R. Rahmat, T. Saputra, A. Hizriadi, T. Z. Lini, M. K. Nasution
{"title":"Performance Test of Parallel Image Processing Using Open MPI on Raspberry PI Cluster Board","authors":"R. Rahmat, T. Saputra, A. Hizriadi, T. Z. Lini, M. K. Nasution","doi":"10.1109/elticom47379.2019.8943848","DOIUrl":null,"url":null,"abstract":"Image processing has the characteristic of input and output data in the form of images when the segmentation process is performed using Raspberry Pi with limited parameters; it will take a long time to process. An alternative can be applied using a cluster computer method to overcome this issue. In this study, a Raspberry cluster system will be built using Open MPI. Open MPI served as a compiler cluster by dividing the work process into several connected clusters device nodes so that the program can be run on cluster devices simultaneously. The test result showed that the program worked well in the cluster scope and the image conversion process performed better in the cluster scope than in single device.","PeriodicalId":131994,"journal":{"name":"2019 3rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 3rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/elticom47379.2019.8943848","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
Image processing has the characteristic of input and output data in the form of images when the segmentation process is performed using Raspberry Pi with limited parameters; it will take a long time to process. An alternative can be applied using a cluster computer method to overcome this issue. In this study, a Raspberry cluster system will be built using Open MPI. Open MPI served as a compiler cluster by dividing the work process into several connected clusters device nodes so that the program can be run on cluster devices simultaneously. The test result showed that the program worked well in the cluster scope and the image conversion process performed better in the cluster scope than in single device.