Which of the following scenarios would be an ideal use case for external sorting?
Reordering a linked list in a real-time graphics engine
Sorting a list of recently accessed files by timestamp
Sorting a small array of integers within a mobile app
Generating a leaderboard from a massive online gaming database
Why is the choice of the number of ways in multiway merge sort a trade-off?
Lower ways improve cache locality but decrease sorting speed.
Lower ways are faster for small datasets but slower for large ones.
Higher ways simplify the algorithm but limit dataset size.
Higher ways reduce disk I/O but increase memory usage.
What is the worst-case time complexity of Timsort, and how does it compare to the worst-case complexities of Merge sort and Insertion sort?
Timsort: O(n log n), Merge sort: O(n log n), Insertion sort: O(n^2)
Timsort: O(n^2), Merge sort: O(n log n), Insertion sort: O(n^2)
Timsort: O(n), Merge sort: O(n log n), Insertion sort: O(n)
Timsort: O(n log n), Merge sort: O(n^2), Insertion sort: O(n log n)
How does parallel merge sort achieve improved performance over a sequential merge sort?
By dividing the sorting workload among multiple processors.
By reducing the overall number of comparisons required.
By eliminating the need for merging sorted sub-arrays.
By using a more efficient comparison function for elements.
What is the primary motivation behind using a hybrid sorting algorithm like Timsort instead of sticking to a single, well-established sorting algorithm?
Hybrid algorithms eliminate the need for recursion, leading to significant space complexity advantages.
Hybrid algorithms like Timsort exploit common patterns in real-world data, leading to often better performance than consistently applying one algorithm.
Hybrid algorithms always guarantee the best-case time complexity (O(n)) for all inputs.
Hybrid algorithms reduce code complexity, making them easier to implement than single algorithms.
How does parallel merge sort leverage multiple cores for improved performance?
It employs a different sorting algorithm on each core for diversity
It assigns each element to a separate core for independent sorting
It divides the data, sorts sub-arrays concurrently, then merges the results
It uses a single core for sorting but multiple cores for data I/O
In external sorting, why is it common to divide the input data into chunks that fit in memory?
To reduce the complexity of the sorting algorithm.
To distribute the sorting workload across multiple processors.
To enable the use of faster in-memory sorting algorithms.
To minimize the number of files needed for intermediate results.
What is the significance of the minimum run size ('minrun') parameter in Timsort's implementation?
It determines the maximum size of a run that will be sorted using Insertion sort.
It sets the threshold for switching from Merge sort to Quicksort during the sorting process.
It specifies the minimum number of elements that will trigger the use of Timsort; smaller datasets are sorted using a simpler algorithm.
It controls the maximum depth of recursion allowed during the merge process, limiting space complexity.
What factor might limit the effectiveness of parallel sorting algorithms?
The efficiency of the chosen sorting algorithm.
The speed of the storage device used for reading and writing data.
The overhead of communication and synchronization between threads.
The size of the dataset being sorted.
What is a potential drawback of using a high number of ways (e.g., 1024-way) in a multiway merge sort for external sorting?
Higher complexity in managing the merging of numerous runs.
Reduced efficiency in handling datasets with high entropy.
Decreased performance due to excessive disk I/O operations.
Significantly increased memory consumption for buffering.