We consider the implementation of a frontal code for the solution of large sparse unsymmetric linear systems on a high-performance computer where data must be in the cache before arithmetic operations can be performed on it. In particular, we show how we can modify the frontal solution algorithm to enhance the proportion of arithmetic operations performed using Level 3 BLAS thus enabling better reuse of data in the cache. We illustrate the e¤ects of this on Silicon Graphics Power Challenge machines using problems which arise in real engineering and industrial applications. ( 1998 John Wiley & Sons, Ltd. KEY WORDS: unsymmetric sparse matrices; frontal solver; direct methods; Þnite elements; BLAS; computational kernel
AbstractWe examine the computational efficiency of linear algebra components in iterative solvers fo...
In nite element simulations, the overall computing time is dominated by the time needed to solve lar...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...
We describethe design, implementation, and performance of a frontal code for the solution of large, ...
In previous work, a cache-aware sparse matrix multiplication for linear programming interior point m...
In recent years there have been a number of important developments in frontal algorithms for solving...
In previous work, a cache-aware sparse matrix multiplication for linear programming interior point m...
For many finite element problems, when represented as sparse matrices, iterative solvers are found t...
SIGLEAvailable from British Library Document Supply Centre-DSC:8715.1804(CLRC-RAL-TR--97-001) / BLDS...
This report has been developed over the work done in the deliverable [Nava94] There it was shown tha...
AbstractWe review the influence of the advent of high-performance computing on the solution of linea...
This dissertation presents an architecture to accelerate sparse matrix linear algebra,which is among...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
AbstractWe examine the computational efficiency of linear algebra components in iterative solvers fo...
In nite element simulations, the overall computing time is dominated by the time needed to solve lar...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...
We describethe design, implementation, and performance of a frontal code for the solution of large, ...
In previous work, a cache-aware sparse matrix multiplication for linear programming interior point m...
In recent years there have been a number of important developments in frontal algorithms for solving...
In previous work, a cache-aware sparse matrix multiplication for linear programming interior point m...
For many finite element problems, when represented as sparse matrices, iterative solvers are found t...
SIGLEAvailable from British Library Document Supply Centre-DSC:8715.1804(CLRC-RAL-TR--97-001) / BLDS...
This report has been developed over the work done in the deliverable [Nava94] There it was shown tha...
AbstractWe review the influence of the advent of high-performance computing on the solution of linea...
This dissertation presents an architecture to accelerate sparse matrix linear algebra,which is among...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
AbstractWe examine the computational efficiency of linear algebra components in iterative solvers fo...
In nite element simulations, the overall computing time is dominated by the time needed to solve lar...
The need to solve large sparse linear systems of equations efficiently lies at the heart of many app...