Multithreading in Python with Global Interpreter Lock (GIL) Example

The python programming language allows you to use multiprocessing or multithreading.In this tutorial, you will learn how to write multithreaded applications in Python.

What is a Thread?

A thread is a unit of exection on concurrent programming. Multithreading is a technique which allows a CPU to execute many tasks of one process at the same time. These threads can execute individually while sharing their process resources.

What is a Process?

A process is basically the program in execution. When you start an application in your computer (like a browser or text editor), the operating system creates a process.

What is Multithreading?

Multithreading is a technique which allows a CPU to execute multiple threads at the same time. These threads can execute individually while sharing their process resources.

What is Multiprocessing?

Multiprocessing allows you to run multiple unrelated processes simultaneously. These processes do not share their resources and communicate through IPC.

Python Multithreading vs Multiprocessing

To understand processes and threads, consider this scenario: An .exe file on your computer is a program. When you open it, the OS loads it into memory, and the CPU executes it. The instance of the program which is now running is called the process.

Every process will have 2 fundamental components:

  • The Code
  • The Data

Now, a process can contain one or more sub-parts called threads. This depends on the OS architecture,.You can think about a thread as a section of the process which can be executed separately by the operating system.

In other words, it is a stream of instructions which can be run independently by the OS. Threads within a single process share the data of that process and are designed to work together for facilitating parallelism.

In this tutorial, you will learn,

Why use Multithreading?

Multithreading allows you to break down an application into multiple sub-tasks and run these tasks simultaneously. If you use multithreading properly, your application speed, performance, and rendering can all be improved.

Python MultiThreading

Python supports constructs for both multiprocessing as well as multithreading. In this tutorial, you will primarily be focusing on implementing multithreaded applications with python. There are two main modules which can be used to handle threads in Python:

Now, let’s try using the threading module to implement your previous example. Again, fire up your IDLE and type in the following:

At any given time, a philosopher must either be eating or thinking.

Moreover, a philosopher must take the two forks adjacent to him (i.e., the left and right forks) before he can eat the spaghetti. The problem of deadlock occurs when all five philosophers pick up their right forks simultaneously.

Since each of the philosophers has one fork, they will all wait for the others to put their fork down. As a result, none of them will be able to eat spaghetti.

Similarly, in a concurrent system, a deadlock occurs when different threads or processes (philosophers) try to acquire the shared system resources (forks) at the same time. As a result, none of the processes get a chance to execute as they are waiting for another resource held by some other process.

Race Conditions

A race condition is an unwanted state of a program which occurs when a system performs two or more operations simultaneously. For example, consider this simple for loop: