Deep Dive into Python's GIL: Understanding its Impact on Multi-Threading

Deep Dive into Python's GIL: Understanding its Impact on Multi-Threading
Photo by Jeremy Bishop / Unsplash

When it comes to Python and multi-threading, there's one concept that often stirs up confusion and debate: the Global Interpreter Lock, or GIL. It's a unique feature of CPython, Python's default interpreter, and it has a significant impact on how we write and optimize multi-threaded Python programs. In this post, we'll explore what the GIL is, why it exists, how it affects multi-threading, and ways to work around it.

Understanding the GIL

At its core, the Global Interpreter Lock is a mutex (or a lock) that allows only one thread to execute at a time in a single process. GIL was introduced to prevent multiple native threads from executing Python bytecodes simultaneously. This is because CPython's memory management is not thread-safe.

To put it simply, the GIL is a mechanism to prevent your Python programs from corrupting memory when two threads try to modify the same Python object simultaneously. Therefore, even if your machine has multiple CPUs or cores, a single Python process won't utilize more than one at a time due to the GIL.

Impact on Multi-Threading

Because of the GIL, multi-threaded CPU-bound programs may run slower than expected on multi-core processors. This is because the GIL allows only one thread to execute at a time within a single process.

But not all Python programs are CPU-bound and not all programs will suffer from the GIL. For I/O-bound programs, such as programs that spend most of their time waiting for data from a network or a disk read, Python threads can be efficient. That's because while one thread is waiting for the I/O, the GIL can be released for other threads to use.

GIL in Action: An Illustrative Example

Let's consider a simple CPU-bound task: calculating the sum of the first N integers. We will compare a single-threaded approach versus a multi-threaded approach.

import time
import threading

# Function to compute the sum of first N integers
def compute_sum(N):
    return sum(range(1, N+1))

N = 10**7

# Single-threaded approach
start_time_single = time.time()
end_time_single = time.time()

print(f"Single thread time: {end_time_single - start_time_single}")

>>> Single thread time: 0.09821081161499023 # Around 0.098s

Now let's split the task into two threads. One computes the sum of the first N/2 integers, and the other computes the sum of the rest.

# Multi-threaded approach
result = [0, 0]

def compute_sum_thread(i, start, end):
    result[i] = sum(range(start, end+1))

t1 = threading.Thread(target=compute_sum_thread, args=(0, 1, N//2))
t2 = threading.Thread(target=compute_sum_thread, args=(1, N//2+1, N))

start_time_multi = time.time()



total = result[0] + result[1]

end_time_multi = time.time()

print(f"Multi thread time: {end_time_multi - start_time_multi}")

>>> Multi thread time: 0.09809112548828125  # Around 0.098s

Despite splitting the task into two threads, you might observe that the multi-threaded version doesn't execute twice as fast as the single-threaded version, due to Python's GIL, which allows only one thread to execute at a time within a single process. In fact, the time to obtain results in both implementations was almost the same.

Workarounds and Alternatives

Despite its notorious reputation, there are several ways to work around the GIL and make your Python programs more concurrent.

  1. Multiprocessing: Instead of threads, use processes. The multiprocessing module in Python creates separate Python interpreter processes, each with its own GIL, allowing for true concurrent execution.
  2. Native Extensions: Write the CPU-bound parts of your program in a language like C or Cython, and then use Python's native extension APIs to run this code. This code can run outside of the GIL.
  3. Alternative Python Interpreters: Consider using Jython, IronPython, or PyPy, alternative Python interpreters that don't have a GIL. Note, however, that these interpreters may not be 100% compatible with CPython and may not support all the libraries that CPython supports.

Why Does Python Have GIL?

You might be wondering if the GIL can impose such limitations on multi-threaded programs, why does Python have it in the first place? The reasons are rooted in Python's design principles and its focus on simplicity and ease of use.

  1. Memory Management: One of the main reasons for the existence of the GIL is to simplify memory management. Python's memory allocator isn't thread-safe. The GIL prevents multiple native threads from executing Python bytecodes at once, which could potentially lead to inconsistencies in shared data and memory corruption.
  2. Easy C Extension Writing: Python has a large ecosystem of C extensions, such as NumPy and SciPy. Writing these extensions is made easier by the GIL because developers don't have to worry about simultaneous threads modifying shared data. If Python removed the GIL, many of these extensions would potentially break or would need to implement their own locking mechanisms.
  3. Performance Benefits for Single-Threaded Programs: Interestingly, in some cases, the GIL can actually improve performance. For single-threaded programs, which are quite common in Python, removing the GIL could result in a slowdown due to the overhead of unnecessary locking and unlocking operations.
  4. Historical Reasons: The GIL has been part of Python since its inception. Over the years, Python's core development team has made several attempts to remove the GIL, but these have been challenging due to the need to maintain backward compatibility and the impact on single-threaded performance.

Python's Future and GIL

The GIL has been part of Python for a long time, and despite its impact on multi-threading, it's not going anywhere soon. There are ongoing efforts to remove or replace the GIL in CPython, but these efforts face significant challenges due to backward compatibility and the impact on single-threaded performance.

Despite these challenges, the Python community continues to explore ways to improve concurrency in Python. For example, Python 3.7 introduced the concurrent.futures module, which simplifies running tasks concurrently using threads or processes.

While the GIL does impose some limitations on multi-threaded CPU-bound programs, it's not the end of the story for concurrency in Python


In this deep dive into Python's Global Interpreter Lock (GIL), we've explored its impact on multi-threading and its rationale. The GIL, unique to CPython, enforces the execution of only one thread at a time in a single process, making it a key factor in Python's memory safety but at the same time limiting the speed of multi-threaded CPU-bound programs.

Despite the limitations imposed by the GIL, it's not an insurmountable obstacle. Strategies such as multiprocessing, using native extensions, or leveraging alternative Python interpreters can help us design efficient and concurrent applications.

The GIL remains a fundamental part of Python due to its role in memory management, ease of C extension writing, and performance benefits for single-threaded programs. However, Python's vibrant community continually strives for improvements, as seen in new features like the concurrent.futures module.

In summary, the GIL is a trade-off within Python's design—an embodiment of Python's prioritization of simplicity and ease of use. Understanding its existence, impact, and ways around it can inform our decisions in structuring Python applications, emphasizing that while the GIL presents a challenge to concurrency, it certainly doesn't spell the end of the story.


  • The Global Interpreter Lock (GIL) is a feature of CPython that allows only one thread to execute at a time in a single process.
  • While it ensures memory safety, it limits the efficiency of multi-threaded CPU-bound programs.
  • Workarounds to these limitations include multiprocessing, native extensions, and alternative Python interpreters.
  • The GIL remains integral to Python due to historical reasons and its role in simplifying Python's design.
  • Understanding the GIL's impact and how to work around it is crucial for writing efficient Python code that requires concurrency or parallelism.

Your insights and experiences with Python's GIL are invaluable to me, so don't hesitate to share them in the comments. Enjoyed this post? Make sure to subscribe to my newsletter to keep up with upcoming technical explorations.

Maciej Marzęta

Maciej Marzęta

Founder of MarzTech. Python Technical Leader at unicorn startup. Crypto enthusiast and programmer for life.
Cracow, Poland