Concurrent Model

本文综述了多种并发编程模型,包括但不限于线程与互斥锁、事务内存、消息传递、进程间通信等,并探讨了Go语言中的并发实现,以及如Actor模型、Future/Promise、协程、事件驱动等高级并发概念。
As an AI language model, I do not have access to the `concurrent` module or any other modules of Python. However, I can provide a brief explanation of what the `concurrent` module is and what it is used for. The `concurrent` module is a built-in Python module that provides a high-level interface for asynchronously executing functions using threads or processes. It includes the `futures` module, which provides a way to execute a function asynchronously and retrieve its result later. The `concurrent.futures` module is typically used to speed up IO-bound tasks, such as network requests, by allowing multiple requests to be executed in parallel. The `concurrent.futures` module provides two classes for submitting tasks: `ThreadPoolExecutor` and `ProcessPoolExecutor`. Both classes provide a `submit()` method that takes a function and its arguments as input and returns a `Future` object. A `Future` object represents the result of an asynchronous computation that has not yet completed. The `Future` object can be used to check if the computation is complete and retrieve the result when it is. The `concurrent.futures` module also provides a `wait()` function that can be used to wait for multiple `Future` objects to complete. The `wait()` function takes a list of `Future` objects and blocks until all of them have completed. Overall, the `concurrent.futures` module provides a simple and convenient way to execute functions asynchronously and retrieve their results. It can be a useful tool for speeding up IO-bound tasks and improving the performance of Python applications.
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