Evaluation of the OGF GridRPC Data Management library, and study of its integration into an International Sparse Linear Algebra Expert System

International audienceThe Data Management API for the GridRPC describes an optional API that extends the GridRPC standard. It provides a minimal subset of functions to handle a large set of data operations, among which movement, replication, migration and stickyness. We already showed that its use leads to 1) reduced time to completion of application, since useless transfers are avoided; 2) improved feasibility of some computations, depending on the availability of services and/or storage space constraints; 3) complete code portability between two GridRPC middleware; and 4) seamless interoperability, in our example between the French GridRPC middleware DIET and the Japanese middleware Ninf, distributed on French and Japanese administrative domains respectively, leading to both of them contributing to the same calculus, their respective servers sharing only data through our implementation of the GridRPC DM API. We have extended the implementation of the library and a further integration has been made available into DIET as a back-end of its data manager Dagda. We thus present how the library is used in the International Sparse Linear Algebra Expert System GridTLSE which manages entire expertises for the user, including data transfers, tasks executions, and graphical charts, to help analysing the overall execution. GridTLSE relies on DIET to distribute computations and thus can benefit from the persistency functionalities to provide scientists with faster results when their expertises require the same input matrices. In addition, with the possibility for two middleware to interact in a seamless way as long as they’re using an implementation of the GridRPC Data Management API, new architecture of different domains can easily be integrated to the expert system and thus helps the linear algebra community

MUMPS based approach to parallelize the block cimmino algorithm

The Cimmino method is a row projection method in which the original linear system is divided into subsystems. At every iteration, it computes one projection per subsystem and uses these projections to construct an approximation to the solution of the linear system. The usual parallelization strategy applied in block algorithms is to distribute the different blocks on the different available processors. In this paper, we follow another approach where we do not perform explicitely this block distribution to processors whithin the code, but let the multi-frontal sparse solver MUMPS handle the data distribution and parallelism. The data coming from the subsystems defined by the block partition in the Block Cimmino method are gathered in an unique matrix which is analysed, distributed and factorized in parallel by MUMPS. Our target is to define a methodology for parallelism based only on the functionalities provided by general sparse solver libraries and how efficient this way of doing can be. We relate the development of this new approach from an existing code written in Fortran 77 to the MUMPS-embedded version. The results of the ongoing numerical experiments will be presented in the conferenc

Parallelization of subdomain methods with overlapping for linear and nonlinear convection-diffusion problems

International audienceLinear and nonlinear convection-diffusion problems are considered. The numerical solution of these problems via the Schwarz alternating method is studied. A new class of parallel asynchronous iterative methods with flexible communication is applied. The implementation of parallel asyn-chronous and synchronous algorithms on distributed memory multiprocessors is described. Experimental results obtained on an IBM SP2 by using PVM are presented and analyzed. The interest of asynchronous iterative methods with flexible communication is clearly shown

Partitioning Strategies for the Block Cimmino algorithm (Precond 2011)

Session 9International audienc

Diet-ethic: Fair Scheduling of Optional Computations in GridRPC Middleware

Most HPC platforms require users to submit a pre-determined number of computation requests (also called jobs). Unfortunately, this is cumbersome when some of the computations are optional, i.e., they are not critical, but their completion would improve results. For example, given a deadline, the number of requests to submit for a Monte Carlo experiment is difficult to choose. The more requests are completed, the better the results are, however, submitting too many might overload the platform. Conversely, submitting too few requests may leave resources unused and misses an opportunity to improve the results. This paper introduces and solves the problem of scheduling optional computations. An architecture which auto-tunes the number of requests is proposed, then implemented in the DIET GridRPC middleware. Real-life experiments show that several metrics are improved, such as user satisfaction, fairness and the number of completed requests. Moreover, the solution is shown to be scalable.La plupart des plate-formes HPC demandent à l'utilisateur de soumettre un nombre pré-déterminé de requêtes de calcul (aussi appelées " job "). Malheureusement, cela n'est pas pertinent quand une partie des calculs est optionnelle, c'est-à-dire, que l'exécution des requêtes n'est pas critique pour l'utilisateur, mais que leur complétion pourrait améliorer les résultats. Par exemple, étant donnée une date limite, le nombre de requêtes à soumettre pour une expérience Monte Carlo est difficile à choisir. Plus il y a des requêtes qui sont exécutées, meilleures sont les résultats. Cependant, en soumettant trop de requêtes, on risque de surcharger la plate-forme. À l'opposé, en ne soumettant pas assez de requêtes, les ressources sont sous-exploitées alors qu'elles auraient pu être utilisées pour améliorer les résultats. Cet article introduit et résout le problème d'ordonnancer des requêtes optionnelles. Une architecture qui choisit automatiquement le nombre de requêtes est proposée puis implémentée dans l'intergiciel GridRPC DIET. Les expériences faites sur de vraies plate-formes - telles que Grid'5000 - montrent que plusieurs métriques peuvent être améliorées, telles que la satisfaction des utilisateurs, l'équité et le nombre des requêtes exécutées. Enfin, la solution proposée passe à l'échelle

Diet-ethic: Fair Scheduling of Optional Computations in GridRPC Middleware

Most HPC platforms require users to submit a pre-determined number of computation requests (also called jobs). Unfortunately, this is cumbersome when some of the computations are optional, i.e., they are not critical, but their completion would improve results. For example, given a deadline, the number of requests to submit for a Monte Carlo experiment is difficult to choose. The more requests are completed, the better the results are, however, submitting too many might overload the platform. Conversely, submitting too few requests may leave resources unused and misses an opportunity to improve the results. This paper introduces and solves the problem of scheduling optional computations. An architecture which auto-tunes the number of requests is proposed, then implemented in the DIET GridRPC middleware. Real-life experiments show that several metrics are improved, such as user satisfaction, fairness and the number of completed requests. Moreover, the solution is shown to be scalable.La plupart des plate-formes HPC demandent à l'utilisateur de soumettre un nombre pré-déterminé de requêtes de calcul (aussi appelées " job "). Malheureusement, cela n'est pas pertinent quand une partie des calculs est optionnelle, c'est-à-dire, que l'exécution des requêtes n'est pas critique pour l'utilisateur, mais que leur complétion pourrait améliorer les résultats. Par exemple, étant donnée une date limite, le nombre de requêtes à soumettre pour une expérience Monte Carlo est difficile à choisir. Plus il y a des requêtes qui sont exécutées, meilleures sont les résultats. Cependant, en soumettant trop de requêtes, on risque de surcharger la plate-forme. À l'opposé, en ne soumettant pas assez de requêtes, les ressources sont sous-exploitées alors qu'elles auraient pu être utilisées pour améliorer les résultats. Cet article introduit et résout le problème d'ordonnancer des requêtes optionnelles. Une architecture qui choisit automatiquement le nombre de requêtes est proposée puis implémentée dans l'intergiciel GridRPC DIET. Les expériences faites sur de vraies plate-formes - telles que Grid'5000 - montrent que plusieurs métriques peuvent être améliorées, telles que la satisfaction des utilisateurs, l'équité et le nombre des requêtes exécutées. Enfin, la solution proposée passe à l'échelle

On the Easy Use of Scientific Computing Services for Large Scale Linear Algebra and Parallel Decision Making with the P-Grade Portal

International audienceScientific research is becoming increasingly dependent on the large-scale analysis of data using distributed computing infrastructures (Grid, cloud, GPU, etc.). Scientific computing (Petitet et al. 1999) aims at constructing mathematical models and numerical solution techniques for solving problems arising in science and engineering. In this paper, we describe the services of an integrated portal based on the P-Grade (Parallel Grid Run-time and Application Development Environment) portal (http://www.p-grade.hu) that enables the solution of large-scale linear systems of equations using direct solvers, makes easier the use of parallel block iterative algorithm and provides an interface for parallel decision making algorithms. The ultimate goal is to develop a single sign on integrated multi-service environment providing an easy access to different kind of mathematical calculations and algorithms to be performed on hybrid distributed computing infrastructures combining the benefits of large clusters, Grid or cloud, when needed

ParKerC: Toolbox for Parallel Kernel Clustering Methods

978-1-83953-108-8International audienceA large variety of fields such as biology, information retrieval, image segmentation needs unsupervised methods able to gather data without a priori information on shapes or locality. By investigating a parallel strategy based on overlapping domain decomposition, we present a toolbox which is a parallel implementation of two fully unsupervised kernel methods respectively based on density-based properties and spectral properties in order to treat large data sets in fields of pattern recognition

Algorithmes parallèles asynchrones pour la simulation numérique

En simulation numérique, la discrétisation des problèmes aux limites nous amène à résoudre des systèmes algébriques de grande dimension. Parmi les voies d'investigation et compte tenu de l'évolution actuelle des architectures des ordinateurs, la parallélisation des algorithmes est une solution naturelle pour résoudre ces problèmes. Or lorsqu'on exploite des calculateurs parallèles, les ptems d'attente dus à la synchronisation entre les processus coopérants deviennent pénalisants ; cette perte de temps s'avère d'autant plus considérable en présence de déséquilibre de charge. [...]In numerical simulation, the discretization of boundary value problems lead to the solution of large sparse linear systems. Among the research topics and regard to the evolution of computer architectures, the parallelisation of the algorithms is a natural way to overcome the problems. However, the overhead due to the synchronisation between the processors is the drawback of the use of parallel computers ; the waste of time is even more significant as the load is unbalanced. [...]TOULOUSE-ENSEEIHT (315552331) / SudocSudocFranceF