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Multithreading is a programming technique in which multiple threads of execution run within the same process. A thread is a lightweight unit of execution that can perform a particular task independently while sharing the process's memory and other resources with other threads. In the context of Data Structures and Algorithms, multithreading is important because it demonstrates how computational tasks can be divided into smaller concurrent activities and how shared data structures or resources can be protected from simultaneous modification. A process may contain one or more threads. When a program has only one thread, its operations are generally performed sequentially. With multithreading, two or more threads can make progress concurrently. For example, one thread can perform a searching operation while another performs a different computation. Threads within the same process share variables, objects, memory, and other resources. This sharing makes communication between threads relatively convenient, but it also creates the possibility of synchronization problems. Python supports multithreading through the built-in `threading` module. The module provides classes and functions for creating, starting, coordinating, and synchronizing threads. Important components include the `Thread` class, `Lock`, `RLock`, `Semaphore`, `Event`, and `Condition`. Among these, `Lock` is one of the fundamental synchronization primitives used to protect a shared resource. A major issue in multithreaded programming is a race condition. ______ ______ _____ ______ __________ ___ _________ _______ _______ _____ _________ ________.
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