scholarly journals Analysis of Parallel Merge Sort Algorithm

2010 ◽  
Vol 1 (19) ◽  
pp. 70-73 ◽  
Author(s):  
K.B Manwade
Keyword(s):  
Parallel Merge ◽  
Merge Sort ◽  
Sort Algorithm

ON PARALLEL PREFIX COMPUTATION

1994 ◽  
Vol 04 (04) ◽  
pp. 429-436 ◽  
Author(s):  
SANJEEV SAXENA ◽  
P.C.P. BHATT ◽  
V.C. PRASAD
Keyword(s):  
Linear Time ◽  
Time Algorithm ◽  
Optimal Time ◽  
Word Size ◽  
Parallel Prefix ◽  
Parallel Merge ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Crcw Pram ◽  
Prefix Sums

We prove that prefix sums of n integers of at most b bits can be found on a COMMON CRCW PRAM in [Formula: see text] time with a linear time-processor product. The algorithm is optimally fast, for any polynomial number of processors. In particular, if [Formula: see text] the time taken is [Formula: see text]. This is a generalisation of previous result. The previous [Formula: see text] time algorithm was valid only for O(log n)-bit numbers. Application of this algorithm to r-way parallel merge sort algorithm is also considered. We also consider a more realistic PRAM variant, in which the word size, m, may be smaller than b (m≥log n). On this model, prefix sums can be found in [Formula: see text] optimal time.

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.

Parallel merge sort using comparison matrices. I

1994 ◽  
Vol 30 (5) ◽  
pp. 631-647 ◽  
Author(s):  
Ya. E. Romm
Keyword(s):  
Parallel Merge ◽  
Merge Sort

Formal Verification of a Merge Sort Algorithm in SPARK

2021 ◽  
Author(s):  
Ryan Baity ◽  
Laura R. Humphrey ◽  
Kenneth Hopkinson
Keyword(s):  
Formal Verification ◽  
Merge Sort ◽  
Sort Algorithm

Algorithm Improvement of Two-Way Merge Sort Based on OpenMP

2014 ◽  
Vol 701-702 ◽  
pp. 24-29
Author(s):  
Jun Zhang ◽  
Yong Ping Gao ◽  
Yue Shun He ◽  
Xue Yuan Wang
Keyword(s):  
Parallel Processing ◽  
Time Complexity ◽  
Good Time ◽  
Processing Capacity ◽  
Time Efficiency ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Programming Interface ◽  
Multi Core Processor

Two-way merge sort algorithm has a good time efficiency which has been used widely. The sort algorithm can be improved on speed and efficient based on its own potential parallelism via the parallel processing capacity of multi-core processor and the convenient programming interface of OpenMP. The time complexity is improved to O(nlog2n/TNUM) and inversely proportional to the number of parallel threads. The experiment results show that the improved two-way merge sort algorithm become much more efficient compared to the traditional one.

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