gil | 易学教程

How asyncio.sleep isn't blocking thread?

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问题 I'm reading 'Fluent Python' by 'Luciano Ramalho' over and over, but I couldn't understand asyncio.sleep's behavior inside asyncio. Book says at one part: Never use time.sleep in asyncio coroutines unless you want to block the main thread, therefore freezing the event loop and probably the whole application as well. (...) it should yield from asyncio.sleep(DELAY). On the other part: Every Blocking I/O function in the Python standard library releases the GIL (...) The time.sleep() function also

Can you race condition in Python while there is a GIL?

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问题 My understanding is that due to the Global Interpreter Lock in cPython, only one thread can ever be executed at any one time. Does this or does this not automatically protected against race conditions, such as the lost update problem? Just to be clear, I am asking from a theoretical perspective. I would never write threaded code without synchronization. 回答1: Due to the GIL, there is only ever one thread per process active to execute Python bytecode; the bytecode evaluation loop is protected

Does new implementation of GIL in Python handled race condition issue?

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问题 I've read an article about multithreading in Python where they trying to use Synchronization to solve race condition issue. And I've run the example code below to reproduce race condition issue: import threading # global variable x x = 0 def increment(): """ function to increment global variable x """ global x x += 1 def thread_task(): """ task for thread calls increment function 100000 times. """ for _ in range(100000): increment() def main_task(): global x # setting global variable x as 0 x

Multi-threading and Single-threading performance issues in CPU-bound task

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问题 The two following single-threading and multi-threading scripts are taking the same time when I give as input a big number like 555550000 single thread import threading, time a=[] def print_factors(x): for i in range(1, x + 1): if x % i == 0: a.append(i) n=int(input("Please enter a large number")) print ("Starting time is %s" % ( time.ctime(time.time()) )) print("The factors of",n,"are:") thread = threading.Thread(target=print_factors,args=(n,)) thread.start() thread.join() print("Finishing

Calling python functions from MATLAB workers

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问题 I'm trying to run a MATLAB script using parfor which runs a simulink model in parallel by sim function, and this simulink model contains a MATLAB Function Block. The function defined in this block calls a python function using "py." mechanism. Unfortunately, the run sometimes seems stuck in the middle, when I fed a large number of simulations. The debugging output stops to show up. I suspect a deadlock somewhere. So, my questions are Can we run a simulink model which contains python functions

How to invoke Python function as a callback inside C++ thread using pybind11

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问题 I designed a C++ system that invokes user defined callbacks from procedure running in a separate thread. Simplified system.hpp looks like this: #pragma once #include <atomic> #include <chrono> #include <functional> #include <thread> class System { public: using Callback = std::function<void(int)>; System(): t_(), cb_(), stop_(true) {} ~System() { stop(); } bool start() { if (t_.joinable()) return false; stop_ = false; t_ = std::thread([this]() { while (!stop_) { std::this_thread::sleep_for

How to invoke Python function as a callback inside C++ thread using pybind11

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Why does time.sleep(…) not get affected by the GIL?

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问题 From what I understood when doing research on the Python GIL, is that only one thread can be executed at the once (Whoever holds the lock). However, if that is true, then why would this code only take 3 seconds to execute, rather than 15 seconds? import threading import time def worker(): """thread worker function""" time.sleep(3) print 'Worker' for i in range(5): t = threading.Thread(target=worker) t.start() By intuition about threads, I would have thought this would take 3 seconds, which it

Pybind11 Parallel-Processing Issue in Concurrency::parallel_for

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问题 I have a python code that performs filtering on a matrix. I have created a C++ interface using pybind11 that successfully runs in serialized fashion (please see the code in below). I am trying to make it parallel-processing to hopefully reduce the computation time compared to its serialized version. To do this, I have splitted my array of size M×N into three sub-matrices of size M×(N/3) to process them in parallel using the same interface. I used ppl.h library to make a parallel for-loop and

并发编程-多线程,GIL锁

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本章内容: 1.什么是GIL 2.GIL带来的问题 3.为什么需要GIL 4.关于GIL的性能讨论 5.自定义的线程互斥锁与GIL的区别 6.线程池与进程池 7.同步异步，阻塞非阻塞一.什么是GIL 官方解释: ''' In CPython, the global interpreter lock, or GIL, is a mutex that prevents multiple native threads from executing Python bytecodes at once. This lock is necessary mainly because CPython’s memory management is not thread-safe. (However, since the GIL exists, other features have grown to depend on the guarantees that it enforces.) ''' 释义: 在CPython中，这个全局解释器锁，也称为GIL,是一个互斥锁,防止多个线程在同一时间执行Python字节码,这个锁是非常重要的,因为CPython的内存管理非线程安全的,很多其他的特性依赖于GIL,所以即使它影响了程序效率也无法将其直接去除总结: 在CPython中,GIL会把线程的并行变成串行