## 问题 You’ve heard about the Global Interpreter Lock (GIL), and are worried that it might beaffecting the performance of your multithreaded program. ## 解决方案 Although Python fully supports thread programming, parts of the C implementationof the interpreter are not entirely thread safe to a level of allowing fully concurrentexecution. In fact, the interpreter is protected by a so-called Global Interpreter Lock(GIL) that only allows one Python thread to execute at any given time. The most no‐ticeable effect of the GIL is that multithreaded Python programs are not able to fullytake advantage of multiple CPU cores (e.g., a computationally intensive applicationusing more than one thread only runs on a single CPU). Before discussing common GIL workarounds, it is important to emphasize that the GILtends to only affect programs that are heavily CPU bound (i.e., dominated by compu‐tation). If your program is mostly doing I/O, such as network communication, threadsare often a sensible choice because they’re mostly going to spend their time sittingaround waiting. In fact, you can create thousands of Python threads with barely a con‐cern. Modern operating systems have no trouble running with that many threads, soit’s simply not something you should worry much about.For CPU-bound programs, you really need to study the nature of the computation beingperformed. For instance, careful choice of the underlying algorithm may produce a fargreater speedup than trying to parallelize an unoptimal algorithm with threads. Simi‐larly, given that Python is interpreted, you might get a far greater speedup simply bymoving performance-critical code into a C extension module. Extensions such asNumPy are also highly effective at speeding up certain kinds of calculations involvingarray data. Last, but not least, you might investigate alternative implementations, suchas PyPy, which features optimizations such as a JIT compiler (although, as of this writing,it does not yet support Python 3).It’s also worth noting that threads are not necessarily used exclusively for performance.A CPU-bound program might be using threads to manage a graphical user interface, anetwork connection, or provide some other kind of service. In this case, the GIL canactually present more of a problem, since code that holds it for an excessively long periodwill cause annoying stalls in the non-CPU-bound threads. In fact, a poorly written Cextension can actually make this problem worse, even though the computation part ofthe code might run faster than before.Having said all of this, there are two common strategies for working around the limi‐tations of the GIL. First, if you are working entirely in Python, you can use the multiprocessing module to create a process pool and use it like a co-processor. For example,suppose you have the following thread code: # Performs a large calculation (CPU bound)def some_work(args): > ...return result # A thread that calls the above functiondef some_thread(): > while True:...r = some_work(args)... Here’s how you would modify the code to use a pool: # Processing pool (see below for initiazation)pool = None # Performs a large calculation (CPU bound)def some_work(args): > ...return result # A thread that calls the above functiondef some_thread(): > while True:...r = pool.apply(some_work, (args))... # Initiaze the poolif __name__ == ‘__main__': > import multiprocessingpool = multiprocessing.Pool() This example with a pool works around the GIL using a neat trick. Whenever a threadwants to perform CPU-intensive work, it hands the work to the pool. The pool, in turn,hands the work to a separate Python interpreter running in a different process. Whilethe thread is waiting for the result, it releases the GIL. Moreover, because the calculationis being performed in a separate interpreter, it’s no longer bound by the restrictions ofthe GIL. On a multicore system, you’ll find that this technique easily allows you to takeadvantage of all the CPUs.The second strategy for working around the GIL is to focus on C extension program‐ming. The general idea is to move computationally intensive tasks to C, independent ofPython, and have the C code release the GIL while it’s working. This is done by insertingspecial macros into the C code like this: #include “Python.h”... PyObject [*](#)pyfunc(PyObject [*](#)self, PyObject [*](#)args) {...Py_BEGIN_ALLOW_THREADS// Threaded C code...Py_END_ALLOW_THREADS... } If you are using other tools to access C, such as the ctypes library or Cython, you maynot need to do anything. For example, ctypes releases the GIL when calling into C bydefault. ## 讨论 Many programmers, when faced with thread performance problems, are quick to blamethe GIL for all of their ills. However, doing so is shortsighted and naive. Just as a real- world example, mysterious “stalls” in a multithreaded network program might be causedby something entirely different (e.g., a stalled DNS lookup) rather than anything relatedto the GIL. The bottom line is that you really need to study your code to know if theGIL is an issue or not. Again, realize that the GIL is mostly concerned with CPU-boundprocessing, not I/O.If you are going to use a process pool as a workaround, be aware that doing so involvesdata serialization and communication with a different Python interpreter. For this towork, the operation to be performed needs to be contained within a Python functiondefined by the def statement (i.e., no lambdas, closures, callable instances, etc.), and thefunction arguments and return value must be compatible with pickle. Also, the amountof work to be performed must be sufficiently large to make up for the extra communi‐cation overhead.Another subtle aspect of pools is that mixing threads and process pools together can bea good way to make your head explode. If you are going to use both of these featurestogether, it is often best to create the process pool as a singleton at program startup,prior to the creation of any threads. Threads will then use the same process pool for allof their computationally intensive work.For C extensions, the most important feature is maintaining isolation from the Pythoninterpreter process. That is, if you’re going to offload work from Python to C, you needto make sure the C code operates independently of Python. This means using no Pythondata structures and making no calls to Python’s C API. Another consideration is thatyou want to make sure your C extension does enough work to make it all worthwhile.That is, it’s much better if the extension can perform millions of calculations as opposedto just a few small calculations.Needless to say, these solutions to working around the GIL don’t apply to all possibleproblems. For instance, certain kinds of applications don’t work well if separated intomultiple processes, nor may you want to code parts in C. For these kinds of applications,you may have to come up with your own solution (e.g., multiple processes accessingshared memory regions, multiple interpreters running in the same process, etc.). Al‐ternatively, you might look at some other implementations of the interpreter, such asPyPy.See Recipes 15.7 and 15.10 for additional information on releasing the GIL in Cextensions.