In this work we focus on the application phase of AMG preconditioners, and in particular on the choice and implementation of smoothers and coarsest-level solvers capable of exploiting the computational power of clusters of GPUs
An efficient matrix solver is critical to the analytical placement. As the size of the matrix become...
Linear solvers for large and sparse systems are a key element of scientific applications, and their ...
Abstract. Linear systems are required to solve in many scientific applications and the solution of t...
Many scientific applications require the solution of large and sparse linear systems of equations us...
We describe main issues and design principles of an efficient implementation, tailored to recent gen...
Many scientific applications require the solution of large and sparse linear systems of equations us...
The influence of multi-core central processing units and graphics processing units on several algebr...
In this paper, we develop a new parallel auxiliary grid algebraic multigrid (AMG) method to leverage...
We explore a GPU implementation of a Krylov-accelerated algebraic multigrid (AMG) algorithm with fle...
Abstract. Algebraic multigrid methods for large, sparse linear systems are a necessity in many compu...
Algebraic Multigrid (AMG) solvers are an essential component of many large-scale scientific simulati...
We present an efficient, robust and fully GPU-accelerated aggregation-based al-gebraic multigrid pre...
The development of high performance, massively parallel computers and the increasing demands of comp...
This thesis introduces the AscAMG preconditioner, a parallel Algebraic Multigrid Preconditioner for ...
The Algebraic Multigrid (AMG) method has over the years developed into an ecient tool for solving un...
An efficient matrix solver is critical to the analytical placement. As the size of the matrix become...
Linear solvers for large and sparse systems are a key element of scientific applications, and their ...
Abstract. Linear systems are required to solve in many scientific applications and the solution of t...
Many scientific applications require the solution of large and sparse linear systems of equations us...
We describe main issues and design principles of an efficient implementation, tailored to recent gen...
Many scientific applications require the solution of large and sparse linear systems of equations us...
The influence of multi-core central processing units and graphics processing units on several algebr...
In this paper, we develop a new parallel auxiliary grid algebraic multigrid (AMG) method to leverage...
We explore a GPU implementation of a Krylov-accelerated algebraic multigrid (AMG) algorithm with fle...
Abstract. Algebraic multigrid methods for large, sparse linear systems are a necessity in many compu...
Algebraic Multigrid (AMG) solvers are an essential component of many large-scale scientific simulati...
We present an efficient, robust and fully GPU-accelerated aggregation-based al-gebraic multigrid pre...
The development of high performance, massively parallel computers and the increasing demands of comp...
This thesis introduces the AscAMG preconditioner, a parallel Algebraic Multigrid Preconditioner for ...
The Algebraic Multigrid (AMG) method has over the years developed into an ecient tool for solving un...
An efficient matrix solver is critical to the analytical placement. As the size of the matrix become...
Linear solvers for large and sparse systems are a key element of scientific applications, and their ...
Abstract. Linear systems are required to solve in many scientific applications and the solution of t...
DOI
Add to ORKG