Performance Test of Parallel Image Processing Using Open MPI on Raspberry PI Cluster Board

Rahmat, R F; Saputra, Triyan; Hizriadi, Ainul; Lini, Tifani Zata; Nasution, Mahyuddin K. M.

doi:10.1109/elticom47379.2019.8943848

Cited by 9 publications

(4 citation statements)

References 1 publication

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“…When using an Ethernet cable, the variations in performance among the different Raspberry Pi models were noticeable; the Zero W had the poorest performance and 4B showed the best performance. Rahmat et al, (2019) evaluated the performance of Raspberry Pi image processing built using Open MPI. The results showed the programme worked well in the cluster scope and the image conversion process performed better in the cluster scope than on a single device.…”

Section: Comparison Of the Performance Of Raspberry Pi As A Servermentioning

confidence: 99%

Deployment of Offline Learning Management Systems: Comparing the Performance of Selected Micro-servers in Tanzania

Maro

Kondoro

Haßler³

et al. 2023

JL4D

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Low-powered mobile devices such as Raspberry Pis and tablets can be used as micro-servers to implement offline Learning Management Systems (LMS). Despite their potential, especially for low-income countries, such as Tanzania, no research is available detailing the affordances of these devices for supporting LMS features. This study investigated the suitability of various low-cost micro-servers for deploying LMSs. It compared the performance of the Raspberry Pi, Android tablet, and Chromebook in terms of LMS benchmarking, response time, and resource utilisation. Results showed all devices had sufficient hardware resources to support the LMS, however, software stacks, I/O performance, and platform optimisations affected the micro-servers' performance. The Chromebook had the best performance in terms of response time, followed by the Raspberry Pi and tablets. In terms of cost, the Raspberry PI was the cheapest option. The installation process for tablets was more cumbersome than the other devices, meaning the devices with better tooling and a more conventional software stack were a better option for deploying offline micro-servers.

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Section: Comparison Of the Performance Of Raspberry Pi As A Servermentioning

confidence: 99%

Deployment of Offline Learning Management Systems: Comparing the Performance of Selected Micro-servers in Tanzania

Maro

Kondoro

Haßler³

et al. 2023

JL4D

View full text Add to dashboard Cite

show abstract

“…RPi cluster with open-source libraries such as Hadoop and OpenCV could play a pivotal role in image processing e.g. feature points extraction system using Speeded Up Robust Features (SURF) algorithm [27], face recognition [28], image stacking [29], edge detection [30], [31], image analysis [32], ray tracing [33], image conversion [34], image recognition [35], Fourier transform [36] and image classification [37]. Moreover, a RPi cluster can extend its capability in image processing by adding a GPU [38].…”

Section: Image Processingmentioning

confidence: 99%

Distributed Multi-Ant Colony System Algorithm using Raspberry Pi Cluster for Travelling Salesman Problem

Alobaedy

Khalaf

Fazea

2022

eijs

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The traveling salesman problem is addressed in this paper by introducing a distributed multi-ant colony algorithm that is implemented on a Raspberry Pi cluster. The implementation of a master and eight workers, each running on Raspberry Pi nodes, is the central component of this novel technique. Each worker is responsible for managing their own colony of ants, while the master coordinates communications among workers’ nodes and assesses the most optimal approach. To put the newly built cluster through its paces, several datasets of traveling salesman problem are used to test the created cluster. The findings of the experiment indicate that a single board computer cluster, which makes use of multi-ant colony algorithm, is a viable alternative for distributed computing. This approach's extensibility options are extensively discussed as well.

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“…Another experiment used a parallel computing system in image processing to convert an image using raspberry pi's CPU [17]. The cluster computer method is chosen since the image segmentation process is running with limited parameters while using a single raspberry pi.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease

Fajrianti

Pratama

Nasyir

et al. 2022

JOIV : Int. J. Inform. Visualization

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In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.

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Performance Test of Parallel Image Processing Using Open MPI on Raspberry PI Cluster Board

Cited by 9 publications

References 1 publication

Deployment of Offline Learning Management Systems: Comparing the Performance of Selected Micro-servers in Tanzania

Deployment of Offline Learning Management Systems: Comparing the Performance of Selected Micro-servers in Tanzania

Distributed Multi-Ant Colony System Algorithm using Raspberry Pi Cluster for Travelling Salesman Problem

High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease

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