Coarse-grain Load Distribution in Heterogeneous Computing

HPC heterogeneous clusters are composed by different type of machines (various types of component manufacturers, varying computational capacities), and different hardware accelerators. TThe most common type of data distributions is the equal division of the data across all the nodes. A more sophisticated policy of data distribution is needed to explode the computational capacity of the entire system

Explotando jerarquías de memoria distribuida/compartida con Hitmap

Actualmente los clústers de computadoras que se utilizan para computación de alto rendimiento se construyen interconectando máquinas de memoria compartida. Como modelo de programación común para este tipo de clústers se puede usar el paradigma del paso de mensajes, lanzando tantos procesos como núcleos disponibles tengamos entre todas las máquinas del clúster. Sin embargo, esta forma de programación no es eficiente. Para conseguir explotar eficientemente estos sistemas jerárquicos es necesario una combinación de diferentes modelos de programación y herramientas, adecuada cada una de ellas para los diferentes niveles de la plataforma de ejecución. Este trabajo presenta un método que facilita la programación para entornos que combinan memoria distribuida y compartida. La coordinación en el nivel de memoria distribuida se facilita usando la biblioteca Hitmap. Mostraremos como integrar Hitmap con modelos de programación para memoria compartida y con herramientas automáticas que paralelizan y optimizan código secuencial. Esta nueva combinación permitirá explotar las técnicas más apropiadas para cada nivel del sistema además de facilitar la generación de programas paralelos multinivel que adaptan automáticamente su estructura de comunicaciones y sincronización a la máquina donde se ejecuta. Los resultados experimentales muestran como la propuesta del trabajo mejora los mejores resultados obtenidos con programas de referencia optimizados manualmente usando MPI u OpenMP.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Máster en Investigación en Tecnologías de la Información y las Comunicacione

WG1N5315 - Response to Call for AIC evaluation methodologies and compression technologies for medical images: LAR Codec

This document presents the LAR image codec as a response to Call for AIC evaluation methodologies and compression technologies for medical images.This document describes the IETR response to the specific call for contributions of medical imaging technologies to be considered for AIC. The philosophy behind our coder is not to outperform JPEG2000 in compression; our goal is to propose an open source, royalty free, alternative image coder with integrated services. While keeping the compression performances in the same range as JPEG2000 but with lower complexity, our coder also provides services such as scalability, cryptography, data hiding, lossy to lossless compression, region of interest, free region representation and coding

FEMPAR: an object-oriented parallel finite element framework

FEMPAR is an open source object oriented Fortran200X scientific software library for the high-performance scalable simulation of complex multiphysics problems governed by partial differential equations at large scales, by exploiting state-of-the-art supercomputing resources. It is a highly modularized, flexible, and extensible library, that provides a set of modules that can be combined to carry out the different steps of the simulation pipeline. FEMPAR includes a rich set of algorithms for the discretization step, namely (arbitrary-order) grad, div, and curl-conforming finite element methods, discontinuous Galerkin methods, B-splines, and unfitted finite element techniques on cut cells, combined with h-adaptivity. The linear solver module relies on state-of-the-art bulk-asynchronous implementations of multilevel domain decomposition solvers for the different discretization alternatives and block-preconditioning techniques for multiphysics problems. FEMPAR is a framework that provides users with out-of-the-box state-of-the-art discretization techniques and highly scalable solvers for the simulation of complex applications, hiding the dramatic complexity of the underlying algorithms. But it is also a framework for researchers that want to experience with new algorithms and solvers, by providing a highly extensible framework. In this work, the first one in a series of articles about FEMPAR, we provide a detailed introduction to the software abstractions used in the discretization module and the related geometrical module. We also provide some ingredients about the assembly of linear systems arising from finite element discretizations, but the software design of complex scalable multilevel solvers is postponed to a subsequent work.Peer ReviewedPostprint (published version

Efficient I/O for Computational Grid Applications

High-performance computing increasingly occurs on computational grids composed of heterogeneous and geographically distributed systems of computers, networks, and storage devices that collectively act as a single virtual computer. A key challenge in this environment is to provide efficient access to data distributed across remote data servers. This dissertation explores some of the issues associated with I/O for wide-area distributed computing and describes an I/O system, called Armada, with the following features: a framework to allow application and dataset providers to flexibly compose graphs of processing modules that describe the distribution, application interfaces, and processing required of the dataset before or after computation; an algorithm to restructure application graphs to increase parallelism and to improve network performance in a wide-area network; and a hierarchical graph-partitioning scheme that deploys components of the application graph in a way that is both beneficial to the application and sensitive to the administrative policies of the different administrative domains. Experiments show that applications using Armada perform well in both low- and high-bandwidth environments, and that our approach does an exceptional job of hiding the network latency inherent in grid computing