What does space complexity measure in an algorithm?

Space complexity measures the total amount of memory space that an algorithm requires relative to the input size. It encompasses both the fixed part of the memory allocated (which includes constants, simple variables, fixed-size variable components, etc.) and the variable part, which is determined by the dynamics of the algorithm, such as data structures utilized, recursion stack space, and other temporary space used during execution.

This measurement is crucial because it helps in analyzing how efficiently an algorithm utilizes memory resources, particularly as the size of the input grows. For instance, an algorithm might be efficient in terms of time complexity but can become impractical if it demands excessive memory space, limiting its application on systems with constrained resources.

Thus, the focus on memory usage directly aligns with the accepted definition of space complexity, making it the correct interpretation in this context. Conversely, the other options refer to unrelated aspects of algorithm performance, such as code length, CPU time, or processing capacity, which do not fit the definition of space complexity.