Dissertation/ Thesis
Web-site-based partitioning techniques for efficient parallelization of the PageRank computation
| Τίτλος: | Web-site-based partitioning techniques for efficient parallelization of the PageRank computation |
|---|---|
| Συγγραφείς: | Cevahir, Ali |
| Έτος έκδοσης: | 2006 |
| Συλλογή: | Bilkent University: Institutional Repository |
| Θεματικοί όροι: | PageRank, Parallel Sparse-Matrix Vector Multiplication, Graph and Hypergraph Partitioning, QA188 .C49 2006, Sparse matrices Data processing |
| Περιγραφή: | Cataloged from PDF version of article. ; Web search engines use ranking techniques to order Web pages in query results. PageRank is an important technique, which orders Web pages according to the linkage structure of the Web. The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. PageRank computation includes repeated iterative sparse matrix-vector multiplications. Due to the enormous size of the Web matrix to be multiplied, PageRank computations are usually carried out on parallel systems. However, efficiently parallelizing PageRank is not an easy task, because of the irregular sparsity pattern of the Web matrix. Graph and hypergraphpartitioning-based techniques are widely used for efficiently parallelizing matrixvector multiplications. Recently, a hypergraph-partitioning-based decomposition technique for fast parallel computation of PageRank is proposed. This technique aims to minimize the communication overhead of the parallel matrix-vector multiplication. However, the proposed technique has a high prepropocessing time, which makes the technique impractical. In this work, we propose 1D (rowwise and columnwise) and 2D (fine-grain and checkerboard) decomposition models using web-site-based graph and hypergraph-partitioning techniques. Proposed models minimize the communication overhead of the parallel PageRank computations with a reasonable preprocessing time. The models encapsulate not only the matrix-vector multiplication, but the overall iterative algorithm. Conducted experiments show that the proposed models achieve fast PageRank computation with low preprocessing time, compared with those in the literature. ; Cevahir, Ali |
| Τύπος εγγράφου: | thesis |
| Περιγραφή αρχείου: | xii, 78 leaves, graphics; application/pdf |
| Γλώσσα: | English |
| Relation: | https://hdl.handle.net/11693/29894; BILKUTUPB100118 |
| Διαθεσιμότητα: | https://hdl.handle.net/11693/29894 |
| Rights: | info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsbas.2C12C723 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hdl.handle.net/11693/29894# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.2C12C723 RelevancyScore: 758 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 758.004333496094 |
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| Items | – Name: Title Label: Title Group: Ti Data: Web-site-based partitioning techniques for efficient parallelization of the PageRank computation – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cevahir%2C+Ali%22">Cevahir, Ali</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2006 – Name: Subset Label: Collection Group: HoldingsInfo Data: Bilkent University: Institutional Repository – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22PageRank%22">PageRank</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+Sparse-Matrix+Vector+Multiplication%22">Parallel Sparse-Matrix Vector Multiplication</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+and+Hypergraph+Partitioning%22">Graph and Hypergraph Partitioning</searchLink><br /><searchLink fieldCode="DE" term="%22QA188+%2EC49+2006%22">QA188 .C49 2006</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrices+Data+processing%22">Sparse matrices Data processing</searchLink> – Name: Abstract Label: Description Group: Ab Data: Cataloged from PDF version of article. ; Web search engines use ranking techniques to order Web pages in query results. PageRank is an important technique, which orders Web pages according to the linkage structure of the Web. The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. PageRank computation includes repeated iterative sparse matrix-vector multiplications. Due to the enormous size of the Web matrix to be multiplied, PageRank computations are usually carried out on parallel systems. However, efficiently parallelizing PageRank is not an easy task, because of the irregular sparsity pattern of the Web matrix. Graph and hypergraphpartitioning-based techniques are widely used for efficiently parallelizing matrixvector multiplications. Recently, a hypergraph-partitioning-based decomposition technique for fast parallel computation of PageRank is proposed. This technique aims to minimize the communication overhead of the parallel matrix-vector multiplication. However, the proposed technique has a high prepropocessing time, which makes the technique impractical. In this work, we propose 1D (rowwise and columnwise) and 2D (fine-grain and checkerboard) decomposition models using web-site-based graph and hypergraph-partitioning techniques. Proposed models minimize the communication overhead of the parallel PageRank computations with a reasonable preprocessing time. The models encapsulate not only the matrix-vector multiplication, but the overall iterative algorithm. Conducted experiments show that the proposed models achieve fast PageRank computation with low preprocessing time, compared with those in the literature. ; Cevahir, Ali – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Format Label: File Description Group: SrcInfo Data: xii, 78 leaves, graphics; application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://hdl.handle.net/11693/29894; BILKUTUPB100118 – Name: URL Label: Availability Group: URL Data: https://hdl.handle.net/11693/29894 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess – Name: AN Label: Accession Number Group: ID Data: edsbas.2C12C723 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.2C12C723 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: PageRank Type: general – SubjectFull: Parallel Sparse-Matrix Vector Multiplication Type: general – SubjectFull: Graph and Hypergraph Partitioning Type: general – SubjectFull: QA188 .C49 2006 Type: general – SubjectFull: Sparse matrices Data processing Type: general Titles: – TitleFull: Web-site-based partitioning techniques for efficient parallelization of the PageRank computation Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cevahir, Ali IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2006 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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