour and probable race situations, debugging multithreaded programmes can also be more challenging.

Potential for Deadlocks and Starvation: In which threads cannot move forward because they are waiting for one another to release resources, they can result from improper synchronization or resource allocation. Similar to how some threads may run out of resources if resource allocation is not correctly controlled.

Global Interpreter Lock (GIL): The Global Interpreter Lock (GIL) in Python prevents multithreaded programmes from properly utilizing multiple CPU cores. One thread can only run Python bytecode simultaneously due to the GIL, which restricts the possible performance advantages of multithreading for CPU-bound operations. Multithreading can still be advantageous for I/O-bound or concurrent I/O and CPU-bound scenarios requiring external libraries or sub-processes.

Determining when and how to use multithreading successfully requires understanding its benefits and drawbacks. The advantages of multithreading can be reaped while minimizing potential negatives by carefully regulating synchronization, effectively managing shared resources, and taking into account the unique requirements of the programme.

Multithreading in Python

A threading module is provided by Python that enables the construction and administration of threads in a Python programme. The threading module makes Implementing multithreaded applications easier, which offers a high-level interface for working with threads.

Creating Threads in Python

The function that describes the thread’s task is commonly defined when creating a thread in Python using the threading module. The constructor of the Thread class is then given this function as a target. Here’s an example:

import threading
def task():
    print("Thread task executed")
# Create a thread
thread = threading.Thread(target=task)
# Start the thread
# Wait for the thread to complete
print("Thread execution completed")

In this example, we define a task function that prints a message. We create a thread by instantiating the Thread class with the target argument set to the task function. The thread is started using the start() method, which initiates the execution of the task function in a separate thread. Finally, we use the join() method to wait for the thread to complete before moving forward with the main program.

Managing Threads in Python

The threading module provides various methods and attributes to manage threads. Some commonly used methods include:

1. start(): Initiates the execution of the thread’s target function.

2. join([timeout]): Waits for the thread to complete execution. The optional timeout argument specifies the maximum time to wait for thread completion.

3. is_alive(): Returns True if the thread is executing.

4. name: A property that gets or sets the thread’s name.

5. daemon: A Boolean property determining whether the thread is a daemon thread. Daemon threads are abruptly terminated when the main program exits.

These are only a few illustrations of thread management methods and features. To help manage shared resources and synchronize thread execution, the threading module provides extra features, including locks, semaphores, condition variables, and thread synchronization.

The Global Interpreter Lock (GIL) and Its Impact on Multithreading in Python

One thread at a time can execute Python bytecode thanks to a feature called the Global Interpreter Lock (GIL) in CPython, the language’s default implementation. This means that even a Python programme with several threads can only advance one thread simultaneously.

Python’s GIL was created to make memory management easier and guard against concurrent object access. However, because only one thread can run Python bytecode, even on computers with many CPU cores, it also restricts the potential performance advantages of multithreading for CPU-bound operations.

Due to the GIL, multithreading in Python is better suited to I/O-bound activities, concurrent I/O jobs, and situations where threads must wait a long time for I/O operations to complete. In some circumstances, threads can wait while yielding the GIL to other threads, improving concurrency and making greater use of system resources.


It’s vital to remember that the GIL does not completely forbid or invalidate the use of multithreading for specific sorts of operations. Multithreading can still be advantageous regarding concurrent I/O, responsiveness, and effectively handling blocking operations.

However, the multiprocessing module, which uses distinct processes rather than threads, is often advised as a way to get around the GIL’s restrictions for CPU-bound workloads that can benefit from real parallelism over many CPU cores. When considering whether to employ multithreading or consider alternate strategies like multiprocessing for obtaining the desired performance and concurrency in a Python programme, it is essential to understand the impact of the GIL on multithreading in Python.

Key Points to Understand About the GIL

GIL and Python Threads

Python uses threads to achieve concurrency and simultaneously carry out numerous activities. However, even in a multithreaded Python programme, only one thread can execute Python bytecode simultaneously because of the GIL. This limits the possible speed improvements from multithreading for CPU-bound workloads because Python threads cannot operate concurrently on many CPU cores.

GIL’s Role in Memory Management

The GIL makes memory administration easier by limiting access to Python objects. Multiple threads could simultaneously access and alter Python objects without the GIL, potentially causing data corruption and unexpected behavior. By guaranteeing that only one thread may run Python bytecode, the GIL prevents such concurrency problems.

Impact on CPU-Bound Tasks

The GIL significantly affects CPU-bound tasks since only one thread can run Python bytecode concurrently. These tasks demand a lot of CPU computation but little I/O operation waiting. In some circumstances, multithreading with the GIL might not result in appreciable performance gains over a single-threaded strategy.

Scenarios Benefiting from the GIL

Not all tasks are fundamentally negatively impacted. In situations involving I/O-bound operations, when threads spend considerable time waiting for I/O to complete, the GIL can have little effect or even be advantageous. The GIL enhances concurrency and responsiveness by allowing other threads to run while one is stuck on I/O.

Alternatives to the GIL

You might think about switching to the multiprocessing module instead of multithreading if you have CPU-bound jobs that benefit from true parallelism over several CPU cores. You can set up distinct processes using the multiprocessing module with their own Python interpreters and memory spaces. Parallelism is possible because each process has its own GIL and can run Python bytecode concurrently with other processes.

It’s crucial to remember that not every Python implementation has a GIL. Alternative Python implementations, such as Jython and IronPython, do not include a GIL, enabling genuine thread parallelism. Additionally, there are circumstances where certain extension modules, like those written in C/C++, can release the GIL deliberately to boost concurrency.

import threading
def count():
    c = 0
    while c < 100000000:
        c += 1
# Create two threads
thread1 = threading.Thread(target=count)
thread2 = threading.Thread(target=count)
# Start the threads
# Wait for the threads to complete
print("Counting completed")


In this example, we define a count function that increments a counter variable c until it reaches 100 million. We create two threads, thread1 and thread2, and assign the count function as the target for both threads. The threads are started using the start() method, and then we use the join() method to wait for their completion.

When you run this code, you may expect the two threads to divide the counting work and complete the task faster than a single thread. However, due to the GIL, only one thread can execute Python bytecode simultaneously. As a result, the threads take approximately the same time to complete as if the counting was done in a single thread. The impact of the GIL can be observed by modifying the count function to perform CPU-bound tasks, such as complex calculations or intensive mathematical operations. In such cases, multithreading with the GIL may not improve performance over single-threaded execution.

It’s crucial to understand that the GIL influences only the CPython implementation and not all Python implementations. Different interpreter architectures used by alternative implementations like Jython and IronPython, which may achieve real parallelism with threads, do not have a GIL.

Thread Synchronization

Programming for many threads requires careful consideration of thread synchronization. Preventing conflicts and race conditions entails coordinating the execution of several threads and ensuring that shared resources are accessed and modified securely. Threads can interfere with one another without adequate synchronization, resulting in data corruption, inconsistent results, or unexpected behavior.

Need for Thread Synchronization

Thread synchronization is necessary when multiple threads access shared resources or variables simultaneously. The primary goals of synchronization are:

Mutual Exclusion

Ensuring that only one thread can access a shared resource or a critical code section at a time. This prevents data corruption or inconsistent states caused by concurrent modifications.


Allowing threads to communicate and coordinate their activities effectively. This includes tasks like signaling other threads when a condition is met or waiting for a certain condition to be satisfied before proceeding.

Synchronization Techniques

Python provides various synchronization mechanisms to address thread synchronization needs. Some commonly used techniques include locks, semaphores, and condition variables.


A lock, usually called a mutex, is a fundamental primitive for synchronization that permits mutual exclusion. While other threads wait for the lock to be released, it ensures that only one thread can ever acquire the lock. For this function, the Python threading library offers a Lock class.

import threading
counter = 0
counter_lock = threading.Lock()
def increment():
    global counter
    with counter_lock:
        counter += 1
# Create multiple threads to increment the counter
threads = []
for _ in range(10):
    t = threading.Thread(target=increment)
# Wait for all threads to complete
for t in threads:
print("Counter:", counter)

In this example, a shared counter variable is incremented by multiple threads. The Lock object, counter_lock, ensures mutual exclusion while accessing and modifying the counter.


A semaphore is a synchronization object that maintains a count. It allows multiple threads to enter a critical section up to a specified limit. If the limit is reached, subsequent threads will be blocked until a thread releases the semaphore. The threading module provides a Semaphore class for this purpose.

import threading
semaphore = threading.Semaphore(3)  # Allow 3 threads at a time
resource = []
def access_resource():
    with semaphore:
# Create multiple threads to access the resource
threads = []
for i in range(10):
    t = threading.Thread(target=access_resource, name=f"Thread-{i+1}")
# Wait for all threads to complete
for t in threads:
print("Resource:", resource)

In this example, a semaphore with a limit of 3 controls access to a shared resource. Only three threads can enter the critical section at a time, while others wait for the semaphore to be released.

Condition Variables

Condition variables allow threads to wait for a specific condition to be met before proceeding. They provide a mechanism for threads to signal each other and coordinate their activities. The threading module provides a Condition class for this purpose.

import threading
buffer = []
buffer_size = 5
buffer_lock = threading.Lock()
buffer_not_full = threading.Condition(lock=buffer_lock)
buffer_not_empty = threading.Condition(lock=buffer_lock)
def produce_item(item):
    with buffer_not_full:
        while len(buffer) >= buffer_size:
def consume_item():
    with buffer_not_empty:
        while len(buffer) == 0:
        item = buffer.pop(0)
        return item
# Create producer and consumer threads
producer = threading.Thread(target=produce_item, args=("Item 1",))
consumer = threading.Thread(target=consume_item)

In this example, a producer thread produces items and adds them to a shared buffer, while a consumer thread consumes items from the buffer. The condition variables buffer_not_full and buffer_not_empty synchronize the producer and consumer threads, ensuring that the buffer is not full before producing and not empty before consuming.


Multithreading in Python is a powerful method for achieving concurrency and enhancing application performance. It enables parallel processing and responsiveness by allowing multiple threads to run simultaneously within a single process. However, it’s essential to understand the Global Interpreter Lock (GIL) in Python, which limits true parallelism in CPU-bound processes. Best practices to build efficient multithreaded programs include identifying critical sections, synchronizing access to shared resources, and ensuring thread safety. Selecting the appropriate synchronization methods, such as locks and condition variables, is crucial. Although multithreading is particularly beneficial for I/O-bound operations, as it enables parallel processing and maintains program responsiveness, its impact on CPU-bound processes may be limited due to the GIL. Nevertheless, embracing multithreading and following best practices can lead to faster execution and an improved user experience in Python applications.

Key Takeaways

Some of the key-take-away points are as follows:

1. Multithreading allows concurrent execution of multiple threads within a single process, improving responsiveness and enabling parallelism.

2. Understanding the Global Interpreter Lock (GIL) in Python is crucial when working with multithreading, as it restricts true parallelism for CPU-bound tasks.

3. Synchronization mechanisms like locks, semaphores, and condition variables ensure thread safety and avoid race conditions in multithreaded programs.

4. Multithreading is well-suited for I/O-bound tasks, where it can overlap I/O operations and maintain program responsiveness.

5. Debugging and troubleshooting multithreaded code requires careful consideration of synchronization issues, proper error handling, and utilizing logging and debugging tools.

Frequently Asked Questions

Q1. What is the Global Interpreter Lock (GIL) in Python, and how does it affect multithreading?

A. One thread at a time can only execute Python bytecode thanks to the Global Interpreter Lock (GIL) feature of CPython, the standard Python implementation. This constraint limits genuine parallelism in multithreading and may affect the speed of CPU-intensive tasks.

Q2. Can multithreading improve the performance of CPU-bound tasks in Python?

A. Python’s GIL may prevent multithreading from significantly impacting CPU-bound job performance. The GIL prohibits concurrent execution of Python bytecode by multiple threads. However, CPU-bound processes involving I/O operations or external libraries that release the GIL while being executed can still benefit from multithreading.

Q3. How can I achieve parallel execution in Python if the GIL limits multithreading?

A. If parallel execution is essential for CPU-bound tasks, you can consider using multiprocessing instead of multithreading. Multiprocessing allows for true parallelism by running multiple processes simultaneously, each with its own Python interpreter and memory space.

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