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The main thread takes the GIL during the initialization, so it's free to enter. The eval_breaker flag tells the GIL-holding thread to suspend bytecode execution, and gil_drop_request explains why. This is most probably not an accurate measure since the client and the server run on the same machine, but that's not the point. Does it mean that we are to live with the GIL forever? Copyright (c) 2001-2022 Python Software Foundation. They are called by the GIL-holding thread when it suspends bytecode execution: On Unix-like systems the implementation of the GIL relies on primitives provided by the pthreads library. The conclusion here is that it's possible to speed up CPU-intensive Python code using multithreading if the code calls C functions that release the GIL. There is also the taskset command that allows you to set the CPU affinity of a process without touching the source code at all. The newly created thread starts executing the t_bootstrap() function with the boot argument. So what CPython does is put those things in a thread state struct and associate the thread state with the OS thread. In short, they work as follows. Here's how this logic is implemented in the code: In a single-threaded Python program, the main thread is the only thread, and it never releases the GIL. I'll try to keep track of important changes and add update notes. It's stored in the _ceval_runtime_state struct, which in turn is a part of _PyRuntimeState that all Python threads have an access to: Note that _gilstate_runtime_state is a struct different from _gil_runtime_state. Thus, they don't need to use additional locking to make the code thread-safe. You can now choose to sort by Trending, which boosts votes that have happened recently, helping to surface more up-to-date answers. The second way to fix the convoy effect is even more hacky. To learn more about the project and the ideas behind it, see the design document and the GitHub repo. Strictly speaking, CPython supports multi-io-bound-thread + single-cpu-bound-thread. How do I check if directory exists in Python? The team has no plans to change or remove the GIL. If you don't surround the sequence with a lock, then another thread can access the data structure somewhere in the middle of the modification and get a broken incomplete view. They are not exposed in Python, but C extensions can use them via the Python/C API. The mutex ensures that the awaiting thread doesn't miss the condition going from false to true. These is also a nice writeup on LWN. To understand how modern OS schedulers work, I've read Robert Love's book Linux Kernel Development. */, /* This condition variable allows one or several threads to wait, until the GIL is released. Let me first remind you what Python threads are and how multithreading works in Python. The GIL never had a huge fanbase. How to clamp an e-bike on a repair stand? These function also take care of setting gilstate->tstate_current. The I/O-bound thread can be scheduled only when the I/O operation completes, so it has less chances to take the GIL first. If not the GIL, some decrements could overwrite each other and the object would stay in memory forever. In other words, Python thread = OS thread + Python thread state. So CPython applies the release-perform-acquire pattern not just to I/O operations but also to other blocking calls into the OS like select() and pthread_mutex_lock(), and to heavy computations in pure C. For example, hash functions in the hashlib standard module release the GIL. The proof-of-concept comes with a modified bundled "pip" that includes an alternative package index. More on why we might need to do that later. Then it waits till the request to the database is written into the socket opened to the DB. Here are two ways to fix it. But some I/O operations are really fast. The GIL has long been seen as an obstacle to better multithreaded performance in CPython (and thus Python generally). The conclusion is that changing the switch interval is an option for fixing the convoy effect, but you should be careful to measure how the change affects your application. It's called subinterpreters. The echo server without the CPU-bound thread handles 30k RPS, which means that a single request takes about 1/30k 30 s. Ideally, you would like to run an I/O-bound thread as soon the I/O operation it waits for completes. In addition, the mutex also protects, /* This condition variable helps the GIL-releasing thread wait for, a GIL-awaiting thread to be scheduled and take the GIL. The first effect of the GIL is well-known: multiple Python threads cannot run in parallel. Atomic increments and decrements alone added about 30% overhead. Recall the countdown() function. The proper solution is to differentiate between the threads. Is 'Koi no Summer Vacation' better translated as 'Love of Summer Vacation' instead of 'Summer Vacation of Love'? It's to prevent race conditions and make certain operations atomic from the perspective of other threads. We may run countdown(100_000_000) in a single thread, or countdown(50_000_000) in two threads, or countdown(25_000_000) in four threads, and so forth. I'm looking into multi-processing and I'll probably have to use it. When it enters the loop, it just starts executing bytecode instructions one by one according to the switch. To help you with this venture, I wrote the following bonus section. 464). Similarly, the GIL allows threads to safely access global and interpreter-wide data: loaded modules, preallocated objects, interned strings as so on. Update from October 7, 2021: [1] Restricting threads to one core doesn't really fix the convoy effect. 2x less? Here's the RPS I get if I vary the switch interval and the number of CPU threads: Smaller switch intervals make I/O-bound threads more responsive. Update from October 7, 2021: I've now learned that restricting threads to one core helps with the convoy effect only when the client is restricted to the same core, which is how I set up the benchmark. The fix is more of a hack. The GIL is so helpful because CPython increments and decrements integers that can be shared between threads all over the place. Why hasn't it been merged? These calls atomically unlock the mutex and make the thread block. The GIL-holding thread sees the flags when it starts the next iteration of the evaluation loop and releases the GIL. The switch interval is measured in microseconds, so the smallest value is 0.000001. One of Pythons long-standing weaknesses, its inability to scale well in multithreaded environments, is the target of a new proposal among the core developers of the popular programming language. A few actually do that, though, mod_wsgi being a notable example. First you need to install pyenv on Linux or on macOS . Waiting for IO to complete may take 90% (or more) of the time the request is processed. I never ran into it, nor could I find evidence that anyone else did. If the switch occurred, this means that another thread took the GIL, and it's fine to compete for the GIL again. Along the way, we'll discuss what the GIL really is, why it exists, how it works, and how it's going to affect Python concurrency in the future. As we add one CPU-bound thread, the RPS drops significantly. */. Many efforts have been made to remove it over the years, but at the cost of hurting single-threaded performancein other words, by making the vast majority of existing Python applications slower. This is a proof-of-concept implementation of CPython that supports multithreading without the global interpreter lock (GIL). In October 2020, Mark Shannon proposed a plan to make CPython 5x faster over several years. It seems useless if the threaded code has equivalent speed to a normal program. But if we decrease the switch interval, we will see a slowdown. This is where multithreading helps: another thread can run in the meantime. Given that IO operation is order of magnitude slower than CPU operation most of the time such application is waiting for IO to complete. Copyright 2021 IDG Communications, Inc. If the operation is really fast such as a non-blocking send(), the chances are actually quite good but only on a single-core machine where the OS has to decide which thread to schedule. The boot argument is a struct that contains the target function, the passed arguments, and a thread state for the new OS thread. How can I create and update the existing SPF record to allow more than 10 entries? There is no way around it. It waits for a fixed time interval called the switch interval (5 ms by default), and if the GIL is not released during that time, it sets the eval_breaker and gil_drop_request flags. pthread_setaffinity_np() is a C function. Multithreaded performance, on some benchmarks, scales almost linearly with each new thread in the best casee.g., when using 20 threads, an 18.1 speedup on one benchmark and a 19.8 speedup on another. Let's now see what happens in a multi-threaded program. So, one can conclude that the python threading module is useful when writing IO bound programs. Developer Sam Grosshas proposeda major change to the Global Interpreter Lock, or GILa key component in CPython, the reference implementation of Python. Also, you don't need to modify the CPython source code or mess with ctypes to restrict Python threads to certain cores. When the reference count reaches zero, the object is deallocated. To call it from Python, you may use something like ctypes. What's the canonical way to check for type in Python? See the discussion for more details. The benchmarks used in this post are available on GitHub. I/O bound method: file.open, file.write, file.read, socket.send, socket.recv, etc. When Python calls these I/O functions, it will release GIL and acquire GIL after I/O function returns implicitly. Gilectomy could run some Python code and run it in parallel. The overall effect of this change, and a number of others with it, actually boosts single-threaded performance slightlyby around 10%, according to some benchmarks performed on a forked version of the interpreter versus the mainline CPython 3.9 interpreter. Moreover, if we start two, three, or four CPU-bound processes, the RPS stays about the same. Some implementation details will certainly change as CPython evolves. These conditional variables are protected by two mutexes: gil->mutex and gil->switch_mutex. A small switch interval and several threads is when you get poor performance. Now, how much is 5 ms? When single threaded application is waiting on IO it just not using the core and the core is available for execution. From what I understand, the GIL makes it impossible to have threads that harness a core each individually. 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. What is the Python 3 equivalent of "python -m SimpleHTTPServer". There were similar projects before, but they failed because they lacked proper funding or expertise. The patch worked, but a scheduler is never a trivial thing, so merging it to CPython required a lot of effort. In the language without the GIL like C, we would see a speedup as we increase the number of threads. All global state is made per-interpreter, and interpreters communicate via message passing only. What we've seen today only makes it worse. This can change only when all the global state is made per-interpreter.