STL: Rethinking std::list::sort()
Traditionally, std::list::sort() implemented a bottom-up Merge Sort algorithm using pointers. However, starting with Visual Studio 2015, the standard library switched to a top-down Merge Sort strategy. Despite the initial perception of inefficiency due to repeated sequential scans on each level of recursion, a closer examination of the code reveals a different story.
The Top-Down Approach and its Benefits
Instead of scanning the list to split it, the top-down approach recursively divides the integer size by 2, enabling faster merging by reducing the number of element comparisons. Additionally, the initial use of std::next to locate the midpoint may seem inefficient, but it takes advantage of the list's properties to efficiently divide the list in half.
The change to using iterators avoids memory allocation and ensures exception safety. If a compare function throws an exception, the list will remain ordered without losing any data. The use of std::list::splice in the merge logic allows for efficient movement of nodes within the original list, further enhancing its stability and exception handling.
Performance Considerations
Contrary to initial assumptions, top-down Merge Sort in std::list::sort() often outperforms bottom-up Merge Sort in certain scenarios. For lists with scattered nodes or when memory is limited, top-down Merge Sort exhibits better cache behavior, resulting in faster execution. However, if memory is plentiful, moving the list to an array or vector and sorting it in that format is generally more efficient.
Alternative Bottom-Up Merge Sort with Iterators
Despite the efficiency of the top-down approach, some have sought to modify the bottom-up Merge Sort to work with iterators, eliminating the need for an array of lists. This approach utilizes an array of iterators to track sorted run boundaries and employs std::list::splice for merging, achieving similar results to the top-down approach.
Conclusion
The switch to a top-down Merge Sort in std::list::sort() was not a hasty decision but a carefully considered optimization that yielded significant performance and stability improvements. While the top-down approach may not always be ideal, it has proven its worth in certain scenarios, offering a faster and more reliable sorting algorithm.
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