Understanding the Role of Parallel Programming in Multi-core Processor Based Systems

Pandey, Raksha; Badal, Neelendra

doi:10.2139/ssrn.3350311

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…In contrast, MISD involves the execution of multiple instructions on a single data stream [6]. SIMD allows for multiple threads to execute the same operation across multiple data elements, and can be implemented at various levels, including core, thread, and instruction [7]. MIMD, on the other hand, maps multiple instructions to multiple data points, with each processing core handling its unique set of instructions and data points [8].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

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

“…For example, the Intel Core i9-7900X processor and AMD Ryzen Threadripper processor are the two most advanced multi-core processors in their product lines, respectively. [1] The computers can process complex calculations depend on Multicores CPU. Besides, Multi-cores CPU supports many consumer software with multithreading.…”

Section: Introductionmentioning

confidence: 99%

The Effect of High Performance Computer on Deep Neural Network

Zhang¹,

Zhang²,

Jiang³

et al. 2021

Eng. Sci.

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In recent years, convolutional neural networks (CNNs) have been extended widely to a large number of computer vision applications such as image classification, image detection, image segmentation, etc. In this paper, the three image processing applications are implemented by integrating with CNNs and the high performance computing (HPC) systems. To observe the performance of HPC, three CNN models for each image processing application have been trained with different values of parameters and their training times are provided in results. Four computing systems, Google collaboratory with central processing unit (CPU), Google collaboratory with graphics processing unit (GPU), Google Cloud with HPC, and The extreme science and engineering discovery environment (XSEDE) with HPC are compared in the work. The training program is suggested to use the parallel algorithm when GPU is available. This project explores that the HPC with GPU has the highest work efficiency regarding operating time.

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Understanding the Role of Parallel Programming in Multi-core Processor Based Systems

Cited by 2 publications

References 8 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

The Effect of High Performance Computer on Deep Neural Network

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