scholarly journals Perbandingan Efisiensi Algoritma Sorting dalam Penggunaan Bandwidth

2020 ◽  
Vol 12 (2) ◽  
pp. 96-103
Author(s):  
Desi Anggreani ◽  
Aji Prasetya Wibawa ◽  
Purnawansyah Purnawansyah ◽  
Herman Herman
Keyword(s):  
Execution Time ◽  
The Other ◽  
Sorting Algorithm ◽  
Memory Usage ◽  
Data Types ◽  
Network Bandwidth ◽  
Sorting Algorithms ◽  
Sorting Data ◽  
Merge Sort ◽  
Sort Algorithm

The most used algorithm is the sorting algorithm. There have been many popping sorting algorithms that can be used, in this study researchers took three sorting algorithms namely Insertion Sort, Selection Sort, and Merge Sort. As for this study will analyze the comparison of execution time and memory usage by considering the number of enter data of each algorithm used. The data used in this study is ukhuwah NET network bandwidth usage data connected in the Faculty of Computer Science in the form of double data types. After implementing and analyzing in terms of execution time merge sort algorithm has a faster execution time in sorting data with an average execution time value of 108.593777 ms on the 3000 data count. While in the same amount of data for the most execution time is the Selection Sort algorithm with a large execution time of 144.498144 ms, in terms of memory usage with the amount of data3000 Merge Sort Algorithm has the highest memory usage compared to the other two algorithms which is 21,444 MB while the other two algorithms have a succession of memory usage of 20,837 MB and 20,325MB.

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

2020 ◽  
Vol 11 (2) ◽  
pp. 95-102
Author(s):  
I Nyoman Aditya Yudiswara ◽  
Abba Suganda
Keyword(s):  
Parallel Algorithms ◽  
The Other ◽  
Sorting Algorithm ◽  
Data Parallelism ◽  
Parallel Sorting ◽  
Sequential Algorithms ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Quick Sort

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

BigGIS With Hadoop in MapReduce Environment

2019 ◽  
pp. 25-32
Author(s):  
Nada M. Alhakkak
Keyword(s):  
Big Data ◽  
Scheduling Algorithm ◽  
Real Data ◽  
Map Reduce ◽  
Data Types ◽  
Simulated Environment ◽  
Merge Sort ◽  
Data Source ◽  
Sort Algorithm ◽  
And Storage

BigGIS is a new product that resulted from developing GIS in the “Big Data” area, which is used in storing and processing big geographical data and helps in solving its issues. This chapter describes an optimized Big GIS framework in Map Reduce Environment M2BG. The suggested framework has been integrated into Map Reduce Environment in order to solve the storage issues and get the benefit of the Hadoop environment. M2BG include two steps: Big GIS warehouse and Big GIS Map Reduce. The first step contains three main layers: Data Source and Storage Layer (DSSL), Data Processing Layer (DPL), and Data Analysis Layer (DAL). The second layer is responsible for clustering using swarms as inputs for the Hadoop phase. Then it is scheduled in the mapping part with the use of a preempted priority scheduling algorithm; some data types are classified as critical and some others are ordinary data type; the reduce part used, merge sort algorithm M2BG, should solve security and be implemented with real data in the simulated environment and later in the real world.

scholarly journals Parallel Counting Sort: A Modified of Counting Sort Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 10-15
Author(s):  
Pratyaksa Ocsa Nugraha Saian
Keyword(s):  
Execution Time ◽  
Sorting Algorithm ◽  
Negative Number ◽  
Input List ◽  
Classic Problem ◽  
Sorting Process ◽  
Counting Sort ◽  
Sort Algorithm

Sorting is one of a classic problem in computer engineer. One well-known sorting algorithm is a Counting Sort algorithm. Counting Sort had one problem, it can’t sort a positive and negative number in the same input list. Then, Modified Counting Sort created to solve that’s problem. The algorithm will split the numbers before the sorting process begin. This paper will tell another modification of this algorithm. The algorithm called Parallel Counting Sort. Parallel Counting Sort able to increase the execution time about 70% from Modified Counting Sort, especially in a big dataset (around 1000 and 10.000 numbers).

scholarly journals A Novel Sorting Method for Real and Integer Numbers: An Extension of Counting Sort

2020 ◽  
Vol 8 (6S4) ◽  
pp. 36-39
Keyword(s):  
Data Structures ◽  
Time Complexity ◽  
Sorting Algorithm ◽  
Real Numbers ◽  
Sorting Method ◽  
Sorting Algorithms ◽  
Counting Sort ◽  
Sort Algorithm ◽  
Positive Integers

Sorting is an essential conceptin the study of data structures. There are many sorting algorithms that can sort elements in a given array or list. Counting sort is a sorting algorithm that has the best time complexity. However, the counting sort algorithm only works for positive integers. In this paper, an extension of the counting sort algorithm is proposed that can sort real numbers and integers (both positive and negative).

scholarly journals Design and Analysis of Optimized Stooge Sort Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 1669-1673
Keyword(s):  
Time Complexity ◽  
Large Time ◽  
Sorting Algorithm ◽  
Data Element ◽  
Running Time ◽  
Sorting Algorithms ◽  
Sorting Technique ◽  
Sort Algorithm ◽  
The Given ◽  
Data Sorting

Data sorting hasmany advantages and applications in software and web development. Search engines use sorting techniques to sorttheresult before itispresented totheuser.Thewordsinadictionary are insorted ordersothatthewords canbe found easily.There aremanysorting algorithms that areused in many domains to perform some operation and obtain the desired output. But there are some sorting algorithms that take large time in sorting the data. This huge time can be vulnerable to the operation. Every sorting algorithm has the different sorting technique to sort the given data, Stooge sort is asorting algorithm which sorts the data recursively. Stooge sort takes comparatively more time as compared tomany othersorting algorithms.Stooge sortworks recursively to sort the data element but the Optimized Stooge sort does not use recursive process. In this paper, we propose Optimized Stooge sort to reduce the time complexity of the Stooge sort. The running time of Optimized Stooge sort is very much reduced as compared to theStooge sort algorithm. The existing researchfocuses onreducing therunning time of Stooge sort. Our results show that the Optimized Stooge sort is faster than the Stooge sort algorithm.

Performance Comparison of Parallel Sorting Algorithms on Homogeneous Cluster of Workstations

2012 ◽  
Vol 433-440 ◽  
pp. 3900-3904
Author(s):  
Lai Lai Win Kyi ◽  
Nay Min Tun
Keyword(s):  
Close Relation ◽  
Performance Comparison ◽  
Parallel Computer ◽  
Sorting Algorithm ◽  
Parallel Sorting ◽  
Sorting Algorithms ◽  
Homogeneous Cluster ◽  
Cluster Of Workstations ◽  
Parallel Merge ◽  
Merge Sort

Sorting appears the most attention among all computational tasks over the past years because sorted data is at the heart of many computations. Sorting is of additional importance to parallel computing because of its close relation to the task of routing data among processes, which is an essential part of many parallel algorithms. Many parallel sorting algorithms have been investigated for a variety of parallel computer architectures. In this paper, three parallel sorting algorithms have been implemented and compared in terms of their overall execution time. The algorithms implemented are the odd-even transposition sort, parallel merge sort and parallel shell sort. Cluster of Workstations or Windows Compute Cluster has been used to compare the algorithms implemented. The C# programming language is used to develop the sorting algorithms. The MPI library has been selected to establish the communication and synchronization between processors. The time complexity for each parallel sorting algorithm will also be mentioned and analyzed.

scholarly journals FPGA-Based Implementation of MMC Control Based on Sorting Networks

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2394 ◽  
Author(s):  
Mattia Ricco ◽  
Laszlo Mathe ◽  
Eric Monmasson ◽  
Remus Teodorescu
Keyword(s):  
Execution Time ◽  
Sampling Period ◽  
Sorting Algorithm ◽  
Multilevel Converter ◽  
Voltage Balancing ◽  
Sorting Networks ◽  
Sorting Algorithms ◽  
And Performance ◽  
Proper Operation ◽  
Made In

In Modular Multilevel Converter (MMC) applications, the balancing of the capacitor voltages is one of the most important issues for achieving the proper behavior of the MMC. The Capacitor Voltage Balancing (CVB) control is usually based on classical sorting algorithms which consist of repetitive/recursive loops. This leads to an increase of the execution time when many Sub-Modules (SMs) are employed. When the execution time of the balancing is longer than the sampling period, the proper operation of the MMC cannot be ensured. Moreover, due to their inherent sequential operation, sorting algorithms are suitable for software implementation (microcontrollers or DSPs), but they are not appropriate for a hardware implementation. Instead, in this paper, Sorting Networks (SNs) are proposed due to their convenience for implementation in FPGA devices. The advantages and the main challenges of the Bitonic SN in MMC applications are discussed and different FPGA implementations are presented. Simulation results are provided in normal and faulty conditions. Moreover, a comparison with the widely used bubble sorting algorithm and max/min approach is made in terms of execution time and performance. Finally, hardware-in-the-loop results are shown to prove the effectiveness of the implemented SN.

Comparative of Advanced Sorting Algorithms (Quick Sort, Heap Sort, Merge Sort, Intro Sort, Radix Sort) Based on Time and Memory Usage

2021 ◽  
Author(s):  
Marcellino Marcellino ◽  
Davin William Pratama ◽  
Steven Santoso Suntiarko ◽  
Kristien Margi
Keyword(s):  
Memory Usage ◽  
Radix Sort ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Quick Sort

scholarly journals Efficient Data Sorting Introducing a New Method

2021 ◽  
Author(s):  
Raghavendra Devidas ◽  
Aishwarya Kulkarni
Keyword(s):  
Data Processing ◽  
New Method ◽  
Sorting Algorithm ◽  
New Approach ◽  
Data Comparison ◽  
Sorting Algorithms ◽  
Sorting Data ◽  
Efficient Data ◽  
Data Sorting

The efficiency of data sorting algorithms is the key aspect which determines the speed of data processing and searching. The best known efficiency of sorting algorithm has been Log (N) if there are N terms. All of the well-known sorting algorithms use various techniques to sort data. The basis for most of these are comparing the data terms with each other. In this manuscript, we are introducing a new approach for sorting data. This method is postulated to have the highest efficiency ever achieved by any of the sorting algorithms. We achieve this by sorting data without comparing the data terms. Or achieving results of data comparison without comparing the terms explicitly.

scholarly journals A Capacitor Voltage Balancing Approach Based on Mapping Strategy for MMC Applications

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 449 ◽  
Author(s):  
Ricco ◽  
Mathe ◽  
Hammami ◽  
Franco ◽  
Rossi ◽  
...  
Keyword(s):  
Execution Time ◽  
Computational Effort ◽  
Superior Performance ◽  
Sorting Algorithm ◽  
Multilevel Converters ◽  
Voltage Balancing ◽  
Mapping Strategy ◽  
Sorting Algorithms ◽  
New Strategy ◽  
On Chip

This paper proposes a new strategy to achieve balanced capacitor voltages in modular multilevel converters. Among the possible solutions, centralized arm control approaches are often adopted. These methods require a balancing technique based on a sorted list of the sub-modules according to their capacitor voltages. In order to achieve the aforementioned sorted list, different algorithms have been proposed in literature, such as: Sorting algorithms, max/min approaches, etc. However, the sorting algorithms require a long execution time, while the max/min approaches affect the converter dynamic response during faults. To overcome these issues, a new mapping strategy providing a quasi-sorted list is proposed in this paper. The suggested method is compared in simulation with both the classical bubble sorting algorithm, and the max/min method during both normal and faulty conditions. Moreover, the three methods have been implemented in a Xilinx Zynq-7000 System-on-Chip (SoC) device, in order to analyze the corresponding execution time and the required computational effort. Hardware-in-the-loop results are presented for demonstrating the superior performance of the proposed balancing strategy.

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