Task scheduling algorithm based on priority transformation in heterogeneous platforms

Task scheduling algorithm based on priority transformation in heterogeneous platforms | 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 Task scheduling algorithm based on priority transformation in heterogeneous platforms Chukang Zhong, Jianyuan Wang, Jinbao Chen, Di Wu, Jian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6880893/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In heterogeneous platforms, efficient task scheduling is a crucial condition for achieving high-performance computing. To address this requirement, this paper proposes an improved genetic algorithm and conducts task scheduling based on priority transformation. The algorithm combines a priority queue and a processor mapping queue to form a hybrid encoding of chromosomes in the genetic algorithm. It enhances the implementation methods of selection, crossover, and mutation to improve the efficiency of task scheduling. Adaptive mechanisms, elitism preservation and degradation-extinction mechanisms are introduced to prevent the genetic algorithm from falling into local optima, thereby increasing convergence speed and stabilizing scheduling results. Finally, simulation experiments are conducted, and a CPU-GPU-FPGA heterogeneous platform is built using OpenCL for validation. Compared to standard genetic algorithms and classic algorithms such as HEFT and CPOP, the proposed algorithm demonstrates more stable optimal solution acquisition capabilities and better scheduling performance. Genetic algorithm Priority transformation Task scheduling Heterogeneous computing OpenCL Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 06 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 25 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 13 Jun, 2025 First submitted to journal 12 Jun, 2025 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-6880893","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477766082,"identity":"e59674e7-3ac2-42ff-8cdb-9edb0aba88b8","order_by":0,"name":"Chukang Zhong","email":"","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":false,"prefix":"","firstName":"Chukang","middleName":"","lastName":"Zhong","suffix":""},{"id":477766083,"identity":"971e79fc-ae64-463e-a013-4fffaead13f3","order_by":1,"name":"Jianyuan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYLACCRDB3gDlHSBaCw9MKVFaIPoSiNRicPzs4ReWbYflzSXfHpP4UcMgx3cjgfFzAT4tZ/LSLCTbDhvunJ2XJtlzjMFY8kYCs/QMPFrMDuSYGQC1MG64nWMmwcPGkLjhRgIbMw8+LeffgLXYb7h5xkzyzz+GesJabuQYPwBqARrOYybN28aQYEBIi/2NN2YMEufSkzecyTG2lu2TMJx55mGzND4tkv05xp8lyqxtNxw/Y3jzzTcbeb7jyQc/49MCBGzSkmxwDihWGRvwa2BgYP744Q8hNaNgFIyCUTCiAQCD0E5Ad0Y/iAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":true,"prefix":"","firstName":"Jianyuan","middleName":"","lastName":"Wang","suffix":""},{"id":477766084,"identity":"15fcb7be-61ef-4015-a23d-b896b0661e6b","order_by":2,"name":"Jinbao Chen","email":"","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":false,"prefix":"","firstName":"Jinbao","middleName":"","lastName":"Chen","suffix":""},{"id":477766085,"identity":"12c75b65-3305-4f63-94d5-c6d63b20f98a","order_by":3,"name":"Di Wu","email":"","orcid":"","institution":"China Rong Tong Academy of Sciences Group Corporation Limited","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""},{"id":477766086,"identity":"22121ab5-4ce6-419b-9e6b-a270ad68229c","order_by":4,"name":"Jian Wang","email":"","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-12 13:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6880893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6880893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85636546,"identity":"d78fc76b-a1db-4179-9639-1b59efb5dda7","added_by":"auto","created_at":"2025-06-30 06:07:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676943,"visible":true,"origin":"","legend":"","description":"","filename":"Taskschedulingalgorithmbasedonprioritytransformationinheterogeneousplatforms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6880893/v1_covered_f1bcca61-f4f5-4373-a17f-75926023281f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Task scheduling algorithm based on priority transformation in heterogeneous platforms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic algorithm, Priority transformation, Task scheduling, Heterogeneous computing, OpenCL","lastPublishedDoi":"10.21203/rs.3.rs-6880893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6880893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn heterogeneous platforms, efficient task scheduling is a crucial condition for achieving high-performance computing. To address this requirement, this paper proposes an improved genetic algorithm and conducts task scheduling based on priority transformation. The algorithm combines a priority queue and a processor mapping queue to form a hybrid encoding of chromosomes in the genetic algorithm. It enhances the implementation methods of selection, crossover, and mutation to improve the efficiency of task scheduling. Adaptive mechanisms, elitism preservation and degradation-extinction mechanisms are introduced to prevent the genetic algorithm from falling into local optima, thereby increasing convergence speed and stabilizing scheduling results. Finally, simulation experiments are conducted, and a CPU-GPU-FPGA heterogeneous platform is built using OpenCL for validation. Compared to standard genetic algorithms and classic algorithms such as HEFT and CPOP, the proposed algorithm demonstrates more stable optimal solution acquisition capabilities and better scheduling performance.\u003c/p\u003e","manuscriptTitle":"Task scheduling algorithm based on priority transformation in heterogeneous platforms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 05:35:21","doi":"10.21203/rs.3.rs-6880893/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-16T21:17:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T10:14:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-05T13:04:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-28T14:43:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228529747856239819731261104115435251247","date":"2025-06-27T06:11:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184402194528334426362835010927201508259","date":"2025-06-27T02:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155087341752372483728573701664549776437","date":"2025-06-25T11:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144869034080112574387063988531502941377","date":"2025-06-25T06:35:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T05:53:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T18:33:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-13T08:27:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cluster Computing","date":"2025-06-12T13:30:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"021dd8fe-80d1-44de-bd91-ab51c7d90b84","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-14T10:24:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 05:35:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6880893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6880893","identity":"rs-6880893","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}