An Efficient O( $N$ ) Comparison-Free Sorting Algorithm

Abdel-Hafeez, Saleh; Gordon‐Ross, Ann

doi:10.1109/tvlsi.2017.2661746

Cited by 32 publications

(10 citation statements)

References 38 publications

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“…Abdel-Hafeez et al [1] proposed a novel mathematical comparison-free sorting algorithm that sorts input data integer elements on-the-fly without any comparison operations. They evaluate their work using a CMOS custom application specified integrated circuits (90-nm Taiwan Semiconductor Manufacturing Company) with a 1 V power supply.…”

Section: Introductionmentioning

confidence: 99%

Investigating power efficiency of mergesort

et al. 2019

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Excessive power consumption emerged as a major obstacle to achieving exascale performance in next-generation supercomputers, creating a need to explore new ways to reduce those requirements. In this study, we present a comprehensive empirical investigation of a power advantage anticipated in the mergesort method based on identifying a feature expected to be physically power efficient. We use a highperformance quicksort as a realistic baseline to compare. Results show a generic mergosort to have a distinct advantage over an optimized quicksort lending support to our expectation. They also help develop some insights toward power efficiency gains likely meaningful in a future exascale context where trading some of the abundant performance for much needed power savings in a ubiquitous computation may prove interesting.

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Section: Introductionmentioning

confidence: 99%

Investigating power efficiency of mergesort

et al. 2019

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“…As there are advancements in hardware, several parallel techniques are developed. Efficiency of the algorithms can be measured by both time and space complexities [5][6][7][8][9][10].…”

Section: Related Workmentioning

confidence: 99%

Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

Buradagunta¹,

Bodapati²,

Mundukur³

et al. 2020

ISI

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Sorting is a huge demand research area in computer science. Sorting is a process of arranging the elements in an order. In practical application computing requires things to be in order. A comparative study is done in this study and performed the comparison for both positive and negative numbers by taking the random numbers as input. In this study, different algorithms like UNH Sort, Selection Sort, Bubble Sort, Insertion Sort, Merge Sort, Quick Sort are considered for the experimentation. From the obtained results we can conclude that, Initially when the input size is less UNH Sort is giving best when it is compared with bubble sort. When the input size increases bubble sort takes long time to perform sorting. And Quick sort takes less time to perform sorting.

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“…3) Clock cycles: The clock cycle is the speed of the processor. It is the time taken to complete a full process [33].One can predict this from the speed of the processor [34]. The i7 CPU with a speed of 3.6 GHz is used as a hardware tool in this study.…”

Section: ) Comparison Of Computational Efficiency On Cpu (Serail) Anmentioning

confidence: 99%

The Computational Efficiency of Monte Carlo Breakage of Articles using Serial and Parallel Processing: A Comparison

Devi¹,

Kumar²

2019

IJACSA

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This paper presents a GPU-based parallelized and a CPU-based serial Monte-Carlo method for breakage of a particle. We compare the efficiency of the graphic card's graphics processing unit (GPU) and the general-purpose central processing unit (CPU), in a simulation using Monte Carlo (MC) methods for processing the particle breakage. Three applications are used to compare the computational performance times, clock cycles and speedup factors, to find which platform is faster under which conditions. The architecture of the GPU is becoming increasingly programmable; it represents a potential speedup for many applications compared to the modern CPU. The objective of the paper is to compare the performance of the GPU and Intel Core i7-4790 multicore CPU. The implementation for the CPU was written in the C programming language, and the GPU implemented the kernel using Nvidia's CUDA (Compute Unified Device Architecture). This paper compares the computational times, clock cycles and the speedup factor for a GPU and a CPU, with various simulation settings such as the number of simulation entries (SEs), for a better understanding of the GPU and CPU computational efficiency. It has been found that the number of SEs directly affects the speedup factor.

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An Efficient O( $N$ ) Comparison-Free Sorting Algorithm

Cited by 32 publications

References 38 publications

Investigating power efficiency of mergesort

Investigating power efficiency of mergesort

Performance Comparison of Sorting Algorithms with Random Numbers as Inputs

The Computational Efficiency of Monte Carlo Breakage of Articles using Serial and Parallel Processing: A Comparison

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