Concurrency Concepts
Explore the foundational concepts of concurrency and parallelism in Python programming. Learn to distinguish between overlapping task execution and simultaneous processing. Understand how these concepts impact the design of responsive applications and when to apply concurrency or parallelism based on I/O or CPU-bound workloads.
So far, the programs in this course have executed sequentially. In this model, each statement completes before the next statement executes. This linear model is straightforward and predictable, and it works well for small scripts and controlled workflows.
Modern applications, however, rarely operate under such simple conditions. For example, a web browser must download resources while remaining responsive to user input, and a game engine must render graphics continuously while saving state in the background. If these tasks were executed strictly one after another, the application would appear frozen or unresponsive.
To design responsive and high-performance systems, we must learn how to structure programs so that multiple tasks can make progress without blocking one another. Before introducing threads or asynchronous constructs, it is essential to understand the conceptual distinction between concurrency and parallelism. This theoretical foundation is critical for writing correct and efficient concurrent software.
Concurrency vs. parallelism
In everyday discussion, “concurrency” and “parallelism” are often used interchangeably. In computer science, however, they refer to distinct concepts. Understanding this distinction is important when designing efficient concurrent programs.
Concurrency is about structure. It is the ability of a program to handle multiple tasks in overlapping time periods. A concurrent program is designed so that tasks can start, run, and complete in ...