Topics
linear algebraOrganisation
matrix multiplicationMilitary conflict
National audienceIn this paper, we deal with algorithmic issues on heterogeneous platforms. We concentrate on dense linear algebra kernels, such as matrix multiplication or LU decomposition. Block cyclic distribution techniques used in ScaLAPACK are no longer sufficient to balance the load among processors running at different speeds. The main result of this paper is to provide a static data distribution scheme that leads to an asymptotically perfect load balancing for LU decomposition, thereby providing solid foundations toward the design of a cluster-oriented version of ScaLAPACK
Abstract—Two strategies of distribution of computations can be used to implement parallel solvers fo...
International audienceWe study the implementation of dense linear algebra computations, such as matr...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
National audienceIn this paper, we deal with algorithmic issues on heterogeneous platforms. We conce...
In this paper, we deal with algorithmic issues on heterogeneous platforms. We concentrate on dense l...
This paper discusses some algorithmic issues when computing with a heterogeneous network of workstat...
International audienceThis paper discusses some algorithmic issues when computing with a heterogeneo...
(eng) Implementing linear algebra kernels on distributed memory parallel computers raises the proble...
In this paper, we deal with redistribution issues for dense linear algebra kernels on heterogeneous ...
This paper describes the design of ScaLAPACK, a scalable software library for performing dense and b...
In this paper, we deal with redistribution issues for dense linear algebra kernels on heterogeneous ...
In this paper, we present a new load balancing technique, called panel scattering, which is generall...
This paper discusses the design and the implementation of the LU factorization routines included in ...
(eng) We study the implementation of dense linear algebra computations, such as matrix multiplicatio...
International audienceLarge clusters and supercomputers are rapidly evolving and may be subject to r...
Abstract—Two strategies of distribution of computations can be used to implement parallel solvers fo...
International audienceWe study the implementation of dense linear algebra computations, such as matr...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
National audienceIn this paper, we deal with algorithmic issues on heterogeneous platforms. We conce...
In this paper, we deal with algorithmic issues on heterogeneous platforms. We concentrate on dense l...
This paper discusses some algorithmic issues when computing with a heterogeneous network of workstat...
International audienceThis paper discusses some algorithmic issues when computing with a heterogeneo...
(eng) Implementing linear algebra kernels on distributed memory parallel computers raises the proble...
In this paper, we deal with redistribution issues for dense linear algebra kernels on heterogeneous ...
This paper describes the design of ScaLAPACK, a scalable software library for performing dense and b...
In this paper, we deal with redistribution issues for dense linear algebra kernels on heterogeneous ...
In this paper, we present a new load balancing technique, called panel scattering, which is generall...
This paper discusses the design and the implementation of the LU factorization routines included in ...
(eng) We study the implementation of dense linear algebra computations, such as matrix multiplicatio...
International audienceLarge clusters and supercomputers are rapidly evolving and may be subject to r...
Abstract—Two strategies of distribution of computations can be used to implement parallel solvers fo...
International audienceWe study the implementation of dense linear algebra computations, such as matr...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
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