Which variable is commonly used to eliminate race conditions?

The use of a functional global variable is an effective method for eliminating race conditions in LabVIEW programming. A functional global variable, also known as a clustered variable, encapsulates data within a VI that serves as a single point of access. This means that multiple callers can access the data without causing conflicts or inconsistencies in the value, thus preventing race conditions that occur when multiple parts of a program attempt to read from or write to the same variable concurrently.

Functional global variables manage access to their data through the use of special constructs such as data acquisition logic that ensures that readings and writes happen in a controlled sequence. This means that only one section of the code can modify the variable at a time, which ensures data integrity and state management over time.

In contrast, local variables, global variables, and shared variables do not inherently manage access and can lead to conditions where multiple parts of the code access or modify data simultaneously, potentially resulting in unpredictable behavior of the program due to race conditions. Thus, using a functional global variable not only provides a mechanism to share data across different parts of the application but also incorporates safeguards to maintain data consistency.