Improving Scalability and Usability of Parallel Runtime Environments for High Availability and High Performance Systems

The number of processors embedded in high performance computing platforms is growing daily to solve larger and more complex problems. Hence, parallel runtime environments have to support and adapt to the underlying platforms that require scalability and fault management in more and more dynamic environments. This dissertation aims to analyze, understand and improve the state of the art mechanisms for managing highly dynamic, large scale applications. This dissertation demonstrates that the use of new scalable and fault-tolerant topologies, combined with rerouting techniques, builds parallel runtime environments, which are able to efficiently and reliably deliver sets of information to a large number of processes. Several important graph properties are provided to illustrate the theoretical capability of these topologies in terms of both scalability and fault-tolerance, such as reasonable degree, regular graph, low diameter, symmetric graph, low cost factor, low message traffic density, optimal connectivity, low fault-diameter and strongly resilient. The dissertation builds a communication framework based on these topologies to support parallel runtime environments. Such a framework can handle multiple types of messages, e.g., unicast, multicast, broadcast and all-gather. Additionally, the communication framework has been formally verified to work in both normal and failure circumstances without creating any of the common problems such as broadcast storm, deadlock and non-progress cycle

A distributed analysis and monitoring framework for the compact Muon solenoid experiment and a pedestrian simulation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The design of a parallel and distributed computing system is a very complicated task. It requires a detailed understanding of the design issues and of the theoretical and practical aspects of their solutions. Firstly, this thesis discusses in detail the major concepts and components required to make parallel and distributed computing a reality. A multithreaded and distributed framework capable of analysing the simulation data produced by a pedestrian simulation software was developed. Secondly, this thesis discusses the origins and fundamentals of Grid computing and the motivations for its use in High Energy Physics. Access to the data produced by the Large Hadron Collider (LHC) has to be provided for more than five thousand scientists all over the world. Users who run analysis jobs on the Grid do not necessarily have expertise in Grid computing. Simple, userfriendly and reliable monitoring of the analysis jobs is one of the key components of the operations of the distributed analysis; reliable monitoring is one of the crucial components of the Worldwide LHC Computing Grid for providing the functionality and performance that is required by the LHC experiments. The CMS Dashboard Task Monitoring and the CMS Dashboard Job Summary monitoring applications were developed to serve the needs of the CMS community

Economic-based Distributed Resource Management and Scheduling for Grid Computing

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications

GRID superscalar: a programming model for the Grid

Durant els darrers anys el Grid ha sorgit com una nova plataforma per la computació distribuïda. La tecnologia Gris permet unir diferents recursos de diferents dominis administratius i formar un superordinador virtual amb tots ells. Molts grups de recerca han dedicat els seus esforços a desenvolupar un conjunt de serveis bàsics per oferir un middleware de Grid: una capa que permet l'ús del Grid. De tota manera, utilitzar aquests serveis no és una tasca fácil per molts usuaris finals, cosa que empitjora si l'expertesa d'aquests usuaris no està relacionada amb la informàtica.Això té una influència negativa a l'hora de que la comunitat científica adopti la tecnologia Grid. Es veu com una tecnologia potent però molt difícil de fer servir. Per facilitar l'ús del Grid és necessària una capa extra que amagui la complexitat d'aquest i permeti als usuaris programar o portar les seves aplicacions de manera senzilla.Existeixen moltes propostes d'eines de programació pel Grid. En aquesta tesi fem un resum d'algunes d'elles, i podem veure que existeixen eines conscients i no-conscients del Grid (es programen especificant o no els detalls del Grid, respectivament). A més, molt poques d'aquestes eines poden explotar el paral·lelisme implícit de l'aplicació, i en la majoria d'elles, l'usuari ha de definir aquest paral·lelisme de manera explícita. Una altra característica que considerem important és si es basen en llenguatges de programació molt populars (com C++ o Java), cosa que facilita l'adopció per part dels usuaris finals.En aquesta tesi, el nostre objectiu principal ha estat crear un model de programació pel Grid basat en la programació seqüencial i els llenguatges més coneguts de la programació imperativa, capaç d'explotar el paral·lelisme implícit de les aplicacions i d'accelerar-les fent servir els recursos del Grid de manera concurrent. A més, com el Grid és de naturalesa distribuïda, heterogènia i dinàmica i degut també a que el nombre de recursos que pot formar un Grid pot ser molt gran, la probabilitat de que es produeixi una errada durant l'execució d'una aplicació és elevada. Per tant, un altre dels nostres objectius ha estat tractar qualsevol tipus d'error que pugui sorgir durant l'execució d'una aplicació de manera automàtica (ja siguin errors relacionats amb l'aplicació o amb el Grid). GRID superscalar (GRIDSs), la principal contribució d'aquesta tesi, és un model de programació que assoleix elsobjectius mencionats proporcionant una interfície molt petita i simple i un entorn d'execució que és capaç d'executar en paral·lel el codi proporcionat fent servir el Grid. La nostra interfície de programació permet a un usuari programar una aplicació no-conscient del Grid, amb llenguatges imperatius coneguts i populars (com C/C++, Java, Perl o Shell script) i de manera seqüencial, per tant dóna un pas important per ajudar als usuaris a adoptar la tecnologia Grid.Hem aplicat el nostre coneixement de l'arquitectura de computadors i el disseny de microprocessadors a l'entorn d'execució de GRIDSs. Tal com es fa a un processador superescalar, l'entorn d'execució de GRIDSs és capaç de realitzar un anàlisi de dependències entre les tasques que formen l'aplicació, i d'aplicar tècniques de renombrament per incrementar el seu paral·lelisme. GRIDSs genera automàticament a partir del codi principal de l'usuari un graf que descriu les dependències de dades en l'aplicació. També presentem casos d'ús reals del model de programació en els camps de la química computacional i la bioinformàtica, que demostren que els nostres objectius han estat assolits.Finalment, hem estudiat l'aplicació de diferents tècniques per detectar i tractar fallades: checkpoint, reintent i replicació de tasques. La nostra proposta és proporcionar un entorn capaç de tractar qualsevol tipus d'errors, de manera transparent a l'usuari sempre que sigui possible. El principal avantatge d'implementar aquests mecanismos al nivell del model de programació és que el coneixement a nivell de l'aplicació pot ser explotat per crear dinàmicament una estratègia de tolerància a fallades per cada aplicació, i evitar introduir sobrecàrrega en entorns lliures d'errors.During last years, the Grid has emerged as a new platform for distributed computing. The Grid technology allows joining different resources from different administrative domains and forming a virtual supercomputer with all of them.Many research groups have dedicated their efforts to develop a set of basic services to offer a Grid middleware: a layer that enables the use of the Grid. Anyway, using these services is not an easy task for many end users, even more if their expertise is not related to computer science. This has a negative influence in the adoption of the Grid technology by the scientific community. They see it as a powerful technology but very difficult to exploit. In order to ease the way the Grid must be used, there is a need for an extra layer which hides all the complexity of the Grid, and allows users to program or port their applications in an easy way.There has been many proposals of programming tools for the Grid. In this thesis we give an overview on some of them, and we can see that there exist both Grid-aware and Grid-unaware environments (programmed with or without specifying details of the Grid respectively). Besides, very few existing tools can exploit the implicit parallelism of the application and in the majority of them, the user must define the parallelism explicitly. Another important feature we consider is if they are based in widely used programming languages (as C++ or Java), so the adoption is easier for end users.In this thesis, our main objective has been to create a programming model for the Grid based on sequential programming and well-known imperative programming languages, able to exploit the implicit parallelism of applications and to speed them up by using the Grid resources concurrently. Moreover, because the Grid has a distributed, heterogeneous and dynamic nature and also because the number of resources that form a Grid can be very big, the probability that an error arises during an application's execution is big. Thus, another of our objectives has been to automatically deal with any type of errors which may arise during the execution of the application (application related or Grid related).GRID superscalar (GRIDSs), the main contribution of this thesis, is a programming model that achieves these mentioned objectives by providing a very small and simple interface and a runtime that is able to execute in parallel the code provided using the Grid. Our programming interface allows a user to program a Grid-unaware application with already known and popular imperative languages (such as C/C++, Java, Perl or Shell script) and in a sequential fashion, therefore giving an important step to assist end users in the adoption of the Grid technology.We have applied our knowledge from computer architecture and microprocessor design to the GRIDSs runtime. As it is done in a superscalar processor, the GRIDSs runtime system is able to perform a data dependence analysis between the tasks that form an application, and to apply renaming techniques in order to increase its parallelism. GRIDSs generates automatically from user's main code a graph describing the data dependencies in the application.We present real use cases of the programming model in the fields of computational chemistry and bioinformatics, which demonstrate that our objectives have been achieved.Finally, we have studied the application of several fault detection and treatment techniques: checkpointing, task retry and task replication. Our proposal is to provide an environment able to deal with all types of failures, transparently for the user whenever possible. The main advantage in implementing these mechanisms at the programming model level is that application-level knowledge can be exploited in order to dynamically create a fault tolerance strategy for each application, and avoiding to introduce overhead in error-free environments

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

A self-mobile skeleton in the presence of external loads

Multicore clusters provide cost-effective platforms for running CPU-intensive and data-intensive parallel applications. To effectively utilise these platforms, sharing their resources is needed amongst the applications rather than dedicated environments. When such computational platforms are shared, user applications must compete at runtime for the same resource so the demand is irregular and hence the load is changeable and unpredictable. This thesis explores a mechanism to exploit shared multicore clusters taking into account the external load. This mechanism seeks to reduce runtime by finding the best computing locations to serve the running computations. We propose a generic algorithmic data-parallel skeleton which is aware of its computations and the load state of the computing environment. This skeleton is structured using the Master/Worker pattern where the master and workers are distributed on the nodes of the cluster. This skeleton divides the problem into computations where all these computations are initiated by the master and coordinated by the distributed workers. Moreover, the skeleton has built-in mobility to implicitly move the parallel computations between two workers. This mobility is data mobility controlled by the application, the skeleton. This skeleton is not problem-specific and therefore it is able to execute different kinds of problems. Our experiments suggest that this skeleton is able to efficiently compensate for unpredictable load variations. We also propose a performance cost model that estimates the continuation time of the running computations locally and remotely. This model also takes the network delay, data size and the load state as inputs to estimate the transfer time of the potential movement. Our experiments demonstrate that this model takes accurate decisions based on estimates in different load patterns to reduce the total execution time. This model is problem-independent because it considers the progress of all current computations. Moreover, this model is based on measurements so it is not dependent on the programming language. Furthermore, this model takes into account the load state of the nodes on which the computation run. This state includes the characteristics of the nodes and hence this model is architecture-independent. Because the scheduling has direct impact on system performance, we support the skeleton with a cost-informed scheduler that uses a hybrid scheduling policy to improve the dynamicity and adaptivity of the skeleton. This scheduler has agents distributed over the participating workers to keep the load information up to date, trigger the estimations, and facilitate the mobility operations. On runtime, the skeleton co-schedules its computations over computational resources without interfering with the native operating system scheduler. We demonstrate that using a hybrid approach the system makes mobility decisions which lead to improved performance and scalability over large number of computational resources. Our experiments suggest that the adaptivity of our skeleton in shared environment improves the performance and reduces resource contention on nodes that are heavily loaded. Therefore, this adaptivity allows other applications to acquire more resources. Finally, our experiments show that the load scheduler has a low incurred overhead, not exceeding 0.6%, compared to the total execution time

PVM emulation in the Harness metacomputing framework -- design and performance evaluation

This paper describes the Harness-PVM emulation module that makes it possible to run legacy PVM applications within the Harness metacomputing system environment. We show benchmark results suggesting that component- and Java-based frameworks may provide substantial benefits without significantly compromising performance. Augmenting or implementing grids using such component based systems can thus be advantageous in a number of situations