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
Abstract: An Orchestrator coordinates and controls computations at parallel and sequential computing...
An extremely common bottleneck encountered in statistical learning algorithms is inversion of huge c...
This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous mast...
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 ...
The inversion of matrices was calculated on a single transputer and on a network of transputers to s...
We take advantage of the new tasking features in OpenMP to propose advanced task-parallel algorithms...
In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factori...
We extend a two-level task partitioning previously applied to the inversion of dense matrices via Ga...
Matrix inversion for real-time applications can be a challenge for the designers since its computati...
Abstract: An Orchestrator coordinates and controls computations at parallel and sequential computing...
An extremely common bottleneck encountered in statistical learning algorithms is inversion of huge c...
This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous mast...
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 ...
The inversion of matrices was calculated on a single transputer and on a network of transputers to s...
We take advantage of the new tasking features in OpenMP to propose advanced task-parallel algorithms...
In this paper, we tackle the inversion of large-scale dense matrices via conventional matrix factori...
We extend a two-level task partitioning previously applied to the inversion of dense matrices via Ga...
Matrix inversion for real-time applications can be a challenge for the designers since its computati...
Abstract: An Orchestrator coordinates and controls computations at parallel and sequential computing...
An extremely common bottleneck encountered in statistical learning algorithms is inversion of huge c...
This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous mast...