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).\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

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

Scheduling algorithms for peer-to-peer collaborative file distribution

Peer-to-Peer file sharing applications on the Internet, such as BitTorrent, Gnutella, etc., have been immensely popular. Prior research mainly focuses on peer and content discovery, overlay topology formation, fairness and incentive issues, etc, but seldom investigates the data distribution problem which is also a core component of any file sharing application. In this paper, we present the first effort in addressing this collaborative file distribution problem and formally define the scheduling problem in a simplified context. We suggest several types of algorithms, including a novel Bipartite Matching algorithm, for solving the problem. Simulation results show that our weighted bipartite algorithm finds an optimal solution for all cases tested. Therefore, we believe our algorithm is a promising solution to be employed as the core scheduling module in P2P file sharing applications, shortening the total download time experienced by users. © 2005 IEEE.published_or_final_versio

Revisiting Matrix Product on Master-Worker Platforms

This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous master-worker platforms. While matrix-product is well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: - Centralized data. We assume that all matrix files originate from, and must be returned to, the master. - Heterogeneous star-shaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. - Limited memory. Because we investigate the parallelization of large problems, we cannot assume that full matrix panels can be stored in the worker memories and re-used for subsequent updates (as in ScaLAPACK). We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on various platforms at Ecole Normale Superieure de Lyon and the University of Tennessee. However, we point out that in this first version of the report, experiments are limited to homogeneous platforms

Implementation of MD5 Framework for Privacy-Preserving Support for Mobile Healthcare

The improvement of science and technology has made life so easy and fast that smartphones and other touch-screen minicomputers have become the most trusted personal storage and communication devices for individuals. Comparable to the rich enhancement in wireless body sensor networks, it is valuable to the development of medical treatment to be exceptionally adaptable and become very flexible by means of smartphones through 2G and 3G system bearers. This has made treatment simple even to the common individual in the general public with less payable cash. In this paper, we introduce privacy-preserving support for mobile healthcare using message digest where we have used an MD5 algorithm instead of AES, which can certainly achieve an efficient way and minimizes the memory consumed and the large amount of PHI data of the medical user (patient) is reduced to a fixed amount of size compared to AES which in parallel increases the speed of the data to be sent to TA without any delay which in-turn. This study implements a secure and privacy-preserving opportunistic computing framework (SPOC) for mobile-health care emergency. Utilizing smartphones and SPOC, assets like computing power and energy can be gathered to reliably to take care of intensive personal health information (PHI) of the medicinal client when he/she is in critical situation with minimal privacy disclosure. With these, the healthcare authorities can treat the patients (restorative clients) remotely, where the patients live at home or at different spots they run. This sort of a treatment can be done under mHealth (Mobile-Healthcare). In malice of the fact that in them-medicinal services administration, there are numerous security and information protection issues to be succeed. The main aim of this paper is to bring medical health to patients in remote locations by providing the basic triage of an emergency to increase the patient’s body acceptance until they can reach a proper medical facility, in addition to providing emergency care in minimal payable cash

A Framework for Adaptive Collective Communications on Heterogeneous Hierarchical Networks

Extended version of the IPDPS 2006 paperToday, due to the wide variety of existing parallel systems consisting on collections of heterogeneous machines, it is very difficult for a user to solve a target problem by using a single algorithm or to write portable programs that perform well on multiple computational supports. The inherent heterogeneity and the diversity of networks of such environments represent a great challenge to model the communications for high performance computing applications. Our objective within this work is to propose a generic framework based on communication models and adaptive techniques for dealing with prediction of communication performances on cluster-based hierarchical platforms. Toward this goal, we introduce the concept of polyalgorithmic model of communications, which correspond to selection of the most adapted communication algorithms and scheduling strategies, giving the characteristics of the hardware resources of the target parallel system. We apply this methodology on collective communication operations and show that the framework provides significant performances while determining the best algorithm depending on the problem and architecture parameters

Efficient collective communication on heterogeneous networks of workstations

banikaze,moorthy,panda

Efficient Collective Communication on Heterogeneous Networks of Workstations

Networks of Workstations (NOW) have become an attractive alternative platform for high performance computing. Due to the commodity nature of workstations and interconnects and due to the multiplicity of vendors and platforms, the NOW environments are being gradually redefined as Heterogeneous Networks of Workstations (HNOW) environments. This paper presents a new framework for implementing collective communication operations (as defined by the Message Passing Interface (MPI) standard) efficiently for the emerging HNOW environments. We first classify different types of heterogeneity in HNOW and then focus on one important characteristic: communication capabilities of workstations. Taking this characteristic into account, we propose two new approaches (Speed-Partitioned Ordered Chain (SPOC) and Fastest-Node First (FNF)) to implement collective communication operations with reduced latency. We also investigate methods for deriving optimal trees for broadcast and multicast operations. H..

Efficient Collective Communication on Heterogeneous Networks of Workstations

: Networks of Workstations (NOW) have become an attractive alternative platform for high performance computing. Due to the commodity nature of workstations and interconnects and the multiplicity of vendors and platforms for NOW systems, the NOW environments are being gradually redefined as Heterogeneous Networks of Workstations (HNOW) environments. This paper presents a new framework for implementing collective communication operations (as defined by the Message Passing Interface (MPI) standard) efficiently for the emerging HNOW environments. We first classify different types of heterogeneity in HNOW and then focus on one important characteristic: communication capabilities of workstations. Taking this characteristic into account, we show that the algorithms such as the Binomial-tree based algorithms which are currently used for implementing collective operations are not efficient. We propose two new approaches (Speed-Partitioned Ordered Chain (SPOC) and Fastest-Node First (FNF)) to..