This repository contains 5 sections of codes to solve matrix computations of various types. The following codes used the MPI/OpenMP programming paradigm, and were tested on four (or eight) nodes of the MC cluster.
Block banded triangular solver to solve Lx = b, where L is a banded lower triangular matrix with m subdiagonals.
Solve Lˆy = f, where Lˆ is a banded lower triangular matrix of order N = 32, 768 = 215, with t = 128 subdiagonals on a cluster of multicore nodes.
The SPIKE-like algorithm should be implemented using MPI. In this algorithm, use BBTS (from Part (a)) to solve the system involvings Lˆi.
Solve the dense linear system Ax = b using the approximate block LU Factorization algorithm with the procedure of ”diagonal boosting”
Let α be a multiple of the unit roundoff, e.g. 10−6, and aj be the jth column of the updated matrix A after step j − 1. In step j, if the diagonal pivot does not satisfy |pivot| > α||aj ||1, its value is ”boosted” as, pivot = pivot + β||aj ||1, if pivot > 0, pivot = pivot − β||aj ||1, if pivot < 0, where β is often taken as the square root of α.
Implement the Spike algorithm in Chapter 5.2.1 to solve Ax = b, where A is a banded matrix, on a cluster of multicore nodes.
Data Source: http://yifanhu.net/GALLERY/GRAPHS/search.html
Designed a parallel C++ solver with libraries MPI, MKL and LAPACK based on Kaczmarz scheme to solve a sparse linear system Ax = f of order 1 million
Implemented the RCK scheme to do reordering in sparse matrix and utilized Block Gauss-Seidel and permutations to transform the model into independent least square problems within a parallel Conjugate Gradient framework, resulting in a more robust and scalable method compared with GMRES using ILU preconditioner
Textbook: Parallelism in Matrix Computations
Jul.3 2016