Adaptive Pulsating Workload Scheduling for Cosmic Simulation: A Thermal-Aware Distributed Computing Architecture

Adaptive Pulsating Workload Scheduling for Cosmic Simulation: A Thermal-Aware Distributed Computing Architecture | 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 Adaptive Pulsating Workload Scheduling for Cosmic Simulation: A Thermal-Aware Distributed Computing Architecture xiaochen xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6504227/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 We propose an adaptive pulsating workload scheduling architecture for cosmic simulations, which dynamically balances computational intensity with thermal and memory bandwidth efficiency in distributed GPU clusters. The core innovation lies in a self-regulating algorithm inspired by pulsating heat pipe dynamics, where workload intensity alternates between high and low phases to maintain optimal operating conditions. The scheduler integrates real-time thermal feedback from infrared sensors and on-die probes, adjusting task parallelism and frequency scaling to prevent overheating while meeting computational deadlines. Moreover, a memory bandwidth optimizer dynamically switches between strided prefetching and cache-aware data reorganization, further improving efficiency. The system employs a transformer-based reinforcement learning agent to predict optimal pulsation cycles, minimizing energy consumption, deadline misses, and thermal violations. Unlike conventional static schedulers, our method achieves significant improvements in both performance and hardware longevity, particularly for magnetohydrodynamic cosmology and dark matter distribution simulations. Experimental results demonstrate that the proposed architecture reduces energy consumption by up to 27% while maintaining 98% deadline adherence under thermal constraints. This work bridges the gap between high-performance computing and sustainable resource utilization, offering a scalable solution for next-generation cosmic simulations. Biological sciences/Computational biology and bioinformatics Physical sciences/Astronomy and planetary science Physical sciences/Engineering Physical sciences/Nanoscience and technology Physical sciences/Physics 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. 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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-6504227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":446343174,"identity":"3b4d73f8-7584-4e8b-83c8-a5b2a1e8071f","order_by":0,"name":"xiaochen xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYHAC9h8fDCTkSNMjOaPAwpg0LdI8HyoSG4hWbj4jgcFwhoFEet/xBMYPH3OI0CJz5gBDAtAvuTPPPGCWnLmNCC0S7A0MB4G25G64kcDGzEuUFmYGxmYeoMMMiNfC3sDMDNSSQIIWngNsjECHGc4887CZSL9IJLAxfPhTJ893PPngh4/EaGFg4P8AoQ+QEDVQcCCBVB2jYBSMglEwUgAA1ZMycYH6NxYAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"xiaochen","middleName":"","lastName":"xiao","suffix":""}],"badges":[],"createdAt":"2025-04-22 12:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6504227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6504227/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82482674,"identity":"a91875d7-f967-47a3-a510-320b95a0101f","added_by":"auto","created_at":"2025-05-12 04:01:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":833239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6504227/v1_covered_3a9f3aca-7d72-4c76-b222-5a18f07f1a97.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Pulsating Workload Scheduling for Cosmic Simulation: A Thermal-Aware Distributed Computing Architecture","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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-6504227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6504227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We propose an adaptive pulsating workload scheduling architecture for cosmic simulations, which dynamically balances computational intensity with thermal and memory bandwidth efficiency in distributed GPU clusters. The core innovation lies in a self-regulating algorithm inspired by pulsating heat pipe dynamics, where workload intensity alternates between high and low phases to maintain optimal operating conditions. The scheduler integrates real-time thermal feedback from infrared sensors and on-die probes, adjusting task parallelism and frequency scaling to prevent overheating while meeting computational deadlines. Moreover, a memory bandwidth optimizer dynamically switches between strided prefetching and cache-aware data reorganization, further improving efficiency. The system employs a transformer-based reinforcement learning agent to predict optimal pulsation cycles, minimizing energy consumption, deadline misses, and thermal violations. Unlike conventional static schedulers, our method achieves significant improvements in both performance and hardware longevity, particularly for magnetohydrodynamic cosmology and dark matter distribution simulations. 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