Academic Journal
Multi-Level Parallel CPU Execution Method for Accelerated Portion-Based Variant Call Format Data Processing.
| Title: | Multi-Level Parallel CPU Execution Method for Accelerated Portion-Based Variant Call Format Data Processing. |
|---|---|
| Authors: | Mochurad, Lesia, Tsmots, Ivan, Mostova, Vita, Kystsiv, Karina |
| Source: | Computation; Feb2026, Vol. 14 Issue 2, p48, 20p |
| Subject Terms: | Parallel processing, Genetic mutation, Computer multitasking, High performance computing, Compilers (Computer programs), Resource allocation |
| Abstract: | This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a formally defined local processing scheme and integrates three coordinated levels of parallelism: block-based partitioning of file-backed VCF portions read sequentially into localized fragments with data-level parallel processing; task-level decomposition of feature construction into independent transformations; and execution-level specialization via JIT compilation of numerical kernels. To prevent performance degradation caused by nested parallelism, a resource-control mechanism is introduced as an execution rule that bounds effective parallelism and mitigates oversubscription, improving throughput stability on a single multi-core CPU node. Experiments on a public chromosome-17 VCF dataset for BRCA1-region pathogenicity classification demonstrate that the proposed multi-level local CPU execution (parsing/filtering, feature construction, and JIT-specialized numeric kernels) reduces runtime from 291.25 s (sequential) to 73.82 s, yielding a 3.95× speedup. When combined with resource-coordinated parallel model training, the end-to-end runtime further decreases to 51.18 s, corresponding to a 5.69× speedup, while preserving classification quality (accuracy 0.8483, precision 0.8758, recall 0.8261, F1 0.8502). A stage-wise ablation analysis quantifies the contribution of each execution level and confirms consistent scaling under resource-bounded execution. [ABSTRACT FROM AUTHOR] |
| Copyright of Computation is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Biomedical Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edm&genre=article&issn=20793197&ISBN=&volume=14&issue=2&date=20260201&spage=48&pages=48-67&title=Computation&atitle=Multi-Level%20Parallel%20CPU%20Execution%20Method%20for%20Accelerated%20Portion-Based%20Variant%20Call%20Format%20Data%20Processing.&aulast=Mochurad%2C%20Lesia&id=DOI:10.3390/computation14020048 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edm DbLabel: Biomedical Index An: 192040240 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1041.06896972656 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multi-Level Parallel CPU Execution Method for Accelerated Portion-Based Variant Call Format Data Processing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mochurad%2C+Lesia%22">Mochurad, Lesia</searchLink><br /><searchLink fieldCode="AR" term="%22Tsmots%2C+Ivan%22">Tsmots, Ivan</searchLink><br /><searchLink fieldCode="AR" term="%22Mostova%2C+Vita%22">Mostova, Vita</searchLink><br /><searchLink fieldCode="AR" term="%22Kystsiv%2C+Karina%22">Kystsiv, Karina</searchLink> – Name: TitleSource Label: Source Group: Src Data: Computation; Feb2026, Vol. 14 Issue 2, p48, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+mutation%22">Genetic mutation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+multitasking%22">Computer multitasking</searchLink><br /><searchLink fieldCode="DE" term="%22High+performance+computing%22">High performance computing</searchLink><br /><searchLink fieldCode="DE" term="%22Compilers+%28Computer+programs%29%22">Compilers (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes and experimentally evaluates a multi-level CPU-oriented execution method for high-throughput portion-based processing of file-backed Variant Call Format (VCF) data and automated mutation classification. The approach is based on a formally defined local processing scheme and integrates three coordinated levels of parallelism: block-based partitioning of file-backed VCF portions read sequentially into localized fragments with data-level parallel processing; task-level decomposition of feature construction into independent transformations; and execution-level specialization via JIT compilation of numerical kernels. To prevent performance degradation caused by nested parallelism, a resource-control mechanism is introduced as an execution rule that bounds effective parallelism and mitigates oversubscription, improving throughput stability on a single multi-core CPU node. Experiments on a public chromosome-17 VCF dataset for BRCA1-region pathogenicity classification demonstrate that the proposed multi-level local CPU execution (parsing/filtering, feature construction, and JIT-specialized numeric kernels) reduces runtime from 291.25 s (sequential) to 73.82 s, yielding a 3.95× speedup. When combined with resource-coordinated parallel model training, the end-to-end runtime further decreases to 51.18 s, corresponding to a 5.69× speedup, while preserving classification quality (accuracy 0.8483, precision 0.8758, recall 0.8261, F1 0.8502). A stage-wise ablation analysis quantifies the contribution of each execution level and confirms consistent scaling under resource-bounded execution. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Computation is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edm&AN=192040240 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/computation14020048 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 48 Subjects: – SubjectFull: Parallel processing Type: general – SubjectFull: Genetic mutation Type: general – SubjectFull: Computer multitasking Type: general – SubjectFull: High performance computing Type: general – SubjectFull: Compilers (Computer programs) Type: general – SubjectFull: Resource allocation Type: general Titles: – TitleFull: Multi-Level Parallel CPU Execution Method for Accelerated Portion-Based Variant Call Format Data Processing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mochurad, Lesia – PersonEntity: Name: NameFull: Tsmots, Ivan – PersonEntity: Name: NameFull: Mostova, Vita – PersonEntity: Name: NameFull: Kystsiv, Karina IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20793197 Numbering: – Type: volume Value: 14 – Type: issue Value: 2 Titles: – TitleFull: Computation Type: main |
| ResultId | 1 |