Sorting Algorithm for Medium and Large Data Sets Based on Multi-Level Independent Subarrays

In this paper, we propose a new low-complex preprocessing that enables conventional data sorting algorithms to be more efficient for medium and large data sets in a serial/parallel realization. First, we divide the main array into independent subarrays by a multi-level mean-based division. It is realized by calculating the mean value of each level as the pivot to divide its elements into two parts, greater and lower than the pivot, almost in the same lengths with a lower randomness rate to the main array. Then, subarrays can be sorted by the conventional sorting algorithms in a sequential serial realization to extract sorted data gradually or parallel realizations by using independent multi-core structures. It also holds the stability and adaptivity features of the sorting algorithm, if any. The effectiveness of the mean-based pivot to the random one is investigated. To show the superiority of the proposed idea, the simulation results are compared in view of the running time and the number of swaps required to the conventional and proposed serial and parallel Insertion-sort in different lengths of data. Finally, the complexity order of the proposed algorithm in serial and parallel implementations is compared to the conventional one.