International audienceYML is a dedicated framework to develop and run parallel applications over a large scale middleware. This framework makes eas- ier the use of a grid and provides a high level programming tool. It is independent from middlewares and users are not in charge to manage communications. In consequence, it introduces a new level of commu- nications and it generates an overhead. In this paper, we proposed to showed the overhead of YML is tolerable in comparison to a direct use of a middleware. This is based on a matrix inversion method and a large scale platform, Grid'5000
In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factori...
This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous mast...
International audienceTo exploit the potential of multicore architectures, recent dense linear algeb...
International audienceYML is a dedicated framework to develop and run parallel applications over a l...
International audienceIn this paper we present a performance evaluation of large scale matrix algebr...
xxxxIn this paper we propose a framework dedicated to the development and the execution of parallel ...
International audienceIn this paper, we focus on a distributed and parallel programming paradigm for...
This paper presents a parallel out-of-core algorithm to invert huge matrices, that is when size of m...
We study the use of massively parallel architectures for computing a matrix inverse. Two different ...
We take advantage of the new tasking features in OpenMP to propose advanced task-parallel algorithms...
We extend a two-level task partitioning previously applied to the inversion of dense matrices via Ga...
The inversion of matrices was calculated on a single transputer and on a network of transputers to s...
Matrix inversion for real-time applications can be a challenge for the designers since its computati...
In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factori...
This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous mast...
International audienceTo exploit the potential of multicore architectures, recent dense linear algeb...
International audienceYML is a dedicated framework to develop and run parallel applications over a l...
International audienceIn this paper we present a performance evaluation of large scale matrix algebr...
xxxxIn this paper we propose a framework dedicated to the development and the execution of parallel ...
International audienceIn this paper, we focus on a distributed and parallel programming paradigm for...
This paper presents a parallel out-of-core algorithm to invert huge matrices, that is when size of m...
We study the use of massively parallel architectures for computing a matrix inverse. Two different ...
We take advantage of the new tasking features in OpenMP to propose advanced task-parallel algorithms...
We extend a two-level task partitioning previously applied to the inversion of dense matrices via Ga...
The inversion of matrices was calculated on a single transputer and on a network of transputers to s...
Matrix inversion for real-time applications can be a challenge for the designers since its computati...
In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factori...
This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous mast...
International audienceTo exploit the potential of multicore architectures, recent dense linear algeb...