Skewed graph partitioning

Graph partitioning is an important abstraction used in solving many scientific computing problems. Unfortunately, the standard partitioning model does not incorporate considerations that are important in many settings. We address this by describing a generalized partitioning model which incorporates the notion of partition skew and is applicable to a variety of problems. We then develop enhancements to several important partitioning algorithms necessary to solve the generalized partitioning problem. Finally we demonstrate the benefit of employing several of these generalized methods to static decomposition of parallel computing problems

Optimal Task Mapping for NEMO Model

The climate numerical models require a considerable amount of computing power. The modern parallel architectures provide the needed computing power to perform scientific simulations at acceptable resolutions. However, the efficiency of the exploitation of the parallel architectures by the climate models is often poor. Several factors influence the parallel efficiency such as the parallel overhead due to the communications among concurrent tasks, the memory contention among tasks on the same computing node, the load balancing and the tasks synchronization. The work here described aims at addressing two of the factors influencing the efficiency: the communications and the memory contention. The used approach is based on the optimal mapping of the tasks on the SMP nodes of a parallel cluster. The best mapping can heavily influence the time spent for communications between tasks belonging to the same node either to different nodes. Moreover, if we consider that each parallel task will allocate different amount of memory, the optimal tasks mapping can balance the total amount of main memory allocated on the same node and hence reduce the overall memory contention. The climate model taken into consideration is PELAGOS025 made by coupling the NEMO oceanic model with the BFM biogeochemical model. It has been used in a global configuration with a horizontal resolution of 0.25◦. Three different mapping strategies have been implemented, analyzed and compared with the standard allocation performed by the local scheduler. The parallel architecture used for the evaluation is an IBM iDataPlex with Intel SandyBridge processors located at the CMCC’s Supercomputing Center

Adaptation au repartitionnement de graphes d'une méthode d'optimisation globale par diffusion

National audienceLa résolution parallèle efficace de simulations numériques dont les coûts de calcul évoluent au cours du temps (telles que celles mettant en oeuvre un remaillage dynamique) nécessite des méthodes efficaces de redistribution des données attribuées à chaque processeur. L'objet de cet article est d'étudier l'adaptation au repartitionnement de graphes, qui est un modèle courant du problème de redistribution des données, d'un ensemble d'algorithmes utilisés avec succès dans le cadre du partitionnement parallèle de graphes, et tout particulièrement d'un algorithme d'optimisation globale de la forme des partitions par diffusion. Le modèle expérimental exposé ici est séquentiel,mais les algorithmes présentés peuvent facilement être transposés en parallèle. Afin de valider notre approche, nous comparons nos résultats, implémentés au sein du logiciel SCOTCH, à la routine de repartitionnement mise en oeuvre au sein du logiciel ParMETIS

Toward a scalable refinement strategy for multilevel graph repartitioning

Équipe-projet BacchusDynamic load balancing is a mandatory feature for parallel software whose workload evolves with time, such as solvers implementing adaptive mesh refinement. In such solvers, problem space is most often represented as an unstructured mesh, and graph partitioning is used to distribute data and their associated computations across processes. The purpose of this paper is to study the sequential version of a set of graph repartitioning methods and to propose a scalable strategy for parallel graph repartitioning. As the repartitioning methods have been adapted from existing algorithms that are used for parallel partitioning, we will also be able to discuss their parallel behaviors. These methods can be combined in several ways, leading to either multilevel diffusion-based or biased scratch-remap frameworks. The proposed repartitioning framework uses a global diffusion-based algorithm for partition refinement, which may prove a good replacement for Fiduccia-Mattheyses type algorithms as it is inherently parallel. This algorithm yields best results when used in a multilevel framework. To validate our approach, we compare our sequential graph repartitioning implementation within the Scotch software to the ParMeTiS graph repartitioning tool.L'équilibrage dynamique de la charge est une fonctionnalité indispensable aux applications parallèles dont la quantité de calcul évolue en fonction du temps, tels les solveurs utilisant du raffinement de maillage. Dans le cas de ces solveurs, l'espace du problème est le plus souvent représenté par un maillage non structuré et l'on utilise le partitionnement de graphes afin de distribuer sur les processeurs les données et les calculs associés. L'objectif de cet article est d'étudier la version séquentielle d'un ensemble de méthodes de repartitionnement de graphes et de proposer une stratégie scalable pour le repartitionnement parallèle de graphes. Étant donné que ces méthodes de repartitionnement ont été adaptées à partir d'algorithmes existants utilisés pour le partitionnement parallèle, nous aborderons aussi leur comportement parallèle. Ces méthodes peuvent être combinées de plusieurs manières, aboutissant à des plateformes de repartitionnement basées soit sur une diffusion multi-niveaux, soit sur une méthode de type scratch-remap biaisée. La plateforme de repartitionnement que nous proposons utilise un algorithme de diffusion global pour le raffinement de partitions. Celui-ci, du fait qu'il est intrinséquement parallèle, pourrait constituer un bon remplaçant des algorithmes de type Fiduccia-Mattheyses. Cet algorithme donne de meilleurs résultats lorsqu'il est utilisé au sein d'un schéma multi-niveaux. Afin de valider notre approche, nous comparons notre implémentation d'algorithmes de repartitionnement séquentiel de graphes, réalisée au sein du logiciel Scotch, au logiciel de repartitionnement de graphes ParMeTiS

Repartitionnement d'un graphe de M vers N processeurs : application pour l'équilibrage dynamique de charge

National audienceL'équilibrage dynamique de charge est une étape cruciale qui conditionne la performance des codes adaptatifs dont l'évolution de la charge est difficilement prévisible. Néanmoins, l'ensemble des travaux dans ce domaine se limitent -- à notre connaissance -- au cas où le nombre de processeurs est fixé initialement et n'est pas remis en cause lors de l'équilibrage. Cela peut s'avérer particulièrement inefficace, notamment du point de vue de la consommation des ressources. Nous proposons dans cet article un nouvel algorithme de repartitionnement de graphe permettant de faire varier le nombre de processeurs, en supposant que la charge du graphe n'est pas modifiée. Cet algorithme optimise conjointement la coupe et la migration en s'appuyant sur un modèle de partitionnement de graphe à sommets fixes. Des résultats expérimentaux valident nos travaux en les comparant à d'autres approches

Partitioning workflow applications over federated clouds to meet non-functional requirements

PhD ThesisWith cloud computing, users can acquire computer resources when they need them on a pay-as-you-go business model. Because of this, many applications are now being deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly, all these various infrastructure providers o er services with di erent levels of quality. For example, cloud data centres are governed by the privacy and security policies of the country where the centre is located, while many organisations have created their own internal \private cloud" to meet security needs. With all this varieties and uncertainties, application developers who decide to host their system in the cloud face the issue of which cloud to choose to get the best operational conditions in terms of price, reliability and security. And the decision becomes even more complicated if their application consists of a number of distributed components, each with slightly di erent requirements. Rather than trying to identify the single best cloud for an application, this thesis considers an alternative approach, that is, combining di erent clouds to meet users' non-functional requirements. Cloud federation o ers the ability to distribute a single application across two or more clouds, so that the application can bene t from the advantages of each one of them. The key challenge for this approach is how to nd the distribution (or deployment) of application components, which can yield the greatest bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a framework, to partition a work ow-based application over federated clouds in order to exploit the strengths of each cloud. The speci c goal is to split a distributed application structured as a work ow such that the security and reliability requirements of each component are met, whilst the overall cost of execution is minimised. To achieve this, we propose and evaluate a cloud broker for partitioning a work ow application over federated clouds. The broker integrates with the e-Science Central cloud platform to automatically deploy a work ow over public and private clouds. We developed a deployment planning algorithm to partition a large work ow appli- - i - cation across federated clouds so as to meet security requirements and minimise the monetary cost. A more generic framework is then proposed to model, quantify and guide the partitioning and deployment of work ows over federated clouds. This framework considers the situation where changes in cloud availability (including cloud failure) arise during work ow execution

Dynamic Load-Balancing with Variable Number of Processors based on Graph Repartitioning

Dynamic load balancing is an important step conditioning the performance of parallel adaptive codes whose load evolution is difficult to predict. Most of the works which answer this problem perform well, but are limited to an initially fixed number of processors which is not modified at runtime. These approaches can be very inefficient, especially in terms of resource consumption. In this paper, we present a new graph repartitioning algorithm which accepts a variable number of processors, assuming the load is already balanced. Our algorithm minimizes both data communication and data migration overheads, while maintaining the computational load balanced. This algorithm is based on a theoretical result, that constructs optimal communication matrices with both a minimum migration volume and a minimum number of communications. An experimental study which compares our work against state-of-the-art approaches is presented

Load balancing fictions, falsehoods and fallacies

Abstract Effective use of a parallel computer requires that a calculation be carefully divided among the processors. This load balancing problem appears in many guises and has been a fervent area of research for the past decade or more. Although great progress has been made, and useful software tools developed, a number of challenges remain. It is the conviction of the author that these challenges will be easier to address if we first come to terms with some significant shortcomings in our current perspectives. This paper tries to identify several areas in which the prevailing point of view is either mistaken or insufEcient. The goal is to motivate new ideas and directions for this important field

Équilibrage dynamique avec nombre variable de processeurs par une méthode de repartitionnement de graphe

National audienceL'équilibrage dynamique de charge est une étape cruciale qui conditionne la performance des codes adaptatifs dont l'évolution de la charge est difficilement prévisible. Néanmoins, l'ensemble des travaux dans ce domaine se limitent--à notre connaissance--au cas où le nombre de processeurs est fixé initialement et n'est pas remis en cause lors de l'équilibrage. Cela peut s'avérer particulièrement inefficace, notamment du point de vue de la consommation des ressources. Nous proposons dans cet article deux nouveaux algorithmes de repartitionnement de graphe permettant de faire varier le nombre de processeurs, en supposant que la charge du graphe est déjà équilibrée. Ces algorithmes optimisent conjointement la coupe et la migration des données en s'appuyant sur un modèle de partitionnement de graphe à sommets fixes. Des résultats expérimentaux valident nos travaux en les comparant à d'autres approches