Empirical Study To Compare The Performance Of Novel CPU Implementation Of Deep Learning Algorithms With GPU-BASED Implementatioms

Empirical Study To Compare The Performance Of Novel CPU Implementation Of Deep Learning Algorithms With GPU-BASED Implementatioms | 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 Research Article Empirical Study To Compare The Performance Of Novel CPU Implementation Of Deep Learning Algorithms With GPU-BASED Implementatioms Eslam Al-Sobh, Prof. Mahmoud Alshbool, Dr. Yaser Jararweh, Prof. Moath Jarrah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4625052/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 Deep learning architectures and algorithms have shown promising results in different applications. However, training deep learning algorithms is a time-consuming step and researchers used graphics processing unit (GPU) accelerators in order to achieve an acceptable execution time, especially for real life applications. Sub-LInear Deep Learning Engine (SLIDE) is a relatively new work that aims at speeding up deep learning using only central processing unit (CPUs) implementation. Additionally, TorchSLIDE used PyTorch libraries to speed up SLIDE by 2.6X. This research study attempts to provide an empirical comparison for four models which are: PyTorch baseline CPU, PyTorch baseline GPU, TorchSLIDE, and SLIDE. In the experiments, we used a server with 10 cores (Intel i9-7900X) and NVIDIA Quadro P4000 (GP104GL) GPU. Five datasets were used in the evaluation which are: Amazon-670K, Delicious-200K, AmazonCat-13K, LF-AmazonTitles-131K, and Wiki10-31K. Based on the experiments, Py- Torch baseline GPU outperforms the other three implementations and achieved the highest accuracy in less time compared with the others for all datasets. Moreover, SLIDE outperformed TorchSLIDE when using the AmazonCat-13K dataset. Deep Learning Neural Networks Green AI CPU and GPU Parallelization Sub-LInear Deep learning Engine TorchSLIDE PyTorch Baseline 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. 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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-4625052","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322271097,"identity":"8d3201e8-62e2-4152-954c-abc36a53b65f","order_by":0,"name":"Eslam Al-Sobh","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Eslam","middleName":"","lastName":"Al-Sobh","suffix":""},{"id":322271098,"identity":"0e87dd67-b6d8-4989-9743-15bc90577837","order_by":1,"name":"Prof. Mahmoud Alshbool","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Mahmoud","lastName":"Alshbool","suffix":""},{"id":322271099,"identity":"57d76555-c383-4f03-a05f-2b10f6356c90","order_by":2,"name":"Dr. Yaser Jararweh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"Dr.","firstName":"Yaser","middleName":"","lastName":"Jararweh","suffix":""},{"id":322271100,"identity":"2bb03a4f-96f8-461f-abc5-a042ec52b213","order_by":3,"name":"Prof. Moath Jarrah","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Prof.","middleName":"Moath","lastName":"Jarrah","suffix":""}],"badges":[],"createdAt":"2024-06-23 11:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4625052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4625052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69153504,"identity":"dc4ebd9a-e63c-41d3-afe1-a60ba9796fa0","added_by":"auto","created_at":"2024-11-16 15:16:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":540033,"visible":true,"origin":"","legend":"","description":"","filename":"SpringsPaperEslamAlSobh.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4625052/v1_covered_dfd146b3-b2e9-48f3-8af9-8e16214cabb5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empirical Study To Compare The Performance Of Novel CPU Implementation Of Deep Learning Algorithms With GPU-BASED Implementatioms","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":"Deep Learning, Neural Networks, Green AI, CPU and GPU Parallelization, Sub-LInear Deep learning Engine, TorchSLIDE, PyTorch Baseline","lastPublishedDoi":"10.21203/rs.3.rs-4625052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4625052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Deep learning architectures and algorithms have shown promising results in different\napplications. 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