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Mar 31, 2024 · Conclusion. Optimizing Python code involves a nuanced understanding of the language’s execution model, including the implications of the GIL, and the judicious application of profiling tools to ...
- Understanding Python’s GIL
Introduction. Python’s Global Interpreter Lock (GIL) is a...
- Understanding Python’s GIL
Apr 24, 2023 · Understanding Python's Execution Model Before diving into optimization techniques, it's crucial to understand how Python's interpreter and execution model affect performance. Python is an interpreted, high-level programming language, which means that the source code is translated into an intermediate form called bytecode and then executed by the Python virtual machine (PVM).
The execution model of CPython can be summarised as follows: Source Code: The Python source code (.py files) is written by the programmer. AST: The source code is parsed into an Abstract Syntax Tree, representing the syntactical structure. Bytecode Compilation: The AST is compiled into bytecode, a lower-level, platform-independent code.
- OS Threads, Python Threads, and The Gil
- The Effects of The Gil
- The Convoy Effect
- Fixing The Convoy Effect
- A Proper Gil
- Can't We Remove The Gil?
- The Future of The Gil and Python Concurrency
- P.S.
- The Implementation Details of The Gil *
Let me first remind you what Python threads are and how multithreading works in Python. When you run the python executable, the OS starts a new process with one thread of execution called the main thread. As in the case of any other C program, the main thread begins executing python by entering its main()function. All the main thread does next can ...
The first effect of the GIL is well-known: multiple Python threads cannot run in parallel. Thus, a multi-threaded program is not faster than its single-threaded equivalent even on a multi-core machine. As an naive attempt to parallelize Python code, consider the following CPU-bound function that performs the decrement operation a given number of ti...
The convoy effect takes place because each time the I/O-bound thread performs an I/O operation, it releases the GIL, and when it tries to reacquire the GIL after the operation, the GIL is likely to be already taken by the CPU-bound thread. So the I/O-bound thread must wait for at least 5 ms before it can set eval_breaker and gil_drop_requestto forc...
Since the problem is that the I/O-bound thread waits for the switch interval until it requests the GIL, we may try to set the switch interval to a smaller value. Python provides the sys.setswitchinterval(interval) function for that purpose. The interval argument is a floating-point value representing seconds. The switch interval is measured in micr...
The fundamental problem with the GIL is that it interferes with the OS scheduler. Ideally, you would like to run an I/O-bound thread as soon the I/O operation it waits for completes. And that's what the OS scheduler usually does. In CPython, however, the thread then immediately gets stuck waiting for the GIL, so the OS scheduler's decision doesn't ...
The first step to remove the GIL is to understand why it exists. Think why you would typically use locks in a multi-threaded program, and you'll get the answer. It's to prevent race conditions and make certain operations atomic from the perspective of other threads. Say you have a sequence of statements that modifies some data structure. If you don...
This sounds scary, but it's much more probable that CPython will have many GILs than no GIL at all. Literally, there is an initiative to introduce multiple GILs to CPython. It's called subinterpreters. The idea is to have multiple interpreters within the same process. Threads within one interpreter still share the GIL, but multiple interpreters can...
The benchmarks used in this post are available on GitHub. Special thanks to David Beazley for his amazing talks. Larry Hastings' talks on the GIL and Gilectomy (one, two, three) were also very interesting to watch. To understand how modern OS schedulers work, I've read Robert Love's book Linux Kernel Development. Highly recommend it! If you want to...
Technically, the GIL is a flag indicating whether the GIL is locked or not, a set of mutexes and conditional variables that control how this flag is set, and some other utility variables like the switch interval. All these things are stored in the _gil_runtime_statestruct: The _gil_runtime_state stuct is a part of the global state. It's stored in t...
Jun 5, 2024 · Introduction. Python’s Global Interpreter Lock (GIL) is a fundamental concept that often comes up in discussions about Python’s performance and multi-threading capabilities. In this article ...
Feb 18, 2024 · While it does impact certain scenarios, Python’s performance is influenced by various other factors, such as algorithmic complexity, hardware capabilities, and code optimization. GIL and Multithreading. The GIL does not prevent multithreading in Python. It simply limits the parallel execution of Python bytecode.
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Sep 22, 2024 · The GIL is a mechanism that has sparked countless debates among developers, especially those focused on performance and multi-threaded applications. In this article, we will dive deep into what the Global Interpreter Lock (GIL) is, why it exists, and most importantly, how it affects concurrency in Python. We'll also explore the challenges posed ...
