Wall-L merge sort: A tunable and adaptive sorting algorithm for diverse computing environments

Area of Science:

• Computer Science
• Algorithm Analysis

Background:

• Traditional sorting algorithms exhibit rigid structures, limiting efficiency on specific platforms.
• Existing algorithms often fail on resistant platforms or require assistance functions.

Purpose of the Study:

• Introduce Wall-L Merge Sort, a novel algorithm with adaptable time complexity.
• Demonstrate its flexibility and efficiency across various computing environments.

Main Methods:

• Developed Wall-L Merge Sort, integrating quadratic sorting with a multi-layer merging strategy.
• Introduced a parameter L to control merge layers, enabling complexity transition from O(n^2) to O(n log n).

Main Results:

• Wall-L Sort demonstrates tunable time complexity from O(n^2) to O(n log n) via the parameter L.
• The algorithm effectively handles diverse input sizes and resistant platforms, outperforming other methods in specific scenarios.
• Wall-L Sort integrates complexity tuning, cache efficiency, recursion control, parallelism, and adaptability.

Conclusions:

• Wall-L Merge Sort offers a unique, adaptable framework for sorting diverse data.
• Its flexibility makes it suitable for a wide range of platforms, from embedded systems to large-scale computing.
• Theoretical and empirical evidence supports its advantages in handling varied computational demands.