An Alternate GPU-Accelerated Algorithm for Very Large Sparse LU Factorization

GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis

Lower upper (LU) factorization for sparse matrices is the most important computing step for circuit simulation problems. However, parallelizing LU factorization on the graphic processing units (GPUs) turns out to be a difficult problem due to intrinsic data dependence and irregular memory access, which diminish GPU computing power. In this paper, we propose a new sparse LU solver on GPUs for ci...

An Unsymmetric-Pattern Multifrontal Method for Sparse LU Factorization

Sparse matrix factorization algorithms for general problems are typically characterized by irregular memory access patterns that limit their performance on parallel-vector supercomputers. For symmetric problems, methods such as the multifrontal method avoid indirect addressing in the innermost loops by using dense matrix kernels. However, no efficient LU factorization algorithm based primarily ...

Parallel LU Factorization on GPU Cluster

This paper describes our progress in developing software for performing parallel LU factorization of a large dense matrix on a GPU cluster. Three approaches, with increasing software complexity, are considered: (i) a naive “thunking” approach that links the existing parallel ScaLAPACK software library with cuBLAS through a software emulation layer; (ii) a more intrusive magmaBLAS implementation...

LU-AD1 Factorization Algorithm

Parallel Symbolic Factorization for Sparse LU Factorization with Static Pivoting

In this paper we consider a direct method to solve a sparse unsymmetric system of linear equations Ax = b, which is the Gaussian elimination. This elimination consists in explicitly factoring the matrix A into the product of L and U , where L is a unit lower triangular matrix, and U is an upper triangular matrix, followed by solving LUx = b one factor at a time. One of the main characteristics ...