Performance evaluation and analysis of scalable Raspberry Pi 4 Model B clusters

Performance evaluation and analysis of scalable Raspberry Pi 4 Model B clusters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Performance evaluation and analysis of scalable Raspberry Pi 4 Model B clusters Peng Liu, Xiaofan Cao, Yujiao Jia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4460804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study systematically investigates the performance scalability of Raspberry Pi 4 Model B clusters across various configura-tions. Leveraging the latest advancements in the Pi4B architecture, including improved processor performance, increased memory capacity, and enhanced network bandwidth, we explore Raspberry Pi clusters’ computational efficiency and cost-effectiveness. We use the High-Performance Linpack (HPL) benchmark to measure the clusters’ floating-point operations per second (FLOPS) across diverse node combinations and memory usage scenarios, examining cluster scalability andperformance thresholds. Our findings reveal a nuanced, nonlinear scaling behavior of performance with increasing cluster sizes. This paper contributes valuable insights for designing and optimizing Raspberry Pi clusters in scalable computing applications,offering a foundational perspective for future exploration in this rapidly evolving field. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4460804","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309403704,"identity":"c264a2fe-feb8-43d3-9a05-08b3a4306cfe","order_by":0,"name":"Peng Liu","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Liu","suffix":""},{"id":309403705,"identity":"f39e8e06-5890-4364-b692-09f2bf81a56e","order_by":1,"name":"Xiaofan Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3NMWvCQBTA8ReEuARufSG0foULBXHww9wRuCmdCqVDkAMhLkE/gJ/Cxa6Bg5vsfkOGTDc5dJJM0ljQQThrN4f7L48H78cD8PkeNQZAAAb1ead3kVhCyP5B4JdEl8vbhCyUxraYIllXh7b7aLgczrcIReMkuBMZZVogNl+fabWzXEb6HUFbJ6GQpy0L1QzM6zYJSsUl5mMMpHITsqc1OyocmdwmwbEno/0fBPsvvFRITR4m/WX/JbpN0NiM8qXA1IhxXGn7UkbibcK0m5BVpuPuMMVnk1nsiuZpNVQb8124yXU1hKfB7gYn4vP5fL7rfgA+0lceki1SnwAAAABJRU5ErkJggg==","orcid":"","institution":"North China Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"Xiaofan","middleName":"","lastName":"Cao","suffix":""},{"id":309403706,"identity":"c3badc86-4450-494b-b615-8ecb773c7ee9","order_by":2,"name":"Yujiao Jia","email":"","orcid":"","institution":"North China Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2024-05-22 12:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4460804/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4460804/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78345343,"identity":"89185871-814d-4cf6-8665-d2dcbf6550f9","added_by":"auto","created_at":"2025-03-12 09:32:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2070733,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4460804/v1_covered_ce885762-3f62-42c5-940e-f82c04636852.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance evaluation and analysis of scalable Raspberry Pi 4 Model B clusters","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4460804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4460804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study systematically investigates the performance scalability of Raspberry Pi 4 Model B clusters across various configura-tions. 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