A Computational study of sparse or structured matrix operations
Aimaiti, Nuerrennisahan (Nurgul)
University of Lethbridge. Faculty of Arts and Science
Lethbridge, Alta. : Universtiy of Lethbridge, Department of Mathematics and Computer Science
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.
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