Multiprocessing, multithreading, asynchrony in Python and more. What is it and how does it work?

This video discusses the concepts of concurrency, processes, asynchrony, and multi-threading in modern programming. It explores how these elements work together and the different approaches to synchronous programming versus multi-threaded programming.

Python and how multitasking is implemented on a single core. Let's imagine an old computer with just one processor, where the operating system runs multiple processes simultaneously. These processes appear to run concurrently, but in reality, they are executed sequentially due to context switching.

Evolution of Processor Clock Speed The clock speed of processors has evolved from tens of megahertz in the 90s to several gigahertz today. Intel developed a processor with a clock speed of 1 gigahertz, and gradually increased it to multiple megahertz. However, further increases become challenging due to cost and complexity.

Multi-Core Processors Multi-core processors contain multiple computational cores on one chip, allowing each core to execute different processes simultaneously. For example, a processor with four computational cores can concurrently handle four different processes at the same time.

Threads are the simultaneous execution of tasks on a computer. They are essential for parallel operations, allowing multiple instructions to be executed at once. Each process has at least one main thread that executes the program's instructions, while additional threads can be launched for parallel computations.

A race condition can speed up the entire system but also cause problems. In multithreaded programming, it often leads to a situation where the logic of program execution depends on the order of code execution in different threads. This can result in one thread changing data while another reads it without considering that the data may have been modified by another thread at that moment.

Efficiency of Processor Cores Hyper-Threading and multi-threaded programs. Each processor core executes a specific thread at any given time, but some blocks remain idle, reducing the core's utilization.

Logical vs Physical Cores Logical cores vs. physical cores in processors. The operating system sees logical cores as threads, so 6 physical cores with Hyper-Threading appear as 12 logical ones.

Multithreading on multi-core CPUs allows for the efficient utilization of computer resources. For example, a processor with four cores can run multiple threads simultaneously, improving overall performance.

Python's Global Interpreter Lock (GIL) Python's Global Interpreter Lock (GIL) prevents simultaneous execution of multiple threads in Python, making it challenging to effectively utilize multi-core processors.

Comparison with Jazz Cafe Despite criticism, the GIL is similar to the scheme used in Jazz Cafe and does not hinder writing high-performance network applications.

To handle thousands of HTTP requests, JavaScript and Python can be used. However, there are limitations in tracking the activities to avoid race conditions and creating a large number of threads due to hardware constraints. The goal is to parallelize processes for potentially serving thousands of clients simultaneously.

Asynchronous Programming Asynchronous programming allows handling multiple tasks simultaneously within a single process or thread. It prevents the server from waiting for data and enables it to process other tasks while waiting, leading to more efficient programs.

Cooperative Multitasking Cooperative multitasking in asynchronous programming allows a single thread to handle numerous client requests without blocking its execution. This approach enables one thread to efficiently manage a large number of clients by not blocking its execution while waiting for responses from the database.

Concurrency and Scalability The use of asynchronous programming facilitates concurrency and scalability by allowing multiple processes or threads within a program, each capable of handling numerous client requests concurrently without blocking their own execution. This approach leads to highly effective and scalable modern applications.

In this video, we'll explore the specific implementation of event loops in Python for handling asynchronous backend programming. The event loop tracks tasks and executes them until they are put on hold, allowing efficient program execution especially for applications working intensively with databases.

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