What is Python Global Interpreter Lock (GIL)?
In the realm of Python, an infamous creature known as the Global Interpreter Lock (GIL) lurks, affecting performance and threading. As programmers venture into Python’s heart to understand, challenge, and potentially transcend the GIL, they encounter both its limitations and its impact on the implementation of Python in various domains, including data science and machine learning.
Update from Redditor zurtex on disable GIL
See zurtex's comment on reddit
The --disable-gil is not landing for CPython 3.12. In fact, the CPython 3.12 branch is now locked to commits outside the release management team as they prepare for the rc1 release.
Further, such a large change would only land on main (currently 3.13), not directly on a minor version branch once that branch is split from main, which was months ago for 3.12, way before the announcement to intent to accept of the PEP.
PEP 703 might land on CPython 3.13, but the Steering Council were pretty clear they have no issue with it slipping until CPython 3.14. So could be wrong when it says it is landing "soon" further:
PEP 703 is in draft status, it has not been officially accepted yet
Sam Gross is on vacation right now and would have a lot of work to do rebasing nogil branch to main and at least addressing several issues the Faster CPython team raised that can cause it to crash using pure Python code before it would be considered to land into main
CPython 3.12 disable Global Interpreter Lock (GIL)
The ./configure script may soon set the Py_NOGIL macro in Python/patchlevel.h file. That means that the way to disable the GIL looks like this:
Note the --disable-gil flag is PEP draft. Even though it says it’s unrecognized, the above commands will create binaries with GIL disabled.
Disable the GIL at build time: https://peps.python.org/pep-0703/#build-configuration-changes
Re-enable the GIL at runtime: https://peps.python.org/pep-0703/#pythongil-environment-variable
The origin and purpose of Python GIL and the role of Global Interpreter Lock
Originally introduced in Python 1.5, the GIL was a mechanism designed to improve stability. It's essentially a mutex (or a lock) that allows only one thread to execute Python bytecode at a time. This was primarily a solution to handle multi-threading and facilitate memory management, creating a sense of safety and predictability in the Python environment.
The limitation of the Python Global Interpreter Lock (GIL)
However, this safety mechanism came at a cost. Despite its original intent, the GIL became a source of performance hindrance, causing Python programs to become unresponsive during I/O operations or system calls.
Python multithreading became virtually useless for CPU-intensive tasks due to this lock, creating a major roadblock. Despite the simplicity and safety it afforded, we were called into an adventure to challenge and overcome this limitation.
Async libraries and multiprocessing: the best friends of Python concurrency
Before challenging the GIL itself, the initial response was to mitigate its limitations. For high-concurrency tasks, async I/O libraries were introduced. These libraries allow I/O operations and system calls to be made without interfering with the GIL.
For CPU-weighty workloads, multiprocessing emerged, allowing multiple Python runtimes to operate simultaneously. This was possible through the message-passing mechanism, where threads could communicate and share workloads. These tools provided a workaround for the GIL, allowing you to avoid it. But these libraries will not resolve the underlying problem of the GIL. Exploring alternatives to CPython, such as IronPython and Jython, reveals how they handle multi-threading differently. IronPython, for instance, integrates with the .NET framework and benefits from its threading model, while Jython, which runs on the Java Virtual Machine, leverages Java's threading capabilities. These implementations of Python offer insights into different approaches to concurrency, showcasing the versatility of the programming language in adapting to various platforms and environments.
The decision to disable the py.GIL: get ready, the challenge begins
With the release of CPython 3.12, the journey took an exciting turn - the GIL could now be disabled. This venture, requiring a recompilation of Python with the --disable-gil flag, promised dramatic potential performance enhancements. Yet, it also propelled us into a much riskier territory. What would be the implications of such a radical move?
The implication of Python GIL disabling: the safety is off
Disabling the Global Interpreter Lock allowed multiple threads to simultaneously execute Python bytecode. This meant an opportunity to potentially boost performance significantly. However, the risk was considerable; it entirely removed Python's in-built safety cover, making way for potential crashes due to issues with shared resources and memory handling.
Real threads in Python: real rewards
In the wake of disabling GIL, we could now consider a more novel use of threads. Even CPU-intensive tasks could be divided across threads, which could be considered a significant reward of this ordeal. However, this required meticulous handling of shared memory and resources, or else it could lead to disaster - we had effectively unshackled Python from its safety constraints.
Takeaways and conclusions about Python GIL
Despite the thrills and opportunities of disabling the GIL, it became clear that Python’s original design has its merits. While its limitations were glaring, the GIL also provided protection against potential crashes, ensuring the overall stability of the system.
There is potential for improved performance in disabling the GIL, and it comes with considerable risks. Proper understanding and precautions are necessary before venturing into this new territory. It is smarter to optimize within Python's threading model, capitalizing on async I/O libraries and multiprocessing rather than disabling the GIL outright.
The adventure into Python's GIL brings back not just a potential solution but also a deeper understanding of the system at large, promoting a more thoughtful and intricate way to engage with Python. Using the best tools at hand, and avoiding performance penalties from the GIL are possible.
My thoughts on the Python GIL
I was initially excited about the possibility of disabling the Global Interpreter Lock (GIL) in Python, as it promised to enable new opportunities for performance gains. However, upon further reflection, I believe that this is not the case. Disabling the GIL would only allow for the use of threading patterns and threads within Python, which I am not a fan of. My preferred method for achieving high concurrency and efficient programs is to use multiple processes with message passing.
This is how we have designed PubNub, which currently has many Kubernetes clusters distributed around the world and services over 1 billion devices on the network. We are able to achieve this level of concurrency and scale by following patterns that allow us to fully leverage the resources that we purchase from our cloud provider, Amazon Web Services. We do not have to worry about threads, locking, or memory safety because this design pattern is implemented in C, using asynchronous I/O kernel interrupts directly through the epoll kernel API on Linux. This allows us to fully take advantage of our CPU without having to do context thrashing.
The same effect can be achieved in Python. Launching a single-threaded Python worker in a Docker container and then spinning up multiple Docker containers. You can round robin and load balance using a load balancer, such as Nginx, which is a common standard load balancer that is used in the Kubernetes ecosystem. This will achieve what you need. You will want to add an asynchronous I/O library so that the Python engine will not lock while waiting for I/O. This will allow you to ultimately take as much advantage of the hardware that you have purchased.
If you want to just take advantage of concurrency on a single system, like your laptop, using Python multiprocessing Library Is the way to go. It provides mechanisms to distribute workloads to the CPU cores on your system.
Python 3.12 GIL Frequently Asked Questions
We need to cover a few extra items in regard to what you can do next. Here are some common questions that came up during the discussions.
Should I be excited about Python 3.12 and the new --disable-gil flag?
Yes if you like threads and working with the challenges and the extra code efforts.
No otherwise. There are better options!
Whether to be excited about Python 3.12 and the new --disable-gil flag largely depends on your specific use case and your grasp of multi-threading complexities.
If you frequently engage in CPU-intensive tasks and are versed in careful thread management and programming, the option to disable the Global Interpreter Lock (GIL) could be beneficial. This feature may potentially bring dramatic increases in performance because multiple threads can execute Python bytecode simultaneously without GIL slowing them down.
This feature also increases risk. The GIL in Python plays a crucial role in maintaining the integrity of Python objects by preventing multiple threads from executing Python bytecodes simultaneously. Developing threaded programs without GIL necessitates a keen understanding of thread safety and synchronization, as it is now your responsibility to ensure no data races or inconsistencies occur.
Many standard Python libraries rely on the existence of the GIL for thread safety, and these may not function correctly—or at all—if you choose to disable it.
So while this feature could potentially offer significant performance improvements in specific contexts, it is not something to use either lightly or universally. As with any powerful tool, it requires understanding, respect, and care to use it properly and effectively.
For most Python developers, especially those who are not developing CPU-intensive multi-threaded applications, the arrival of async libraries and multiprocessing modules in the Python standard library provides sufficient mechanisms for handling concurrency and parallelism, making the disabling of the GIL less significant.
What becomes available by disabling the GIL?
Disabling the Global Interpreter Lock (GIL) primarily makes it possible for multi-core Python programs to achieve true concurrent thread execution, potentially leading to a significant performance improvement for CPU-bound tasks.
Here is a brief summary of what becomes available when the GIL is disabled:
Concurrent thread execution: With GIL disabled, multiple threads can execute Python bytecodes concurrently without waiting for the lock. This could lead to better utilization of multi-core processors.
Better performance for multi-threaded CPU-bound tasks: For CPU-bound tasks that have been split into multiple threads, disabling the GIL could potentially lead to significant performance improvement, as each thread can execute concurrently on different cores.
More control over thread synchronization: Disabling the GIL means that developers now have to handle thread synchronization themselves. While this requires a deep understanding of multi-threading, it also gives more control over thread operations and could potentially allow for more nuanced and optimized performance tuning.
It is important to be aware of the potential downside. Disabling the GIL removes a safeguard that ensures thread-safe operation for many Python libraries and operations. Therefore, the complexities and potential pitfalls associated with multi-threading, such as race conditions, deadlocks, and other synchronization issues, become the developer's responsibility to address and manage.
Will I see a performance improvement when disabling the GIL?
Whether or not you'll see a performance improvement by disabling the Global Interpreter Lock (GIL) depends on the nature of your Python program.
If your program heavily utilizes CPU-bound tasks and has been designed using multi-threading, you could potentially see a significant performance improvement. This is because with the GIL disabled, multiple threads can execute Python bytecodes on different cores at the same time, leading to better utilization of multi-core processors.
When your program is primarily I/O-bound (for example, it spends most of its time waiting for network responses, reading from disk, etc.), then GIL is less likely to be your bottleneck and disabling it would likely yield minimal, if any, performance improvements. In fact, in I/O-bound scenarios, properly leveraging asynchronous programming and concurrency libraries can be more beneficial than disabling the GIL.
If your program isn't designed to manage thread synchronization correctly, disabling the GIL could actually degrade performance or cause unexpected behavior due to race conditions and other multi-threading related pitfalls.
While there is potential for a performance boost by disabling the GIL in specific scenarios, significant care and expertise are needed to safely and effectively manage true multi-threading in Python without the GIL. For many applications, leveraging other features of Python, like the multiprocessing module or async IO for managing parallel and concurrent tasks, may be a safer and more effective approach.
How do I disable the GIL?
To disable the Global Interpreter Lock (GIL) in Python, you essentially configure and build Python with the --disable-gil flag. As a prerequisite, this method requires compiling Python from source. Additionally, keep in mind that the feature to allow disabling the GIL is in CPython v3.14 possible and onwards. Here are the steps:
1. Download the CPython source file for Python 3.1X or later. Looking more likely to be Python 3.14. You can clone the repository from Github.
git clone -b 3.1X https://github.com/python/cpython.git
2. Go to the downloaded CPython directory.
cd cpython
3. Run the configure script with --disable-gil flag.
./configure --disable-gil
4. Now, build Python.
make
This should not be done lightly as it could potentially lead to threading issues if the code isn't properly thread-safe. This is more suitable for developers who specifically need true multithreading and are prepared to handle the complexities that arise when thread-safety isn't guaranteed by Python. Always make sure your code and any libraries you use are thread-safe before disabling the GIL.
How do I re-enable the GIL with the environment variable?
The Python Global Interpreter Lock (GIL) can be re-enabled by setting an environment variable called PYTHONGIL to 1. This acts as an override to force the GIL back on, even when it has been disabled at build time.
Setting the variable can be done in different ways based on your operating system. Here's how you do it in Unix-based systems including Linux and MacOS:
export PYTHONGIL=1
python my-app.py
For Windows systems, you can set environment variables using the command:
set PYTHONGIL=1
python my-app.py
Keep in mind that these commands will only set the environment variable for the current terminal session. If you want to set it persistently, you would have to add the appropriate command to your shell initialization file (like .bashrc or .bash_profile for bash shell in Unix systems) or set the variable in the system properties on Windows.
Remember that you would need to run your Python program from the same terminal session where you have set this environment variable to see the effect of it on your program.
Are there risks to disabling the GIL?
Yes, there are considerable risks associated with disabling the Global Interpreter Lock (GIL) in Python. The following are the key risks:
Thread-Safety Issues: The GIL provides a degree of thread safety by not allowing multiple threads to execute Python bytecodes simultaneously. By disabling the GIL, the responsibility for ensuring thread safety falls on the developer. This could introduce issues like race conditions, data corruption, and crashes if multiple threads access or modify shared data simultaneously without proper synchronization.
Compatibility Issues: Many Python libraries, including some in the standard library, are written with the assumption that the GIL is enabled. These libraries may use thread-unsafe operations, which are safe under GIL but can cause problems when the GIL is disabled.
Performance Degradation: In some cases, disabling the GIL could actually degrade performance due to the increased overhead of managing multiple threads and synchronizing access to shared data.
Debugging Difficulty: Debugging multi-threaded applications without the GIL can be more complex due to the additional thread synchronization that must be ensured manually.
Risk of Deadlocks: Without the GIL, you may also be more prone to encountering deadlocks, where two or more threads indefinitely wait for the other to release a resource.
Disabling the GIL can potentially boost performance for certain CPU-bound, multi-threaded applications, it is considered a risky move and is typically not recommended unless you are highly familiar with thread synchronization and are prepared to manage the complexities and potential pitfalls of multi-threading.
Do I have alternatives to disabling the GIL?
Yes, there are several other ways to handle concurrent and parallel processing in Python without needing to disable the Global Interpreter Lock (GIL):
Multithreading: For the tasks that are largely I/O bound (like making multiple web requests, reading from a file or database, etc.), Python's threading module can be used to achieve concurrency. The GIL is a bottleneck for CPU-bound tasks, but I/O bound tasks can release the GIL and let other threads run when they are waiting for I/O.
Multiprocessing: The multiprocessing module in Python bypasses the GIL by creating separate Python interpreter processes and therefore allows for true parallelism in CPU-bound tasks. This does come with the overhead of inter-process communication and can use significantly more memory but is a robust way of working around the GIL.
Asynchronous Programming: Python's asyncio module and asynchronous libraries like Twisted and Tornado allow handling of multiple I/O-bound tasks more efficiently by using an event-loop. This model can take full advantage of the wait times inherent in I/O-bound tasks to execute other tasks and hence improve throughput of your program.
NumPy/SciPy for Numeric Processing: These libraries translate much of their computation into compiled code where the GIL can be avoided, making them excellent for numeric computations, and they are well-optimized for vectorized operations.
Cython or C extensions: If performance is critical, you can write C extensions or use Cython to write parts of your code at C-level. In native C code, the GIL can be released, thus alleviating its drawbacks.
Parallel Computing Libraries: Tools such as joblib, Dask, Ray, and others provide higher-level environments for performing parallel computation that can use multiple processors and manage memory sharing efficiently.
The best alternative depends on your specific case and the type of problem you're trying to solve. It's always important to understand the tradeoffs for each option to ensure you make a choice that best matches your requirements.
