gil

I'm trying to make a memoize decorator that works with multiple threads. I understood that I need to use the cache as a shared object between the threads, and acquire/lock the shared object. I'm of course launching the threads: for i in range(5): thread = threading.Thread(target=self.worker, args=(self.call_queue,)) thread.daemon = True thread.start() where worker is: def worker(self, call): func, args, kwargs = call.get() self.returns.put(func(*args, **kwargs)) call.task_done() The problem starts, of course, when I'm sending a function decorated with a memo function (like this ) to many

Since Python has some issues with GIL, Java is better for developing multiprocessing applications. Could you please justify the exact reasoning of java's effective processing than python in your way? Joachim Sauer The biggest problem in multithreading in CPython is the Global Interpreter Lock (GIL) (note that other Python implementations don't necessarily share this problem!) The GIL is an implementation detail that effectively prevents parallel (simultaneous) execution of separate threads in Python. The problem is that whenever Python byte code is to be executed, then the current thread must

I would like to separate my sigle-thread application to number of working threads. Just 1 question - what about performance of this action? If GIL prevents python from executing more than 1 thread at the time will I have any profit? Another point (from c/c++ point of view) - as I know each thread, anyway, can be only executed exclusively, so in the lower level than python interpreter I have the same limitation. Summary: Will the the python threads have lesser efficiency that 'native' thread in part of task switching? Don't worry about the GIL. Depending on the kinds of things your program does

I often see people talking that the GIL is per Python Interpreter (even here on stackoverflow). But what I see in the source code it seems to be that the GIL is a global variable and therefore there is one GIL for all Interpreters in each python process. I know they did this because there is no interpreter object passed around like lua or TCL does, it was just not designed well in the beginning. And thread local storage seems to be not portable for the python guys to use. Is this correct? I had a short look at the 2.4 version I'm using in a project here. Had this changed in later versions,

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: What is a global interpreter lock and why is it an issue? A lot of noise has been made around removing the GIL from Python, and I'd like to understand why that is so important. I have never written a compiler nor an interpreter myself, so don't be frugal with details, I'll probably need them to understand. 回答1: Python's GIL is intended to serialize access to interpreter internals from different threads. On multi-core systems, it means that multiple threads can't effectively make use of multiple cores. (If the GIL didn't lead to