We pursue the scalable parallel implementation of the factor- ization of band matrices with medium to large bandwidth targeting SMP and multi-core architectures. Our approach decomposes the computation into a large number of fine-grained operations exposing a higher degree of parallelism. The SuperMatrix run-time system allows an out-of-order scheduling of operations that is transparent to the programmer. Exper- imental results for the Cholesky factorization of band matrices on two parallel platforms with sixteen processors demonstrate the scalability of the solution
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factori...
Our experimental results showed that block based algorithms for numerically intensive applications a...
AbstractA new parallel algorithm for the LU factorization of a given dense matrix A is described. Th...
We pursue the scalable parallel implementation of the factor- ization of band matrices with medium ...
Abstract. We pursue the scalable parallel implementation of the factor-ization of band matrices with...
The objective of this paper is to extend, in the context of multicore architectures, the concepts of...
The bottleneck of most data analyzing systems, signal processing systems, and intensive computing sy...
Abstract. The objective of this paper is to extend, in the context of multicore architectures, the c...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
A Choleski method is described and used to solve linear systems of equations that arise in large sca...
We discuss the high-performance parallel implementation and execution of dense linear algebra matrix...
With the emergence of thread-level parallelism as the primary means for continued improvement of per...
In this paper, we investigate how to exploit task-parallelism during the execution of the Cholesky f...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
Solving a system of linear equations is a key problem in the field of engineering and science. Matri...
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factori...
Our experimental results showed that block based algorithms for numerically intensive applications a...
AbstractA new parallel algorithm for the LU factorization of a given dense matrix A is described. Th...
We pursue the scalable parallel implementation of the factor- ization of band matrices with medium ...
Abstract. We pursue the scalable parallel implementation of the factor-ization of band matrices with...
The objective of this paper is to extend, in the context of multicore architectures, the concepts of...
The bottleneck of most data analyzing systems, signal processing systems, and intensive computing sy...
Abstract. The objective of this paper is to extend, in the context of multicore architectures, the c...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
A Choleski method is described and used to solve linear systems of equations that arise in large sca...
We discuss the high-performance parallel implementation and execution of dense linear algebra matrix...
With the emergence of thread-level parallelism as the primary means for continued improvement of per...
In this paper, we investigate how to exploit task-parallelism during the execution of the Cholesky f...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
Solving a system of linear equations is a key problem in the field of engineering and science. Matri...
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factori...
Our experimental results showed that block based algorithms for numerically intensive applications a...
AbstractA new parallel algorithm for the LU factorization of a given dense matrix A is described. Th...