Semantic-Based Storage QoS Management Methodology -- Case Study for Distributed Environments

The distributed computing environments, e.g. clouds, often deal with huge amounts of data, which constantly increase. The global growth of data is caused by ubiquitous personal devices, enterprise and scientific applications, etc. As the size of data grows new challenges are emerging in the context of storage management. Modern data and storage resource management systems need to face wide range of problems -- minimizing energy consumption (green data centers), optimizing resource usage, throughput and capacity, data availability, security and legal issues, scalability. In addition users or their applications can have QoS (Quality of Service) requirements concerning the storage access, which further complicates the management. To cope with this problem a common mass storage system model taking into account the performance aspects of a storage system becomes a necessity. The model described with semantic technologies brings a semantic interoperability between the system components. In this paper we describe our approach at data management with QoS based on the developed models as a case study for distributed environments

An improved Ant Colony System for the Sequential Ordering Problem

It is not rare that the performance of one metaheuristic algorithm can be improved by incorporating ideas taken from another. In this article we present how Simulated Annealing (SA) can be used to improve the efficiency of the Ant Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering Problem (SOP). Moreover, we show how the very same ideas can be applied to improve the convergence of a dedicated local search, i.e. the SOP-3-exchange algorithm. A statistical analysis of the proposed algorithms both in terms of finding suitable parameter values and the quality of the generated solutions is presented based on a series of computational experiments conducted on SOP instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed ACS-SA and EACS-SA algorithms often generate solutions of better quality than the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with the proposed SOP-3-exchange-SA local search was able to find 10 new best solutions for the SOP instances from the SOPLIB2006 repository, thus improving the state-of-the-art results as known from the literature. Overall, the best known or improved solutions were found in 41 out of 48 cases.Comment: 30 pages, 8 tables, 11 figure

Work‐Efficient Parallel Union‐Find

The incremental graph connectivity (IGC) problem is to maintain a data structure that can quickly answer whether two given vertices in a graph are connected, while allowing more edges to be added to the graph. IGC is a fundamental problem and can be solved efficiently in the sequential setting using a solution to the classical union‐find problem. However, sequential solutions are not sufficient to handle modern‐day large, rapidly‐changing graphs where edge updates arrive at a very high rate. We present the first shared‐memory parallel data structure for union‐find (equivalently, IGC) that is both provably work‐efficient (ie, performs no more work than the best sequential counterpart) and has polylogarithmic parallel depth. We also present a simpler algorithm with slightly worse theoretical properties, but which is easier to implement and has good practical performance. Our experiments on large graph streams with various degree distributions show that it has good practical performance, capable of processing hundreds of millions of edges per second using a 20‐core machine

Optimisation massivement multi-tâche sur grappes de calcul hétérogènes – Application aux problèmes de permutation

Branch-and-Bound (B&B) is a frequently used tree-search exploratory method for the exact resolution of combinatorial optimization problems (COPs). However, in practice, only small problem instances can be solved on a sequential computer, as B&B generates often generates a huge amount of subproblems to be evaluated. In order to solve large COPs, we revisit the design and implementation of massively parallel B&B on top of large heterogeneous clusters, integrating multi-core CPUs, many-core processors and GPUs.For the efficient storage and management of subproblems an original data structure (IVM) dedicated to permutation problems is used. Because of the highly irregular and unpredictable shape of the B&B tree, dynamic load balancing between parallel exploration processes is one of the main issues addressed in this thesis. Based on a compact encoding of the search space in the form of intervals, work stealing strategies for multi-core and GPU are proposed, as well as hierarchical approaches for load balancing in distributed memory multi-CPU/multi-GPU systems. Three permutation problems, the Flowshop Scheduling Problem (FSP), the Quadratic Assignment Problem (QAP) and the n-Queens puzzle problem are used as test-cases.The resolution, in 9 hours, of a FSP instance with an estimated sequential execution time of 22 years demonstrates the scalability of the proposed algorithms on a cluster composed of 36 GPUs.L'algorithme Branch-and-Bound (B&B) est une méthode de recherche arborescente fréquemment utilisé pour la résolution exacte de problèmes d'optimisation combinatoire (POC). Néanmoins, seules des petites instances peuvent être effectivement résolues sur une machine séquentielle, le nombre de sous-problèmes à évaluer étant souvent très grand. Visant la resolution de POC de grande taille, nous réexaminons la conception et l'implémentation d'algorithmes B&B massivement parallèles sur de larges plateformes hétérogènes de calcul, intégrant des processeurs multi-coeurs, many-cores et et processeurs graphiques (GPUs). Pour une représentation compacte en mémoire des sous-problèmes une structure de données originale (IVM), dédiée aux problèmes de permutation est utilisée. En raison de la forte irrégularité de l'arbre de recherche, l'équilibrage de charge dynamique entre processus d'exploration parallèles occupe une place centrale dans cette thèse. Basés sur un encodage compact de l'espace de recherche sous forme d'intervalles, des stratégies de vol de tâches sont proposées pour processeurs multi-core et GPU, ainsi une approche hiérarchique pour l'équilibrage de charge dans les systèmes multi-GPU et multi-CPU à mémoire distribuée. Trois problèmes d'optimisation définis sur l'ensemble des permutations, le problème d'ordonnancement Flow-Shop (FSP), d'affectation quadratique (QAP) et le problème des n-dames sont utilisés comme cas d'étude. La resolution en 9 heures d'une instance du FSP dont le temps de résolution séquentiel est estimé à 22 ans demontre la capacité de passage à l'échelle des algorithmes proposés sur une grappe de calcul composé de 36 GPUs

Leveraging performance of 3D finite difference schemes in large scientific computing simulations

Gone are the days when engineers and scientists conducted most of their experiments empirically. During these decades, actual tests were carried out in order to assess the robustness and reliability of forthcoming product designs and prove theoretical models. With the advent of the computational era, scientific computing has definetely become a feasible solution compared with empirical methods, in terms of effort, cost and reliability. Large and massively parallel computational resources have reduced the simulation execution times and have improved their numerical results due to the refinement of the sampled domain. Several numerical methods coexist for solving the Partial Differential Equations (PDEs). Methods such as the Finite Element (FE) and the Finite Volume (FV) are specially well suited for dealing with problems where unstructured meshes are frequent. Unfortunately, this flexibility is not bestowed for free. These schemes entail higher memory latencies due to the handling of irregular data accesses. Conversely, the Finite Difference (FD) scheme has shown to be an efficient solution for problems where the structured meshes suit the domain requirements. Many scientific areas use this scheme due to its higher performance. This thesis focuses on improving FD schemes to leverage the performance of large scientific computing simulations. Different techniques are proposed such as the Semi-stencil, a novel algorithm that increases the FLOP/Byte ratio for medium- and high-order stencils operators by reducing the accesses and endorsing data reuse. The algorithm is orthogonal and can be combined with techniques such as spatial- or time-blocking, adding further improvement. New trends on Symmetric Multi-Processing (SMP) systems -where tens of cores are replicated on the same die- pose new challenges due to the exacerbation of the memory wall problem. In order to alleviate this issue, our research is focused on different strategies to reduce pressure on the cache hierarchy, particularly when different threads are sharing resources due to Simultaneous Multi-Threading (SMT). Several domain decomposition schedulers for work-load balance are introduced ensuring quasi-optimal results without jeopardizing the overall performance. We combine these schedulers with spatial-blocking and auto-tuning techniques, exploring the parametric space and reducing misses in last level cache. As alternative to brute-force methods used in auto-tuning, where a huge parametric space must be traversed to find a suboptimal candidate, performance models are a feasible solution. Performance models can predict the performance on different architectures, selecting suboptimal parameters almost instantly. In this thesis, we devise a flexible and extensible performance model for stencils. The proposed model is capable of supporting multi- and many-core architectures including complex features such as hardware prefetchers, SMT context and algorithmic optimizations. Our model can be used not only to forecast execution time, but also to make decisions about the best algorithmic parameters. Moreover, it can be included in run-time optimizers to decide the best SMT configuration based on the execution environment. Some industries rely heavily on FD-based techniques for their codes. Nevertheless, many cumbersome aspects arising in industry are still scarcely considered in academia research. In this regard, we have collaborated in the implementation of a FD framework which covers the most important features that an HPC industrial application must include. Some of the node-level optimization techniques devised in this thesis have been included into the framework in order to contribute in the overall application performance. We show results for a couple of strategic applications in industry: an atmospheric transport model that simulates the dispersal of volcanic ash and a seismic imaging model used in Oil & Gas industry to identify hydrocarbon-rich reservoirs.Atrás quedaron los días en los que ingenieros y científicos realizaban sus experimentos empíricamente. Durante esas décadas, se llevaban a cabo ensayos reales para verificar la robustez y fiabilidad de productos venideros y probar modelos teóricos. Con la llegada de la era computacional, la computación científica se ha convertido en una solución factible comparada con métodos empíricos, en términos de esfuerzo, coste y fiabilidad. Los supercomputadores han reducido el tiempo de las simulaciones y han mejorado los resultados numéricos gracias al refinamiento del dominio. Diversos métodos numéricos coexisten para resolver las Ecuaciones Diferenciales Parciales (EDPs). Métodos como Elementos Finitos (EF) y Volúmenes Finitos (VF) están bien adaptados para tratar problemas donde las mallas no estructuradas son frecuentes. Desafortunadamente, esta flexibilidad no se confiere de forma gratuita. Estos esquemas conllevan latencias más altas debido al acceso irregular de datos. En cambio, el esquema de Diferencias Finitas (DF) ha demostrado ser una solución eficiente cuando las mallas estructuradas se adaptan a los requerimientos. Esta tesis se enfoca en mejorar los esquemas DF para impulsar el rendimiento de las simulaciones en la computación científica. Se proponen diferentes técnicas, como el Semi-stencil, un nuevo algoritmo que incrementa el ratio de FLOP/Byte para operadores de stencil de orden medio y alto reduciendo los accesos y promoviendo el reuso de datos. El algoritmo es ortogonal y puede ser combinado con técnicas como spatial- o time-blocking, añadiendo mejoras adicionales. Las nuevas tendencias hacia sistemas con procesadores multi-simétricos (SMP) -donde decenas de cores son replicados en el mismo procesador- plantean nuevos retos debido a la exacerbación del problema del ancho de memoria. Para paliar este problema, nuestra investigación se centra en estrategias para reducir la presión en la jerarquía de cache, particularmente cuando diversos threads comparten recursos debido a Simultaneous Multi-Threading (SMT). Introducimos diversos planificadores de descomposición de dominios para balancear la carga asegurando resultados casi óptimos sin poner en riesgo el rendimiento global. Combinamos estos planificadores con técnicas de spatial-blocking y auto-tuning, explorando el espacio paramétrico y reduciendo los fallos en la cache de último nivel. Como alternativa a los métodos de fuerza bruta usados en auto-tuning donde un espacio paramétrico se debe recorrer para encontrar un candidato, los modelos de rendimiento son una solución factible. Los modelos de rendimiento pueden predecir el rendimiento en diferentes arquitecturas, seleccionando parámetros suboptimos casi de forma instantánea. En esta tesis, ideamos un modelo de rendimiento para stencils flexible y extensible. El modelo es capaz de soportar arquitecturas multi-core incluyendo características complejas como prefetchers, SMT y optimizaciones algorítmicas. Nuestro modelo puede ser usado no solo para predecir los tiempos de ejecución, sino también para tomar decisiones de los mejores parámetros algorítmicos. Además, puede ser incluido en optimizadores run-time para decidir la mejor configuración SMT. Algunas industrias confían en técnicas DF para sus códigos. Sin embargo, no todos los aspectos que aparecen en la industria han sido sometidos a investigación. En este aspecto, hemos diseñado e implementado desde cero una infraestructura DF que cubre las características más importantes que una aplicación industrial debe incluir. Algunas de las técnicas de optimización propuestas en esta tesis han sido incluidas para contribuir en el rendimiento global a nivel industrial. Mostramos resultados de un par de aplicaciones estratégicas para la industria: un modelo de transporte atmosférico que simula la dispersión de ceniza volcánica y un modelo de imagen sísmica usado en la industria del petroleo y gas para identificar reservas ricas en hidrocarburo

Parallel optimization algorithms for high performance computing : application to thermal systems

The need of optimization is present in every field of engineering. Moreover, applications requiring a multidisciplinary approach in order to make a step forward are increasing. This leads to the need of solving complex optimization problems that exceed the capacity of human brain or intuition. A standard way of proceeding is to use evolutionary algorithms, among which genetic algorithms hold a prominent place. These are characterized by their robustness and versatility, as well as their high computational cost and low convergence speed. Many optimization packages are available under free software licenses and are representative of the current state of the art in optimization technology. However, the ability of optimization algorithms to adapt to massively parallel computers reaching satisfactory efficiency levels is still an open issue. Even packages suited for multilevel parallelism encounter difficulties when dealing with objective functions involving long and variable simulation times. This variability is common in Computational Fluid Dynamics and Heat Transfer (CFD & HT), nonlinear mechanics, etc. and is nowadays a dominant concern for large scale applications. Current research in improving the performance of evolutionary algorithms is mainly focused on developing new search algorithms. Nevertheless, there is a vast knowledge of sequential well-performing algorithmic suitable for being implemented in parallel computers. The gap to be covered is efficient parallelization. Moreover, advances in the research of both new search algorithms and efficient parallelization are additive, so that the enhancement of current state of the art optimization software can be accelerated if both fronts are tackled simultaneously. The motivation of this Doctoral Thesis is to make a step forward towards the successful integration of Optimization and High Performance Computing capabilities, which has the potential to boost technological development by providing better designs, shortening product development times and minimizing the required resources. After conducting a thorough state of the art study of the mathematical optimization techniques available to date, a generic mathematical optimization tool has been developed putting a special focus on the application of the library to the field of Computational Fluid Dynamics and Heat Transfer (CFD & HT). Then the main shortcomings of the standard parallelization strategies available for genetic algorithms and similar population-based optimization methods have been analyzed. Computational load imbalance has been identified to be the key point causing the degradation of the optimization algorithm¿s scalability (i.e. parallel efficiency) in case the average makespan of the batch of individuals is greater than the average time required by the optimizer for performing inter-processor communications. It occurs because processors are often unable to finish the evaluation of their queue of individuals simultaneously and need to be synchronized before the next batch of individuals is created. Consequently, the computational load imbalance is translated into idle time in some processors. Several load balancing algorithms have been proposed and exhaustively tested, being extendable to any other population-based optimization method that needs to synchronize all processors after the evaluation of each batch of individuals. Finally, a real-world engineering application that consists on optimizing the refrigeration system of a power electronic device has been presented as an illustrative example in which the use of the proposed load balancing algorithms is able to reduce the simulation time required by the optimization tool.El aumento de las aplicaciones que requieren de una aproximación multidisciplinar para poder avanzar se constata en todos los campos de la ingeniería, lo cual conlleva la necesidad de resolver problemas de optimización complejos que exceden la capacidad del cerebro humano o de la intuición. En estos casos es habitual el uso de algoritmos evolutivos, principalmente de los algoritmos genéticos, caracterizados por su robustez y versatilidad, así como por su gran coste computacional y baja velocidad de convergencia. La multitud de paquetes de optimización disponibles con licencias de software libre representan el estado del arte actual en tecnología de optimización. Sin embargo, la capacidad de adaptación de los algoritmos de optimización a ordenadores masivamente paralelos alcanzando niveles de eficiencia satisfactorios es todavía una tarea pendiente. Incluso los paquetes adaptados al paralelismo multinivel tienen dificultades para gestionar funciones objetivo que requieren de tiempos de simulación largos y variables. Esta variabilidad es común en la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT), mecánica no lineal, etc. y es una de las principales preocupaciones en aplicaciones a gran escala a día de hoy. La investigación actual que tiene por objetivo la mejora del rendimiento de los algoritmos evolutivos está enfocada principalmente al desarrollo de nuevos algoritmos de búsqueda. Sin embargo, ya se conoce una gran variedad de algoritmos secuenciales apropiados para su implementación en ordenadores paralelos. La tarea pendiente es conseguir una paralelización eficiente. Además, los avances en la investigación de nuevos algoritmos de búsqueda y la paralelización son aditivos, por lo que el proceso de mejora del software de optimización actual se verá incrementada si se atacan ambos frentes simultáneamente. La motivación de esta Tesis Doctoral es avanzar hacia una integración completa de las capacidades de Optimización y Computación de Alto Rendimiento para así impulsar el desarrollo tecnológico proporcionando mejores diseños, acortando los tiempos de desarrollo del producto y minimizando los recursos necesarios. Tras un exhaustivo estudio del estado del arte de las técnicas de optimización matemática disponibles a día de hoy, se ha diseñado una librería de optimización orientada al campo de la Dinámica de Fluidos Computacional y la Transferencia de Calor (CFD & HT). A continuación se han analizado las principales limitaciones de las estrategias de paralelización disponibles para algoritmos genéticos y otros métodos de optimización basados en poblaciones. En el caso en que el tiempo de evaluación medio de la tanda de individuos sea mayor que el tiempo medio que necesita el optimizador para llevar a cabo comunicaciones entre procesadores, se ha detectado que la causa principal de la degradación de la escalabilidad o eficiencia paralela del algoritmo de optimización es el desequilibrio de la carga computacional. El motivo es que a menudo los procesadores no terminan de evaluar su cola de individuos simultáneamente y deben sincronizarse antes de que se cree la siguiente tanda de individuos. Por consiguiente, el desequilibrio de la carga computacional se convierte en tiempo de inactividad en algunos procesadores. Se han propuesto y testado exhaustivamente varios algoritmos de equilibrado de carga aplicables a cualquier método de optimización basado en una población que necesite sincronizar los procesadores tras cada tanda de evaluaciones. Finalmente, se ha presentado como ejemplo ilustrativo un caso real de ingeniería que consiste en optimizar el sistema de refrigeración de un dispositivo de electrónica de potencia. En él queda demostrado que el uso de los algoritmos de equilibrado de carga computacional propuestos es capaz de reducir el tiempo de simulación que necesita la herramienta de optimización

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications