Neetu Faujdar

Analysis and Testing of Sorting Algorithms on a Standard Dataset

Sorting is a huge demand research area in computer science. One of the fundamental issues in computer science is how to order the data in lexicographic order. In practical application computing requires things to be in order. The performance of any computation depends on the sorting algorithms. A lot of sorting algorithm has been developed by many authors to enhance the performance in terms of complexity analysis, and most of author analysis the time complexity and auxiliary space complexity of the sorting algorithms but the total space complexity taken by the algorithms not evaluated yet. The total space complexity contains the primary and secondary memory required to store input and output data, memory required to hold the code and working space. The goal of this paper is to test the various existing sorting algorithms and to evaluate the total space complexity of various sorting algorithms on a standard dataset. Sorting algorithms are evaluated on four cases of standard dataset. The four cases of the dataset are random with repeated data, reverse sorted with repeated data, sorted with repeated data, and nearly sorted with repeated data, and we measure the performance of each sorting algorithm in each case.

The detailed experimental analysis of bucket sort

The bucket sort is a non-comparison sorting algorithm in which elements are scattered over the buckets. We have concluded, based on state-of-art that most of the researchers have been using the insertion sort within buckets. The other sorting technique is also used in many papers over the buckets. From the state-of-art of bucket sort, we have analyzed that insertion sort is preferable in case of low volume of data to be sorted. In this work, authors have used the merge, count and insertion sort separately over the buckets and the results are compared with each other. The sorting benchmark has been used to test the algorithms. For testing the algorithms, sorting benchmark has been used. We have defined the threshold (τ) defined the threshold for saving the time and space of the algorithms. Results indicate that, count sort comes out to be more efficient within the buckets for every type of dataset.

A practical approach of GPU bubble sort with CUDA hardware

Presently GPU computing is in big call with the sphere of sorting. This paper presents the design of GPU (Graphic Processing Unit) bubble sort with CUDA (Compute Unified Device Architecture) hardware. The bubble sort is considered as comparison based sorting. The design of bubble sort using many core GPUs was previously unknown. The paper also presents the speedup acquired by the GPU bubble. The sorting benchmark is used to test bubble sort (parallel & sequential). The six types of test cases are considered by the sorting benchmark. On the basis of experimental analysis, parallel bubble sort achieved 37229 times faster execution time using zero test case and 6375 times faster using sorted test case at N = 2500000 and T = 512. The best case time complexity of the GPU bubble sort is compressed O(n) to O(1) because of the GPU.