T F Overuse of global variables can lead to problems.

Understanding code structure is like mapping the blueprint of a building—it dictates how various components within a program interact and function together. Global variables are like open doors within this blueprint that any room can access. Though they offer convenience, they can disrupt the orderliness of our code.

Think of a project with multiple global variables scattered throughout; it can be like having a puzzle where the pieces are mixed from different sets. A programmer might spend unnecessary time tracking the source and impact of each variable. This clutter not only affects readability but makes maintenance a Herculean task, especially as the codebase grows. Best practices encourage a modular design, where variables have a defined scope and purpose, much like clearly designated spaces within a blueprint for clarity and ease of management.

The risk of unintended modification is akin to a communal kitchen where everyone can add ingredients; without coordination, the final dish can turn into an unintended flavor disaster. In programming, the dish is your program's output, and global variables are the ingredients accessible to everyone.

When different parts of the code change a global variable, tracking the source of an error is like a detective following multiple suspects. It's hard to isolate the change that turned the code sour. These unintentional changes can create bugs that are like hidden culinary mishaps—hard to find and frustrating. This is where 'encapsulation'—a principle of keeping data and methods that manipulate the data within a closed 'container'—comes to the rescue. It ensures changes are localized, predictable, and easier to manage.

Parallel programming lets us cook multiple dishes at once to save time. However, imagine if all chefs had to use the same set of utensils simultaneously. With global variables in parallel programming, this is what happens. They're accessible to every thread in the process, leading to a clash of concurrent operations that can result in data being overwritten or becoming inconsistent; these scenarios are called race conditions.

To prevent this, programmers use synchronization mechanisms like locks, which are like saying 'one chef at a time' for using the shared utensil. However, this adds complexity and can reduce the performance benefits of parallelism. Ideally, minimizing the use of global variables can help ensure that operations are independent and less prone to the pitfalls of concurrency, keeping our parallel kitchen running smoothly and efficiently.