Analisa Komputasi Paralel Mengurutkan Data Dengan Metode Radix Dan Selection

Lumbanraja, Favorisen Rosyking; Aristoteles, Aristoteles; Muttaqina, Nadila Rizqi

doi:10.23960/komputasi.v8i2.2662

Cited by 4 publications

(6 citation statements)

References 2 publications

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“…Each little portion is broken into instructions, which are executed simultaneously on various processors. The implementation of parallel architecture is evaluated based on its performance in terms of speedup and efficiency and then compared to sequential computation [25,26]. The formulae for speedup and efficiency are as follows:…”

Section: Discussionmentioning

confidence: 99%

“…In order to get findings that give a performance improvement by approximating the value of π in parallel, tests were conducted in this study utilizing some parameters. In studies [25,26], the performance analysis of parallel computing uses two parameters namely speedup and efficiency. Speedup represents a metric for determining how much faster a parallel algorithm is compared to its sequential counterpart [3].…”

Section: Testingmentioning

confidence: 99%

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Enhanced π Approximation Through MIMD Parallel Computing: An Efficiency Analysis Utilizing Raspberry Pi

Afrianto,

Mardiansyah,

Ritzkal

et al. 2023

MMEP

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Multiple Instruction Multiple Data is one of the parallel computing architectures in Flynn's taxonomy, where cores can execute independent sets of instructions on independent sets of data. Parallel computing could be used in scientific subfields such as mathematics to approximate the number pi. One of the methods to approximate π is the Gregory-Leibniz method, which proposes the expansion of the arctan x series, which can then be used as an algorithm to approximate π. This method requires lots of term calculations to obtain the accurate digits of π, hence why parallel computing is needed. Building a cost-effective parallel computing architecture from standard desktop computers is difficult due to the high cost and space requirements. This study will build an MIMD parallel computing done by three Raspberry Pi 3Bs, small and affordable credit card sized single board computer, connected through a message-passing interface to get the performance analysis of MIMD parallel computing. The performance analysis shows that with three Raspberry Pis, there is a huge speedup of 6,2953020 and a faster time of 1.49 seconds compared to 9.38 seconds at 5 million terms. As a result of this discovery, the Raspberry Pi could be used in further projects to develop an affordable parallel computing architecture.

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Section: Discussionmentioning

confidence: 99%

Section: Testingmentioning

confidence: 99%

Enhanced π Approximation Through MIMD Parallel Computing: An Efficiency Analysis Utilizing Raspberry Pi

Afrianto,

Mardiansyah,

Ritzkal

et al. 2023

MMEP

View full text Add to dashboard Cite

show abstract

“…Komputasi paralel dapat meningkatkan efisiensi waktu proses perhitungan sehingga penggunaan sumber daya komputasi lebih efektif dan efisien [1]. Untuk menghitung seberapa cepat proses komputasi paralel dibandingkan dengan komputasi serial maka digunakan Speed Up (S) dan Efficiency (E) [4]. Rumus untuk menghitung S dan E sebagai berikut dan , dimana adalah waktu yang dibutuhkan untuk komputas serial, adalah waktu yang digunakan untuk komputasi paralel, dan p adalah jumlah prosesor.…”

Section: Erna Nurmawati Robby Hasan Pangaribuan Ibnu Santosounclassified

“…Efisiensi kinerja paralel menggunakan 2 thread diperoleh lebih baik daripada menggunakan 4 thread, dan 8 thread [10]. Hasil nilai Efesiensi yang didapat dari semua skenario pengujian membuktikan bahwa penggunaan dengan dua prosesor masih tergolong paling efesien dibandingkan empat prosesor [3].…”

Section: Erna Nurmawati Robby Hasan Pangaribuan Ibnu Santosounclassified

Comparison of Serial and Parallel Computation on Predicting Missing Data with EM Algorithm

Nurmawati¹,

Pangaribuan²,

Santoso³

2021

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One way to deal with the presence of missing value or incomplete data is to impute the data using EM Algorithm. The need for large and fast data processing is necessary to implement parallel computing on EM algorithm serial program. In the parallel program architecture of EM Algorithm in this study, the controller is only related to the EM module whereas the EM module itself uses matrix and vector modules intensively. Parallelization is done by using OpenMP in EM modules which results in faster compute time on parallel programs than serial programs. Parallel computing with a thread of 4 (four) increases speed up, reduces compute time, and reduces efficiency when compared to parallel computing by the number of threads 2 (two).

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“…However, not all algorithms are suitable for implementing parallel computing. Findings from research conducted by Favorisen, Aristotle, and Nadila show that the radix sort algorithm can only be accelerated up to 1.2 times even though it already uses four processors [6]. The information used by them is obtained through a random number generation process.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

Nugroho,

Ashari,

Nashrullah

et al. 2023

JAIC

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Tim Sort is a sorting algorithm that combines Merge Sort and Binary Insertion Sort sorting algorithms. Parallel computing is a computational processing technique in parallel or is divided into several parts and carried out simultaneously. The application of parallel computing to algorithms is called parallelization. The purpose of parallelization is to reduce computational processing time, but not all parallelization can reduce computational processing time. Our research aims to analyse the effect of implementing parallel computing on the processing time of the Tim Sort algorithm. The Team Sort algorithm will be parallelized by dividing the flow or data into several parts, then each sorting and recombining them. The libraries we use are OpenMP and MPI, and tests are carried out using up to 16 core processors and data up to 4194304 numbers. The goal to be achieved by comparing the application of OpenMP and MPI to the Team Sort algorithm is to find out and choose which library is better for the case study, so that when there is a similar case, it can be used as a reference for using the library in solving the problem. The results of research for testing using 16 processor cores and the data used prove that the parallelization of the Sort Team algorithm using OpenMP is better with a speed increase of up to 8.48 times, compared to using MPI with a speed increase of 8.4 times. In addition, the increase in speed and efficiency increases as the amount of data increases. However, the increase in efficiency that is obtained by increasing the processor cores decreases.

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Analisa Komputasi Paralel Mengurutkan Data Dengan Metode Radix Dan Selection

Cited by 4 publications

References 2 publications

Enhanced π Approximation Through MIMD Parallel Computing: An Efficiency Analysis Utilizing Raspberry Pi

Enhanced π Approximation Through MIMD Parallel Computing: An Efficiency Analysis Utilizing Raspberry Pi

Comparison of Serial and Parallel Computation on Predicting Missing Data with EM Algorithm

Comparative Analysis of OpenMP and MPI Parallel Computing Implementations in Team Sort Algorithm

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