Dissertation/ Thesis
Προβολή σε EDS

A Computational study of sparse or structured matrix operations

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: A Computational study of sparse or structured matrix operations
Συγγραφείς: Aimaiti, Nuerrennisahan (Nurgul), University of Lethbridge. Faculty of Arts and Science
Συνεισφορές: Hossain, Shahadat
Στοιχεία εκδότη: Universtiy of Lethbridge, Department of Mathematics and Computer Science
Department of Mathematics and Computer Science
Arts and Science
Έτος έκδοσης: 2018
Συλλογή: University of Lethbridge Institutional Repository
Θεματικοί όροι: Sparse matrices -- Data processing, Java (Computer program language), Algebras, linear, High performance computing, Mathematical optimization -- Data processing, Numerical calculations -- Data processing, sparse data structure, CRS, Compressed Row Storage, JSA, Java Sparse Array, diagonal, BLAS, Basic Linear Algebra Subroutines
Περιγραφή: Matrix computation is an important area in high-performance scientific computing. Major computer manufacturers and vendors typically provide architecture- aware implementation libraries such as Basic Linear Algebra Subroutines (BLAS). In this thesis, we perform an experimental study of a subset of matrix operations, where the matrices are dense, sparse, or structured in Java. We implement a subset of BLAS operations in Java and compare their performance with standard data structures Compressed Row Storage (CRS) and Java Sparse Array (JSA) for dense and sparse structured matrices. The diagonal storage format is shown to be a viable alternative for dense and structured matrices.
Τύπος εγγράφου: thesis
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: Thesis (University of Lethbridge. Faculty of Arts and Science); https://hdl.handle.net/10133/5268
Διαθεσιμότητα: https://hdl.handle.net/10133/5268
Αριθμός Καταχώρησης: edsbas.FAD7E847
Βάση Δεδομένων: BASE
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  – Url: https://hdl.handle.net/10133/5268#
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PubType: Dissertation/ Thesis
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  Data: A Computational study of sparse or structured matrix operations
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  Data: Hossain, Shahadat
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  Data: Universtiy of Lethbridge, Department of Mathematics and Computer Science<br />Department of Mathematics and Computer Science<br />Arts and Science
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  Data: 2018
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  Data: <searchLink fieldCode="DE" term="%22Sparse+matrices+--+Data+processing%22">Sparse matrices -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Java+%28Computer+program+language%29%22">Java (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Algebras%22">Algebras</searchLink><br /><searchLink fieldCode="DE" term="%22linear%22">linear</searchLink><br /><searchLink fieldCode="DE" term="%22High+performance+computing%22">High performance computing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization+--+Data+processing%22">Mathematical optimization -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+calculations+--+Data+processing%22">Numerical calculations -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22sparse+data+structure%22">sparse data structure</searchLink><br /><searchLink fieldCode="DE" term="%22CRS%22">CRS</searchLink><br /><searchLink fieldCode="DE" term="%22Compressed+Row+Storage%22">Compressed Row Storage</searchLink><br /><searchLink fieldCode="DE" term="%22JSA%22">JSA</searchLink><br /><searchLink fieldCode="DE" term="%22Java+Sparse+Array%22">Java Sparse Array</searchLink><br /><searchLink fieldCode="DE" term="%22diagonal%22">diagonal</searchLink><br /><searchLink fieldCode="DE" term="%22BLAS%22">BLAS</searchLink><br /><searchLink fieldCode="DE" term="%22Basic+Linear+Algebra+Subroutines%22">Basic Linear Algebra Subroutines</searchLink>
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  Data: Matrix computation is an important area in high-performance scientific computing. Major computer manufacturers and vendors typically provide architecture- aware implementation libraries such as Basic Linear Algebra Subroutines (BLAS). In this thesis, we perform an experimental study of a subset of matrix operations, where the matrices are dense, sparse, or structured in Java. We implement a subset of BLAS operations in Java and compare their performance with standard data structures Compressed Row Storage (CRS) and Java Sparse Array (JSA) for dense and sparse structured matrices. The diagonal storage format is shown to be a viable alternative for dense and structured matrices.
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  Data: Thesis (University of Lethbridge. Faculty of Arts and Science); https://hdl.handle.net/10133/5268
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: Sparse matrices -- Data processing
        Type: general
      – SubjectFull: Java (Computer program language)
        Type: general
      – SubjectFull: Algebras
        Type: general
      – SubjectFull: linear
        Type: general
      – SubjectFull: High performance computing
        Type: general
      – SubjectFull: Mathematical optimization -- Data processing
        Type: general
      – SubjectFull: Numerical calculations -- Data processing
        Type: general
      – SubjectFull: sparse data structure
        Type: general
      – SubjectFull: CRS
        Type: general
      – SubjectFull: Compressed Row Storage
        Type: general
      – SubjectFull: JSA
        Type: general
      – SubjectFull: Java Sparse Array
        Type: general
      – SubjectFull: diagonal
        Type: general
      – SubjectFull: BLAS
        Type: general
      – SubjectFull: Basic Linear Algebra Subroutines
        Type: general
    Titles:
      – TitleFull: A Computational study of sparse or structured matrix operations
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            NameFull: Aimaiti, Nuerrennisahan (Nurgul)
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              Type: published
              Y: 2018
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