Structural Recomputation Framework: A Deterministic Execution Abstraction for Memory-Constrained Dynamic Programming in Bioinformatics

Structural Recomputation Framework: A Deterministic Execution Abstraction for Memory-Constrained Dynamic Programming in Bioinformatics | 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 Structural Recomputation Framework: A Deterministic Execution Abstraction for Memory-Constrained Dynamic Programming in Bioinformatics MD. Arshad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9034420/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Dynamic programming (DP) algorithms are foundational to bioinformatics, yet their application to genomic-scale data is frequently constrained by a quadratic memory bottleneck. This storage requirement primarily arises from the necessity of persisting traceback matrices or trellis structures to enable path reconstruction and posterior decoding. While specialized solutions such as Hirschberg’s algorithm and algorithm-specific checkpointing schemes have been developed to mitigate these constraints, they often remain tightly coupled to specific recurrences and lack a unified execution abstraction. This work introduces the Structured Recomputation Framework (SRF), a deterministic execution-level abstraction that decouples algorithmic recurrence definitions from physical execution schedules. SRF employs a recurrence-agnostic design and a structured recomputation schedule to enforce strict, user-defined memory upper bounds. The framework’s utility is demonstrated across diverse algorithmic paradigms, including sequence alignment (Needleman-Wunsch), Hidden Markov Model evaluation (Forward and Viterbi), and graph-based dynamic programming. Empirical validation on mitochondrial DNA and Gene Ontology datasets confirms a reduction in peak working set size from O(N2) to O(N), with memory savings exceeding 99% for extreme-scale workloads. Deterministic properties are verified through cross-platform and compiler invariance checks, ensuring bit-exact reproducibility across heterogeneous compute environments. SRF provides a stable foundation for memory-efficient biological computing without requiring improvement in asymptotic time complexity, effectively trading compute cycles for spatial capacity to enable exact analyses on commodity hardware. Dynamic Programming Sequence Alignment Memory Constraints Recomputation Deterministic Execution Numerical Constitutionalism Scientific Provenance Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 07 Mar, 2026 Submission checks completed at journal 07 Mar, 2026 First submitted to journal 04 Mar, 2026 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-9034420","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600901405,"identity":"7bc62a51-17c0-4286-b725-4aa9a103cae1","order_by":0,"name":"MD. 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This storage requirement primarily arises from the necessity of persisting\ntraceback matrices or trellis structures to enable path reconstruction and posterior\ndecoding. While specialized solutions such as Hirschberg’s algorithm and\nalgorithm-specific checkpointing schemes have been developed to mitigate these\nconstraints, they often remain tightly coupled to specific recurrences and lack a\nunified execution abstraction. This work introduces the Structured Recomputation\nFramework (SRF), a deterministic execution-level abstraction that decouples\nalgorithmic recurrence definitions from physical execution schedules. SRF employs a\nrecurrence-agnostic design and a structured recomputation schedule to enforce strict,\nuser-defined memory upper bounds. The framework’s utility is demonstrated across\ndiverse algorithmic paradigms, including sequence alignment (Needleman-Wunsch),\nHidden Markov Model evaluation (Forward and Viterbi), and graph-based dynamic\nprogramming. Empirical validation on mitochondrial DNA and Gene Ontology\ndatasets confirms a reduction in peak working set size from O(N2) to O(N), with\nmemory savings exceeding 99% for extreme-scale workloads. Deterministic properties\nare verified through cross-platform and compiler invariance checks, ensuring bit-exact\nreproducibility across heterogeneous compute environments. SRF provides a stable\nfoundation for memory-efficient biological computing without requiring improvement\nin asymptotic time complexity, effectively trading compute cycles for spatial capacity\nto enable exact analyses on commodity hardware.","manuscriptTitle":"Structural Recomputation Framework: A Deterministic Execution Abstraction for Memory-Constrained Dynamic Programming in Bioinformatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 02:48:09","doi":"10.21203/rs.3.rs-9034420/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"336898805108205809527284761999381838436","date":"2026-05-11T08:53:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T16:22:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-07T13:50:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T05:40:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Algorithms for Molecular Biology","date":"2026-03-05T00:12:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"algorithms-for-molecular-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amob","sideBox":"Learn more about [Algorithms for Molecular Biology](http://almob.biomedcentral.com/)","snPcode":"13015","submissionUrl":"https://submission.nature.com/new-submission/13015/3","title":"Algorithms for Molecular Biology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b127d1b9-9940-40a9-9916-5e275c9edf98","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"336898805108205809527284761999381838436","date":"2026-05-11T08:53:26+00:00","index":33,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T16:38:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 02:48:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9034420","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9034420","identity":"rs-9034420","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}