A Matrix Inversion Method with YML/OmniRPC on a Large Scale Platform

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

Optimized Sparse Matrix Formats on GT200 and Fermi GPUs

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Grid computing: a case study in hybrid GMRES method

Abstract. Grid computing in general is a special type of parallel computing. It intends to deliver high-performance computing over distributed platforms for computation and data-intensive applications by making use of a very large amount of resources. The GMRES method is used widely to solve the large sparse linear systems. In this paper, we present an effective parallel hybrid asynchronous method, which combines the typical parallel GMRES method with the Least Square method that needs some eigenvalues obtained from a parallel Arnoldi process. And we apply it on a Grid Computing platform Grid5000. From the numeric results, we will present that this hybrid method has some advantage for some real or complex systems compared to the general method GMRES

A Study of SpMV Implementation using MPI and OpenMP on Intel Many-Core Architecture

Abstract. The Sparse Matrix-Vector Multiplication is the key operation in many iterative methods. The widely used CSR (Compressed Sparse Row

Towards a Scheduling Policy For Hybrid Methods on Computational Grids

In this paper, we propose a cost model for running partic- ular component based applications on a computational Grid. This cost is evaluated by a metascheduler and negotiated with the user by a broker. A specific set of applications is considered: hybrid methods, where components have to be launched simultaneously

A grid-based programming approach for distributed linear algebra applications

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Energy Consumption Evaluation for Krylov Methods on Clusters of gPU Accelerators

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Méthodes itératives hybrides asynchrones sur plateformes de calcul hétérogènes pour la résolution accélérée de grands systèmes linéaires

Nous étudions dans cette thèse une méthode hybride de résolution des systèmes linéaires GMRES/LS-Arnoldi qui accélère la convergence grâce à la connaissance des valeurs propres calculées parallèlement par la méthode d Arnoldi dans les cas réels. Le caractère asynchrone de cette méthode présente l avantage de fonctionner avec une architecture hétérogène. Une étude de cas complexe est également faite en effectuant la transformation de la matrice complexe en une matrice réelle de dimension double. Nous avons mis en oeuvre la méthode GMRES hybride ainsi que la méthode GMRES générale sur trois différents types de plates-formes matérielles. Il s agit respectivement de supercalculateurs IBM série SP, plates-formes matérielles typiquement centralisées; de Grid5000, une plate-forme matérielle typiquement distribuée, et de Tsubame (Tokyo-tech Supercomputer and Ubiquitously Accessible Massstorage Environment) supercalculateur, dont certains noeuds sont munis d une carte accélératrice. Nous avons testé les performances de GMRES général et de GMRES hybride sur ces trois plates-formes, en observant l influence des nombreux paramètres sur les performances. Des résultats significatifs ont ainsi été obtenus, nous permettant non seulement d'améliorer les performances du calcul parallèle, mais aussi de préciser le sens de nos efforts futurs.In this thesis, we have studied an effective parallel hybrid method of solving linear systems, GMRES / LS-Arnoldi, which accelerates the convergence through knowledge of some eigenvalues calculated in paralled by the Arnoldi method in real cases. The asynchronous nature of this method has the advantage of working with a heterogeneous architecture. A study in complex cases is also done by transforming the complex matrix into a real matrix of double dimension. We have implemented our hybrid GMRES method and the general GMRES method on three different types of hardware platforms. They are respectively the IBM SP series supercomputer, a typically centralized hardware platform; Grid5000, a fully distributed hardware platform, and the Tsubame (Tokyo-tech Supercomputer and Ubiquitously Accessible Massstorage Environment) supercomputer, where some nodes are equipped with an accelerator card. We have tested the performance of general GMRES and hybrid GMRES on these three platforms, observing the influence of various parameters for the performance. A number of meaningful results have been obtained; we can not only improve the performance of parallel computing but also specify the direction of our future efforts.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Parallel Basic Matrix Algebra on the Grid'5000 Large Scale Distributed Platform

International audienceIn this paper we present a performance evaluation of large scale matrix algebra applications on the Grid'5000 platform. Grid'5000 is a nation wide experimental set of clusters which provide a reconfigurable and highly controllable and monitorable instrument. We test the scalability of the experimental tool and some optimization techniques for large scale matrix algebra applications in grid infrastructures based on an efficient data locality, already presented for non-dedicated grid platforms. This includes persistent data placement and explicit management of local memories on the computational nodes. We discuss the performances of a block-based matrix-vector product and the Gauss-Jordan method for large matrix inversion. As experimental grid middleware we use the XtremWeb system to manage the Grid'5000 computational resources. We also compare these results with those obtained on large non-dedicated computational platforms distributed on two geographic sites in France and Japan, we show the effectiveness of the presented data placement techniques but that some constraints and limitations on the experimentation and underlying tools make scalability and realistic expectations more difficul