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Concurrency and Parallel Execution in Python

Concurrency and Parallel Execution in Python



Concurrency is a key element of pc programming that helps improve functions’ velocity and responsiveness. Multithreading is a potent methodology for creating concurrency in Python. A number of threads can run concurrently inside a single course of utilizing multithreading, enabling parallel execution and efficient use of system sources. We will delve additional into Python multithreading on this tutorial. We will have a look at its concepts, advantages, and difficulties. We’ll discover ways to set up and management threads, share knowledge amongst them, and assure thread security.

We will even undergo typical traps to keep away from and the beneficial practices for creating and implementing multithreaded programmes. Understanding multithreading is an asset whether or not you might be creating functions that embody community actions, I/O-bound duties, or are simply making an attempt to make your programme extra responsive. You’ll be able to unleash the potential for enhanced efficiency and a seamless person expertise by taking advantage of concurrent execution. Be a part of us on this voyage as we delve into the depths of Python’s multithreading and uncover learn how to harness its potential to create concurrent and efficient functions.

Studying Aims

A few of the studying aims from the subject are as follows:

1. Be taught the basics of multithreading, together with what threads are, how they work inside a single course of, and the way they obtain concurrency. Perceive the advantages and limitations of multithreading in Python, together with the impression of the World Interpreter Lock (GIL) on CPU-bound duties.

2. Discover thread synchronization strategies like locks, semaphores, and situation variables to handle shared sources and keep away from race situations. Discover ways to guarantee thread security and design concurrent packages that deal with shared knowledge effectively and securely.

3. Achieve hands-on expertise creating and managing threads utilizing Python’s threading module. Discover ways to begin, be part of, and terminate threads, and discover frequent patterns for multithreading, corresponding to thread swimming pools and producer-consumer fashions.

This text was printed as part of the Information Science Blogathon.

Concurrency – Fundamentals

A key thought in pc science is concurrency, which refers back to the execution of a number of duties or processes concurrently. It allows programmes to work on a number of duties concurrently, enhancing responsiveness and general efficiency. Concurrency is essential for enhancing programme efficiency as a result of it permits programmes to successfully make the most of system sources like CPU cores, I/O gadgets, and community connections. A programme can effectively use these sources and reduce idle time by operating many actions concurrently, which hurries up execution and improves effectivity.

Distinction Between Concurrency and Parallelism

Concurrency and parallelism are associated ideas however have distinct variations:

Concurrency: “Concurrency” describes a system’s capability to hold out many actions concurrently. Whereas duties could not run concurrently in a concurrent system, they will advance interleaved. Even after they run on a single processing unit, coordinating a number of duties concurrently is the primary purpose.

Parallelism: However, parallelism entails finishing up quite a few duties concurrently, every assigned to a distinct processing unit or core. In a parallel system, it carries out duties concurrently and in parallel. The emphasis is on breaking a problem into extra manageable actions that it could actually perform concurrently to supply faster outcomes.

Managing the execution of many duties to allow them to overlap and advance concurrently is named concurrency. To attain most efficiency, parallelism, then again, entails finishing up quite a few duties concurrently utilizing completely different processing models. Utilizing multithreading and multiprocessing strategies, concurrent and parallel programming is feasible in Python. Utilizing many processes operating concurrently with multiprocessing allows parallelism whereas enabling quite a few threads inside a single course of allows concurrency by way of multithreading.


Concurrency with Multithreading

import threading
import time
def activity(identify):
    print(f"Process {identify} began")
    time.sleep(2)  # Simulating some time-consuming activity
    print(f"Process {identify} accomplished")
# Creating a number of threads
threads = []
for i in vary(5):
    t = threading.Thread(goal=activity, args=(i,))
# Ready for all threads to finish
for t in threads:
    t.be part of()
print("All duties accomplished")

On this instance, we outline a activity operate that takes a reputation as an argument. Every activity simulates a time-consuming operation by sleeping for two seconds. We create 5 threads and assign every to execute the duty operate with a distinct identify. Parallelism is enabled through the use of many processes operating concurrently with multiprocessing, whereas concurrency is enabled by way of multithreading by enabling quite a few threads inside a single course of. The output could differ, however you’ll observe that the duties begin and full in an interleaved method, indicating concurrent execution.

Parallelism with Multiprocessing

import multiprocessing
import time
def activity(identify):
    print(f"Process {identify} began")
    time.sleep(2)  # Simulating some time-consuming activity
    print(f"Process {identify} accomplished")
# Creating a number of processes
processes = []
for i in vary(5):
    p = multiprocessing.Course of(goal=activity, args=(i,))
# Ready for all processes to finish
for p in processes:
    p.be part of()
print("All duties accomplished")

On this instance, we outline the identical activity operate as earlier than. Nevertheless, as an alternative of making threads, we make 5 processes utilizing multiprocessing. Course of class. Every course of is assigned to execute the duty operate with a distinct identify. The processes are began after which joined to attend for his or her completion. Once you run this code, you’ll see that the duties are executed in parallel. Every course of runs independently, using separate CPU cores. In consequence, the duties could also be accomplished in any order, and also you’ll observe a major discount within the execution time in comparison with the multithreading instance.

By contrasting these two examples, you possibly can see how concurrency (multithreading) and parallelism (multiprocessing) differ in Python. Whereas parallelism permits duties to carry out concurrently utilizing completely different processing models, concurrency permits duties to advance concurrently however not essentially in parallel.

Introduction to Multithreading

A programming methodology generally known as multithreading allows quite a few threads of execution to run concurrently inside a single course of. A thread is a compact unit of execution that symbolizes a separate management movement inside a programme. A programme can use multithreading to divide its duties into smaller threads that may run concurrently, enabling concurrent execution and presumably enhancing efficiency. Multithreading is useful when a programme should deal with quite a few separate actions or do a number of duties concurrently. It permits parallelism on the thread stage inside a course of, permitting work to be accomplished concurrently throughout duties.


Benefits of Multithreading

Improved Responsiveness: By enabling processes to carry out concurrently, multithreading can enhance how responsive a programme is. It lets the programme perform laborious duties within the background whereas being interactive and delicate to person interplay.

Environment friendly Useful resource Utilization: Using system sources properly contains effectively utilizing CPU and reminiscence time. A programme can higher make the most of sources by operating quite a few threads concurrently, lowering idle time and maximizing useful resource utilization.

Simplified Design and Modularity: Multithreading can simplify programme design by dividing difficult processes into smaller, extra manageable threads. It encourages modularity, which makes it easier to take care of and cause in regards to the code. Every thread can focus on a definite subtask, making clearer and easier-to-maintain code.

Shared Reminiscence Entry: Direct entry to shared reminiscence by threads operating in the identical course of allows environment friendly knowledge sharing and communication between them. This may be advantageous when threads should cooperate, alternate info, or work on a standard knowledge construction.

Disadvantages of Multithreading

Synchronization and Race Situations: To coordinate entry to shared sources, synchronization strategies are required by multithreading. A scarcity of synchronization permits many threads to entry shared knowledge concurrently, leading to race conditions, corrupted knowledge, and unpredictable behaviour. Synchronization may end in efficiency overhead and will increase the complexity of the code.

Elevated Complexity and Debugging Issue: Programmes utilizing many threads are usually extra refined than these with a single thread. It may be tough to handle shared sources, guarantee thread security, and coordinate the execution of a number of threads. On account of non-deterministic behaviour and possible race conditions, debugging multithreaded programmes can be tougher.

Potential for Deadlocks and Hunger: Wherein threads can not transfer ahead as a result of they’re ready for each other to launch sources, they will consequence from improper synchronization or useful resource allocation. Just like how some threads could run out of sources if useful resource allocation will not be accurately managed.

World Interpreter Lock (GIL): The World Interpreter Lock (GIL) in Python prevents multithreaded programmes from correctly using a number of CPU cores. One thread can solely run Python bytecode concurrently because of the GIL, which restricts the doable efficiency benefits of multithreading for CPU-bound operations. Multithreading can nonetheless be advantageous for I/O-bound or concurrent I/O and CPU-bound eventualities requiring exterior libraries or sub-processes.

Figuring out when and learn how to use multithreading efficiently requires understanding its advantages and downsides. Some great benefits of multithreading may be reaped whereas minimizing potential negatives by rigorously regulating synchronization, successfully managing shared sources, and bearing in mind the distinctive necessities of the programme.

Multithreading in Python

A threading module is supplied by Python that permits the development and administration of threads in a Python programme. The threading module makes Implementing multithreaded functions simpler, which affords a high-level interface for working with threads.

Creating Threads in Python

The operate that describes the thread’s activity is usually outlined when making a thread in Python utilizing the threading module. The constructor of the Thread class is then given this operate as a goal. Right here’s an instance:

import threading
def activity():
    print("Thread activity executed")
# Create a thread
thread = threading.Thread(goal=activity)
# Begin the thread
# Await the thread to finish
thread.be part of()
print("Thread execution accomplished")

On this instance, we outline a activity operate that prints a message. We create a thread by instantiating the Thread class with the goal argument set to the duty operate. The thread is began utilizing the beginning() methodology, which initiates the execution of the duty operate in a separate thread. Lastly, we use the be part of() methodology to attend for the thread to finish earlier than transferring ahead with the primary program.

Managing Threads in Python

The threading module supplies varied strategies and attributes to handle threads. Some generally used strategies embody:

1. begin(): Initiates the execution of the thread’s goal operate.

2. be part of([timeout]): Waits for the thread to finish execution. The non-compulsory timeout argument specifies the utmost time to attend for thread completion.

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

4. identify: A property that will get or units the thread’s identify.

5. daemon: A Boolean property figuring out whether or not the thread is a daemon thread. Daemon threads are abruptly terminated when the primary program exits.

These are just a few illustrations of thread administration strategies and options. To assist handle shared sources and synchronize thread execution, the threading module supplies additional options, together with locks, semaphores, situation variables, and thread synchronization.

The World Interpreter Lock (GIL) and Its Impression on Multithreading in Python

One thread at a time can execute Python bytecode due to a function known as the World Interpreter Lock (GIL) in CPython, the language’s default implementation. Which means that even a Python programme with a number of threads can solely advance one thread concurrently.

Python’s GIL was created to make reminiscence administration simpler and guard in opposition to concurrent object entry. Nevertheless, as a result of just one thread can run Python bytecode, even on computer systems with many CPU cores, it additionally restricts the potential efficiency benefits of multithreading for CPU-bound operations.

As a result of GIL, multithreading in Python is healthier suited to I/O-bound actions, concurrent I/O jobs, and conditions the place threads should wait a very long time for I/O operations to finish. In some circumstances, threads can wait whereas yielding the GIL to different threads, enhancing concurrency and making larger use of system sources.


It’s very important to keep in mind that the GIL doesn’t fully forbid or invalidate the usage of multithreading for certain types of operations. Multithreading can nonetheless be advantageous concerning concurrent I/O, responsiveness, and successfully dealing with blocking operations.

Nevertheless, the multiprocessing module, which makes use of distinct processes reasonably than threads, is usually suggested as a approach to get across the GIL’s restrictions for CPU-bound workloads that may profit from actual parallelism over many CPU cores. When contemplating whether or not to make use of multithreading or take into account alternate methods like multiprocessing for acquiring the specified efficiency and concurrency in a Python programme, it’s important to grasp the impression of the GIL on multithreading in Python.

Key Factors to Perceive Concerning the GIL

GIL and Python Threads

Python makes use of threads to attain concurrency and concurrently perform quite a few actions. Nevertheless, even in a multithreaded Python programme, just one thread can execute Python bytecode concurrently due to the GIL. This limits the doable velocity enhancements from multithreading for CPU-bound workloads as a result of Python threads can not function concurrently on many CPU cores.

GIL’s Position in Reminiscence Administration

The GIL makes reminiscence administration simpler by limiting entry to Python objects. A number of threads might concurrently entry and alter Python objects with out the GIL, probably inflicting knowledge corruption and surprising habits. By guaranteeing that just one thread could run Python bytecode, the GIL prevents such concurrency issues.

Impression on CPU-Certain Duties

The GIL considerably impacts CPU-bound duties since just one thread can run Python bytecode concurrently. These duties demand lots of CPU computation however little I/O operation ready. In some circumstances, multithreading with the GIL won’t end in considerable efficiency features over a single-threaded technique.

Situations Benefiting from the GIL

Not all duties are basically negatively impacted. In conditions involving I/O-bound operations, when threads spend appreciable time ready for I/O to finish, the GIL can have little impact and even be advantageous. The GIL enhances concurrency and responsiveness by permitting different threads to run whereas one is caught on I/O.

Options to the GIL

You may take into consideration switching to the multiprocessing module as an alternative of multithreading when you have CPU-bound jobs that profit from true parallelism over a number of CPU cores. You’ll be able to arrange distinct processes utilizing the multiprocessing module with their very own Python interpreters and reminiscence areas. Parallelism is feasible as a result of every course of has its personal GIL and might run Python bytecode concurrently with different processes.

It’s essential to keep in mind that not each Python implementation has a GIL. Various Python implementations, corresponding to Jython and IronPython, don’t embody a GIL, enabling real thread parallelism. Moreover, there are circumstances the place sure extension modules, like these written in C/C++, can launch the GIL intentionally to spice up concurrency.

import threading
def rely():
    c = 0
    whereas c < 100000000:
        c += 1
# Create two threads
thread1 = threading.Thread(goal=rely)
thread2 = threading.Thread(goal=rely)
# Begin the threads
# Await the threads to finish
thread1.be part of()
thread2.be part of()
print("Counting accomplished")


On this instance, we outline a rely operate that increments a counter variable c till it reaches 100 million. We create two threads, thread1 and thread2, and assign the rely operate because the goal for each threads. The threads are began utilizing the beginning() methodology, after which we use the be part of() methodology to attend for his or her completion.

Once you run this code, it’s possible you’ll anticipate the 2 threads to divide the counting work and full the duty sooner than a single thread. Nevertheless, because of the GIL, just one thread can execute Python bytecode concurrently. In consequence, the threads take roughly the identical time to finish as if the counting was accomplished in a single thread. The impression of the GIL may be noticed by modifying the rely operate to carry out CPU-bound duties, corresponding to advanced calculations or intensive mathematical operations. In such circumstances, multithreading with the GIL could not enhance efficiency over single-threaded execution.

It’s essential to grasp that the GIL influences solely the CPython implementation and never all Python implementations. Totally different interpreter architectures utilized by different implementations like Jython and IronPython, which can obtain actual parallelism with threads, do not need a GIL.

Thread Synchronization

Programming for a lot of threads requires cautious consideration of thread synchronization. Stopping conflicts and race situations entails coordinating the execution of a number of threads and guaranteeing that shared sources are accessed and modified securely. Threads can intervene with each other with out sufficient synchronization, leading to knowledge corruption, inconsistent outcomes, or surprising habits.

Want for Thread Synchronization

Thread synchronization is critical when a number of threads entry shared sources or variables concurrently. The first objectives of synchronization are:

Mutual Exclusion

Guaranteeing that just one thread can entry a shared useful resource or a crucial code part at a time. This prevents knowledge corruption or inconsistent states attributable to concurrent modifications.


Permitting threads to speak and coordinate their actions successfully. This contains duties like signaling different threads when a situation is met or ready for a sure situation to be happy earlier than continuing.

Synchronization Strategies

Python supplies varied synchronization mechanisms to deal with thread synchronization wants. Some generally used strategies embody locks, semaphores, and situation variables.


A lock, often known as a mutex, is a elementary primitive for synchronization that allows mutual exclusion. Whereas different threads await the lock to be launched, it ensures that just one thread can ever purchase the lock. For this operate, the Python threading library affords a Lock class.

import threading
counter = 0
counter_lock = threading.Lock()
def increment():
    world counter
    with counter_lock:
        counter += 1
# Create a number of threads to increment the counter
threads = []
for _ in vary(10):
    t = threading.Thread(goal=increment)
# Await all threads to finish
for t in threads:
    t.be part of()
print("Counter:", counter)

On this instance, a shared counter variable is incremented by a number of threads. The Lock object, counter_lock, ensures mutual exclusion whereas accessing and modifying the counter.


A semaphore is a synchronization object that maintains a rely. It permits a number of threads to enter a crucial part as much as a specified restrict. If the restrict is reached, subsequent threads will likely be blocked till a thread releases the semaphore. The threading module supplies a Semaphore class for this function.

import threading
semaphore = threading.Semaphore(3)  # Permit 3 threads at a time
useful resource = []
def access_resource():
    with semaphore:
        useful resource.append(threading.current_thread().identify)
# Create a number of threads to entry the useful resource
threads = []
for i in vary(10):
    t = threading.Thread(goal=access_resource, identify=f"Thread-{i+1}")
# Await all threads to finish
for t in threads:
    t.be part of()
print("Useful resource:", useful resource)

On this instance, a semaphore with a restrict of three controls entry to a shared useful resource. Solely three threads can enter the crucial part at a time, whereas others await the semaphore to be launched.

Situation Variables

Situation variables permit threads to attend for a particular situation to be met earlier than continuing. They supply a mechanism for threads to sign one another and coordinate their actions. The threading module supplies a Situation class for this function.

import threading
buffer = []
buffer_size = 5
buffer_lock = threading.Lock()
buffer_not_full = threading.Situation(lock=buffer_lock)
buffer_not_empty = threading.Situation(lock=buffer_lock)
def produce_item(merchandise):
    with buffer_not_full:
        whereas len(buffer) >= buffer_size:
def consume_item():
    with buffer_not_empty:
        whereas len(buffer) == 0:
        merchandise = buffer.pop(0)
        return merchandise
# Create producer and client threads
producer = threading.Thread(goal=produce_item, args=("Merchandise 1",))
client = threading.Thread(goal=consume_item)
producer.be part of()
client.be part of()

On this instance, a producer thread produces gadgets and provides them to a shared buffer, whereas a client thread consumes gadgets from the buffer. The situation variables buffer_not_full and buffer_not_empty synchronize the producer and client threads, guaranteeing that the buffer will not be full earlier than producing and never empty earlier than consuming.


Multithreading in Python is a robust methodology for reaching concurrency and enhancing utility efficiency. It allows parallel processing and responsiveness by permitting a number of threads to run concurrently inside a single course of. Nevertheless, it’s important to grasp the World Interpreter Lock (GIL) in Python, which limits true parallelism in CPU-bound processes. Greatest practices to construct environment friendly multithreaded packages embody figuring out crucial sections, synchronizing entry to shared sources, and guaranteeing thread security. Deciding on the suitable synchronization strategies, corresponding to locks and situation variables, is essential. Though multithreading is especially useful for I/O-bound operations, because it allows parallel processing and maintains program responsiveness, its impression on CPU-bound processes could also be restricted because of the GIL. However, embracing multithreading and following greatest practices can result in sooner execution and an improved person expertise in Python functions.

Key Takeaways

A few of the key-take-away factors are as follows:

1. Multithreading permits concurrent execution of a number of threads inside a single course of, enhancing responsiveness and enabling parallelism.

2. Understanding the World Interpreter Lock (GIL) in Python is essential when working with multithreading, because it restricts true parallelism for CPU-bound duties.

3. Synchronization mechanisms like locks, semaphores, and situation variables guarantee thread security and keep away from race situations in multithreaded packages.

4. Multithreading is well-suited for I/O-bound duties, the place it could actually overlap I/O operations and preserve program responsiveness.

5. Debugging and troubleshooting multithreaded code requires cautious consideration of synchronization points, correct error dealing with, and using logging and debugging instruments.

Continuously Requested Questions

Q1. What’s the World Interpreter Lock (GIL) in Python, and the way does it have an effect on multithreading?

A. One thread at a time can solely execute Python bytecode due to the World Interpreter Lock (GIL) function of CPython, the usual Python implementation. This constraint limits real parallelism in multithreading and should have an effect on the velocity of CPU-intensive duties.

Q2. Can multithreading enhance the efficiency of CPU-bound duties in Python?

A. Python’s GIL could forestall multithreading from considerably impacting CPU-bound job efficiency. The GIL prohibits concurrent execution of Python bytecode by a number of threads. Nevertheless, CPU-bound processes involving I/O operations or exterior libraries that launch the GIL whereas being executed can nonetheless profit from multithreading.

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

A. If parallel execution is crucial for CPU-bound duties, you possibly can think about using multiprocessing as an alternative of multithreading. Multiprocessing permits for true parallelism by operating a number of processes concurrently, every with its personal Python interpreter and reminiscence house.

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