Sistemas Paralelos

El objetivo de esta línea es investigar en Sistemas Paralelos, esto es, la combinación de problemas de software asociados con la utilización de arquitecturas de procesamiento paralelo, especialmente sistemas multiprocesador distribuidos. Los temas fundamentales abarcan la especificación, transformación, optimización y verificación de algoritmos ejecutables en sistemas paralelos/distribuidos, la optimización de clases de soluciones en función de modelos de arquitectura multiprocesador, las métricas de complejidad y eficiencia relacionadas con el procesamiento paralelo, la influencia del balance de carga y la asignación de tareas a procesadores, la escalabilidad de los sistemas paralelos, así como simulación y diseño de arquitecturas VLSI orientadas a multiprocesamiento. Asimismo se ha iniciado el estudio de los modelos de predicción de performance en sistemas paralelos. Interesa la aplicación de las investigaciones en áreas como el procesamiento de datos numéricos en cómputo científico, el procesamiento de imágenes digitales y las bases de datos distribuidas. Para esto, se trabaja experimentalmente con distintos modelos de arquitectura disponibles o accesibles desde el III-LIDI.Eje: Sistemas distribuidos y tiempo realRed de Universidades con Carreras en Informática (RedUNCI

Investigación en sistemas paralelos

El objetivo de esta línea es investigar en Sistemas Paralelos, esto es, la combinación de problemas de software asociados con la utilización de arquitecturas de procesamiento paralelo, especialmente sistemas multiprocesador distribuidos (como clusters y multiclusters). Los temas fundamentales abarcan la especificación, transformación, optimización y verificación de algoritmos ejecutables en sistemas paralelos/distribuidos, la optimización de clases de soluciones en función de modelos de arquitectura multiprocesador, las métricas de complejidad y eficiencia relacionadas con el procesamiento paralelo, la influencia del balance de carga y la asignación de tareas a procesadores, la escalabilidad de los sistemas paralelos, los modelos de predicción de performance en sistemas paralelos, así como aspectos de simulación y diseño de arquitecturas VLSI orientadas a multiprocesamiento. Interesa la aplicación de las investigaciones en áreas con procesamiento masivo de datos tales como cómputo científico, procesamiento de imágenes digitales, bases de datos distribuidas, reconocimiento de patrones en secuencias y algoritmos no numéricos complejos. Para esto, se trabaja experimentalmente con distintos modelos de arquitectura disponibles o accesibles desde el III-LIDI y arquitecturas disponibles en distintas Universidades del país y el exterior con las cuales se tienen convenios de cooperación. El proyecto está financiado por la Universidad Nacional de La Plata, la Comisión de Investigaciones Científicas de la Provincia de Buenos Aires y la Agencia Nacional de Promoción Científica y Técnica.Eje: OtrosRed de Universidades con Carreras en Informática (RedUNCI

Investigación en sistemas paralelos

El objetivo de esta línea es investigar en Sistemas Paralelos, esto es, la combinación de problemas de software asociados con la utilización de arquitecturas de procesamiento paralelo, especialmente sistemas multiprocesador distribuidos (como clusters y multiclusters).\nLos temas fundamentales abarcan la especificación, transformación, optimización y verificación de algoritmos ejecutables en sistemas paralelos/distribuidos, la optimización de clases de soluciones en función de modelos de arquitectura multiprocesador, las métricas de complejidad y eficiencia relacionadas con el procesamiento paralelo, la influencia del balance de carga y la asignación de tareas a procesadores, la escalabilidad de los sistemas paralelos, los modelos de predicción de performance en sistemas paralelos, así como aspectos de simulación y diseño de arquitecturas VLSI orientadas a multiprocesamiento.\nInteresa la aplicación de las investigaciones en áreas con procesamiento masivo de datos tales como cómputo científico, procesamiento de imágenes digitales, bases de datos distribuidas, reconocimiento de patrones en secuencias y algoritmos no numéricos complejos. Para esto, se trabaja experimentalmente con distintos modelos de arquitectura disponibles o accesibles desde el III-LIDI y arquitecturas disponibles en distintas Universidades del país y el exterior con las cuales se tienen convenios de cooperación.\nEl proyecto está financiado por la Universidad Nacional de La Plata, la Comisión de Investigaciones Científicas de la Provincia de Buenos Aires y la Agencia Nacional de Promoción Científica y Técnica.Eje: Otro

Open source software GitHub ecosystem: a SEM approach

Open source software (OSS) is a collaborative effort. Getting affordable high-quality software with less probability of errors or fails is not far away. Thousands of open-source projects (termed repos) are alternatives to proprietary software development. More than two-thirds of companies are contributing to open source. Open source technologies like OpenStack, Docker and KVM are being used to build the next generation of digital infrastructure. An iconic example of OSS is 'GitHub' - a successful social site. GitHub is a hosting platform that host repositories (repos) based on the Git version control system. GitHub is a knowledge-based workspace. It has several features that facilitate user communication and work integration. Through this thesis I employ data extracted from GitHub, and seek to better understand the OSS ecosystem, and to what extent each of its deployed elements affects the successful development of the OSS ecosystem. In addition, I investigate a repo's growth over different time periods to test the changing behavior of the repo. From our observations developers do not follow one development methodology when developing, and growing their project, and such developers tend to cherry-pick from differing available software methodologies. GitHub API remains the main OSS location engaged to extract the metadata for this thesis's research. This extraction process is time-consuming - due to restrictive access limitations (even with authentication). I apply Structure Equation Modelling (termed SEM) to investigate the relative path relationships between the GitHub- deployed OSS elements, and I determine the path strength contributions of each element to determine the OSS repo's activity level. SEM is a multivariate statistical analysis technique used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis. It is used to analyze the structural relationship between measured variables and/or latent constructs. This thesis bridges the research gap around longitude OSS studies. It engages large sample-size OSS repo metadata sets, data-quality control, and multiple programming language comparisons. Querying GitHub is not direct (nor simple) yet querying for all valid repos remains important - as sometimes illegal, or unrepresentative outlier repos (which may even be quite popular) do arise, and these then need to be removed from each initial OSS's language-specific metadata set. Eight top GitHub programming languages, (selected as the most forked repos) are separately engaged in this thesis's research. This thesis observes these eight metadata sets of GitHub repos. Over time, it measures the different repo contributions of the deployed elements of each metadata set. The number of stars-provided to the repo delivers a weaker contribution to its software development processes. Sometimes forks work against the repo's progress by generating very minor negative total effects into its commit (activity) level, and by sometimes diluting the focus of the repo's software development strategies. Here, a fork may generate new ideas, create a new repo, and then draw some original repo developers off into this new software development direction, thus retarding the original repo's commit (activity) level progression. Multiple intermittent and minor version releases exert lesser GitHub JavaScript repo commit (or activity) changes because they often involve only slight OSS improvements, and because they only require minimal commit/commits contributions. More commit(s) also bring more changes to documentation, and again the GitHub OSS repo's commit (activity) level rises. There are both direct and indirect drivers of the repo's OSS activity. Pulls and commits are the strongest drivers. This suggests creating higher levels of pull requests is likely a preferred prime target consideration for the repo creator's core team of developers. This study offers a big data direction for future work. It allows for the deployment of more sophisticated statistical comparison techniques. It offers further indications around the internal and broad relationships that likely exist between GitHub's OSS big data. Its data extraction ideas suggest a link through to business/consumer consumption, and possibly how these may be connected using improved repo search algorithms that release individual business value components

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research

Runtime support for load balancing of parallel adaptive and irregular applications

Applications critical to today\u27s engineering research often must make use of the increased memory and processing power of a parallel machine. While advances in architecture design are leading to more and more powerful parallel systems, the software tools needed to realize their full potential are in a much less advanced state. In particular, efficient, robust, and high-performance runtime support software is critical in the area of dynamic load balancing. While the load balancing of loosely synchronous codes, such as field solvers, has been studied extensively for the past 15 years, there exists a class of problems, known as asynchronous and highly adaptive , for which the dynamic load balancing problem remains open. as we discuss, characteristics of this class of problems render compile-time or static analysis of little benefit, and complicate the dynamic load balancing task immensely.;We make two contributions to this area of research. The first is the design and development of a runtime software toolkit, known as the Parallel Runtime Environment for Multi-computer Applications, or PREMA, which provides interprocessor communication, a global namespace, a framework for the implementation of customized scheduling policies, and several such policies which are prevalent in the load balancing literature. The PREMA system is designed to support coarse-grained domain decompositions with the goals of portability, flexibility, and maintainability in mind, so that developers will quickly feel comfortable incorporating it into existing codes and developing new codes which make use of its functionality. We demonstrate that the programming model and implementation are efficient and lead to the development of robust and high-performance applications.;Our second contribution is in the area of performance modeling. In order to make the most effective use of the PREMA runtime software, certain parameters governing its execution must be set off-line. Optimal values for these parameters may be determined through repeated executions of the target application; however, this is not always possible, particularly in large-scale environments and long-running applications. We present an analytic model that allows the user to quickly and inexpensively predict application performance and fine-tune applications built on the PREMA platform

Rate of Change Load Balancing in Distributed and Parallel Systems

Dynamic Load Balancing is an important system function destined to distribute workload among available processors to improve throughput and/or execution times of parallel computer programs either uniform or non-uniform (jobs whose workload varies at run-time in unpredictable ways). Non-uniform computation and communication requirements may bog down a parallel computer if no efficient load distribution is effected. A novel distributed algorithm for load balancing is proposed and is based on local Rate of Change observations rather than on global absolute load numbers. It is a totally distributed algorithm and requires no centralized trigger and/or decision makers. The strategy is discussed and analysed by means of experimental simulation. 1. Introduction We consider the problem of resource management in a multiprocessor system whose operating system supports time sharing among a multiplicity of parallel and/or strictly sequential jobs. We focus on load balancing as an efficient strateg..