Various embodiments relating to encoding a sparse matrix into a data structure format that may be efficiently processed via parallel processing of a computing system are provided. In one embodiment, a sparse matrix may be received. A set of designated rows of the sparse matrix may be traversed ...
Sparse matrix is a matrix which contains very few non-zero elements. When a sparse matrix is represented with a 2-dimensional array, we waste a lot of space to represent that matrix. For example, consider a matrix of size 100 X 100 containing only 10 non-zero elements. In this matrix, ...
Exploiting the zeros inside the sparse global stiffness matrix of a large space structure, a general sparse algorithm has been developed for optimization of large structures. An indirect reference data structure has been used to store the nonzero elements of the stiffness matrix in compact form. ...
Algorithms for symmetric matrix square and inverse Cholesky decomposition within the hierarchic framework are also described. The presented data structure is general; in addition to its use in Hartree-Fock/Kohn-Sham calculations, it may also be used in other research areas where matrices with similar...
Algorithm for Delaunay triangulation and convex-hull computation using a sparse matrix A direct algorithm for computing the Delaunay triangulation of 2D data points is presented. The algorithm is based on a sparse-matrix data structure and on... TP Fang,LA Piegl - 《Computer Aided Design》 被引...
•once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations •consider using the COO format when constructing large matrices Data Structure •An array (self.rows) of rows, each of which is a sorted list of column indices of nonzero...
* C++ Program to Implement Sparse Matrix */ #include <iostream> #include <iomanip> #include <string> usingnamespacestd; /* * Class List Declaration */ classList { private: intindex; intvalue; List*nextindex; public: List(intindex) ...
Sparse Matrix Vector-Multiplication is an important operation for many iterative solvers. However, peak performance is limited by the fact that the commonly used algorithm alternates between compute-bound and memory-bound steps. This paper proposes a novel data structure and an FPGA-based hardware cor...
In this chapter, we presented sparse matrix computation as an important parallel pattern. Sparse matrices are important in many real-world applications that involve modeling complex phenomenon. Furthermore, sparse matrix computation is a simple example of data-dependent performance behavior of many large...
In this method, testing for statistical significance of coefficients, and final choice of model using an information criterion, is built into the model generation algorithm. Using simulated data generated from large random sparse VAR models, we test the performance our methods for different lengths ...