Exceptions for Algorithmic Skeletons

to appearInternational audienceAlgorithmic Skeletons offer high-level abstractions for parallel programming based on recurrent parallelism patterns. Patterns can be combined and nested into more complex parallelism behaviors. Programmers fill the skeleton patterns with the functional (business) code, which transforms the generic skeleton into a specific application. However, when the functional code generate exceptions, this exposes the programmer to details of the skeleton library, breaking the high-level abstraction principle. Furthermore, related parallel activities must be stopped as the exception is raised. This paper describes how to handle exceptions in Algorithmic Skeletons without breaking the high-level abstractions of the programming model. We describe both the behavior of the framework in a formal way, and its implementation in Java: the Skandium Library

Making distributed computing infrastructures interoperable and accessible for e-scientists at the level of computational workflows

As distributed computing infrastructures evolve, and as their take up by user communities is growing, the importance of making different types of infrastructures based on a heterogeneous set of middleware interoperable is becoming crucial. This PhD submission, based on twenty scientific publications, presents a unique solution to the challenge of the seamless interoperation of distributed computing infrastructures at the level of workflows. The submission investigates workflow level interoperation inside a particular workflow system (intra-workflow interoperation), and also between different workflow solutions (inter-workflow interoperation). In both cases the interoperation of workflow component execution and the feeding of data into these components workflow components are considered. The invented and developed framework enables the execution of legacy applications and grid jobs and services on multiple grid systems, the feeding of data from heterogeneous file and data storage solutions to these workflow components, and the embedding of non-native workflows to a hosting meta-workflow. Moreover, the solution provides a high level user interface that enables e-scientist end-users to conveniently access the interoperable grid solutions without requiring them to study or understand the technical details of the underlying infrastructure. The candidate has also developed an application porting methodology that enables the systematic porting of applications to interoperable and interconnected grid infrastructures, and facilitates the exploitation of the above technical framework

LAYSI: A layered approach for SLA-violation propagation in self-manageable cloud infrastructures

Cloud computing represents a promising comput ing paradigm where computing resources have to be allocated to software for their execution. Self-manageable Cloud in frastructures are required to achieve that level of flexibility on one hand, and to comply to users' requirements speci fied by means of Service Level Agreements (SLAs) on the other. Such infrastructures should automatically respond to changing component, workload, and environmental conditions minimizing user interactions with the system and preventing violations of agreed SLAs. However, identification of sources responsible for the possible SLA violation and the decision about the reactive actions necessary to prevent SLA violation is far from trivial. First, in this paper we present a novel approach for mapping low-level resource metrics to SLA parameters necessary for the identification of failure sources. Second, we devise a layered Cloud architecture for the bottom-up propagation of failures to the layer, which can react to sensed SLA violation threats. Moreover, we present a communication model for the propagation of SLA violation threats to the appropriate layer of the Cloud infrastructure, which includes negotiators, brokers, and automatic service deployer. © 2010 IEEE

Accessing very high dimensional spaces in parallel

Access methods are a fundamental tool on Information Retrieval. However, most of these methods suffer the problem known as the curse of dimensionality when they are applied to objects with very high dimensionality representation spaces, such as text documents. In this paper we introduce a new parallel access method that uses several graphs as distributed index structure and a kNN search algorithm. Two parallel versions of the search method are presented, one based on master–slave scheme and the other based on a pipeline. A thorough experimental analysis on different datasets shows that our method can process efficiently large flows of queries, compete with other parallel algorithms and obtain at the same time very high quality results.This research has been supported by the CICYT project TIN2014-53495-R of the Ministerio de Economía y Competitividad

Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances

Cloud provider Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 spot instances (SIs) offer spare computational capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that the delay in acquiring spots can be incredible high. Moreover, SIs may not always be available as they can be reclaimed by EC2 at any given time, with a two-minute interruption notice. In this paper, we propose a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved EC2 pricing model instances. Our solution leverages recent findings on performance and behavior characteristics of EC2 spots. We conducted simulations by submitting real-life workflow applications, constrained by user-defined deadline and budget quality of service (QoS) parameters. The results indicate that our solution improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenarioinfo:eu-repo/semantics/publishedVersio

Foundations of efficient virtual appliance based service deployments

The use of virtual appliances could provide a flexible solution to services deployment. However, these solutions suffer from several disadvantages: (i) the slow deployment time of services in virtual machines, and (ii) virtual appliances crafted by developers tend to be inefficient for deployment purposes. Researchers target problem (i) by advancing virtualization technologies or by introducing virtual appliance caches on the virtual machine monitor hosts. Others aim at problem (ii) by providing solutions for virtual appliance construction, however these solutions require deep knowledge about the service dependencies and its deployment process. This dissertation aids problem (i) with a virtual appliance distribution technique that first identifies appliance parts and their internal dependencies. Then based on service demand it efficiently distributes the identified parts to virtual appliance repositories. Problem (ii) is targeted with the Automated Virtual appliance creation Service (AVS) that can extract and publish an already deployed service by the developer. This recently acquired virtual appliance is optimized for service deployment time with the proposed virtual appliance optimization facility that utilizes active fault injection to remove the non-functional parts of the appliance. Finally, the investigation of appliance distribution and optimization techniques resulted the definition of the minimal manageable virtual appliance that is capable of updating and configuring its executor virtual machine. The deployment time reduction capabilities of the proposed techniques were measured with several services provided in virtual appliances on three cloud infrastructures. The appliance creation capabilities of the AVS are compared to the already available virtual appliances offered by the various online appliance repositories. The results reveal that the introduced techniques significantly decrease the deployment time of virtual appliance based deployment systems. As a result these techniques alleviated one of the major obstacles before virtual appliance based deployment systems

Workflow models for heterogeneous distributed systems

The role of data in modern scientific workflows becomes more and more crucial. The unprecedented amount of data available in the digital era, combined with the recent advancements in Machine Learning and High-Performance Computing (HPC), let computers surpass human performances in a wide range of fields, such as Computer Vision, Natural Language Processing and Bioinformatics. However, a solid data management strategy becomes crucial for key aspects like performance optimisation, privacy preservation and security. Most modern programming paradigms for Big Data analysis adhere to the principle of data locality: moving computation closer to the data to remove transfer-related overheads and risks. Still, there are scenarios in which it is worth, or even unavoidable, to transfer data between different steps of a complex workflow. The contribution of this dissertation is twofold. First, it defines a novel methodology for distributed modular applications, allowing topology-aware scheduling and data management while separating business logic, data dependencies, parallel patterns and execution environments. In addition, it introduces computational notebooks as a high-level and user-friendly interface to this new kind of workflow, aiming to flatten the learning curve and improve the adoption of such methodology. Each of these contributions is accompanied by a full-fledged, Open Source implementation, which has been used for evaluation purposes and allows the interested reader to experience the related methodology first-hand. The validity of the proposed approaches has been demonstrated on a total of five real scientific applications in the domains of Deep Learning, Bioinformatics and Molecular Dynamics Simulation, executing them on large-scale mixed cloud-High-Performance Computing (HPC) infrastructures

Hierarchical hybrid sparse linear solver for multicore platforms

The solution of large sparse linear systems is a critical operationfor many numerical simulations. To cope with the hierarchical designof modern supercomputers, hybrid solvers based on Domain DecompositionMethods (DDM) have been been proposed. Among them, approachesconsisting of solving the problem on the interior of the domains witha sparse direct method and the problem on their interface with apreconditioned iterative method applied to the related SchurComplement have shown an attractive potential as they can combine therobustness of direct methods and the low memory footprint of iterativemethods. In this report, we consider an additive Schwarz preconditionerfor the Schur Complement, which represents a scalable candidate butwhose numerical robustness may decrease when the number of domainsbecomes too large. We thus propose a two-level MPI/thread parallelapproach to control the number of domains and hence the numericalbehaviour. We illustrate our discussion with large-scale matricesarising from real-life applications and processed on both a moderncluster and a supercomputer. We show that the resulting method canprocess matrices such as tdr455k for which we previously either ranout of memory on few nodes or failed to converge on a larger number ofnodes. Matrices such as Nachos_4M that could not be correctly processedin the past can now be efficiently processed up to a very large numberof CPU cores (24,576 cores). The corresponding code has beenincorporated into the MaPHyS package

Un environnement pour le calcul intensif pair à pair

Le concept de pair à pair (P2P) a connu récemment de grands développements dans les domaines du partage de fichiers, du streaming vidéo et des bases de données distribuées. Le développement du concept de parallélisme dans les architectures de microprocesseurs et les avancées en matière de réseaux à haut débit permettent d'envisager de nouvelles applications telles que le calcul intensif distribué. Cependant, la mise en oeuvre de ce nouveau type d'application sur des réseaux P2P pose de nombreux défis comme l'hétérogénéité des machines, le passage à l'échelle et la robustesse. Par ailleurs, les protocoles de transport existants comme TCP et UDP ne sont pas bien adaptés à ce nouveau type d'application. Ce mémoire de thèse a pour objectif de présenter un environnement décentralisé pour la mise en oeuvre de calculs intensifs sur des réseaux pair à pair. Nous nous intéressons à des applications dans les domaines de la simulation numérique et de l'optimisation qui font appel à des modèles de type parallélisme de tâches et qui sont résolues au moyen d'algorithmes itératifs distribués or parallèles. Contrairement aux solutions existantes, notre environnement permet des communications directes et fréquentes entre les pairs. L'environnement est conçu à partir d'un protocole de communication auto-adaptatif qui peut se reconfigurer en adoptant le mode de communication le plus approprié entre les pairs en fonction de choix algorithmiques relevant de la couche application ou d'éléments de contexte comme la topologie au niveau de la couche réseau. Nous présentons et analysons des résultats expérimentaux obtenus sur diverses plateformes comme GRID'5000 et PlanetLab pour le problème de l'obstacle et des problèmes non linéaires de flots dans les réseaux. ABSTRACT : The concept of peer-to-peer (P2P) has known great developments these years in the domains of file sharing, video streaming or distributed databases. Recent advances in microprocessors architecture and networks permit one to consider new applications like distributed high performance computing. However, the implementation of this new type of application on P2P networks gives raise to numerous challenges like heterogeneity, scalability and robustness. In addition, existing transport protocols like TCP and UDP are not well suited to this new type of application. This thesis aims at designing a decentralized and robust environment for the implementation of high performance computing applications on peer-to-peer networks. We are interested in applications in the domains of numerical simulation and optimization that rely on tasks parallel models and that are solved via parallel or distributed iterative algorithms. Unlike existing solutions, our environment allows frequent direct communications between peers. The environment is based on a self adaptive communication protocol that can reconfigure itself dynamically by choosing the most appropriate communication mode between any peers according to decisions concerning algorithmic choice made at the application level or elements of context at transport level, like topology. We present and analyze computational results obtained on several testeds like GRID’5000 and PlanetLab for the obstacle problem and nonlinear network flow problems