A versatile programming model for dynamic task scheduling on cluster computers

This dissertation studies the development of application programs for parallel and distributed computer systems, especially PC clusters. A methodology is proposed to increase the efficiency of code development, the productivity of programmers and enhance performance of executing the developed programs on PC clusters while facilitating improvement of scalability and code portability of these programs. A new programming model, named the Super-Programming Model (SPM), is created. Programs are developed assuming an instruction set architecture comprised of SuperInstructions (SIs). SPM models the target system as a large Virtual Machine (VM); VM contains functional units which are underlain with sub-computer systems and SIs are implemented with codes. When these functional units execute SIs, their codes will run on member computers to perform the corresponding operations. This approach resembles the process of designing instruction sets for microprocessors but the VM employs much coarser instructions and data structures. SIs use Super-Data Blocks (SDBs) as their operands. Each SI is assigned to a single member computer and is indivisible (i.e., its implementation is not interrupted for I/O). SIs have predictable execution times because SDB sizes are limited by predefined thresholds. These qualities of SIs help dynamic load balancing. Employing software to implement instructions makes this approach more flexible. The developed programs fit to architectures of cluster systems better. SPM provides mechanisms, such as dynamic load balancing, to assure the efficient execution of programs. The vast majority of current programming models lack such mechanisms for distributed environments that suffer from long communication latencies. Since SPM employs coarse-grain tasks, the overall management overhead is small. SDB access can often overlap the execution of other SIs; a cache system further decreases average memory latencies. Since all SDBs are virtual entities, with the runtime system support, they can be accessed in parallel and efficiently minimizes additional constraints to parallelism from underlying computer systems. In this research, a reference implementation of VM has been developed. A performance estimation model is developed that takes these features into account. Finally, the definition of scalability for parallel/distributed processing is refined to represent a multi-dimensional entity. Sample cases are analyzed

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

Atomic dataflow model

With the recent switch in the design of general purpose processors from frequency scaling of a single processor core towards increasing the number of processor cores, parallel programming became important not only for scientific programming but also for general purpose programming. This also stressed the importance of programmability of existing parallel programming models which were primarily designed for performance. It was soon recognized that new programming models are needed that will make parallel programming possible not only to experts, but to a general programming community. Transactional Memory (TM) is an example which follows this premise. It improves dramatically over any previous synchronization mechanism in terms of programmability and composability, at the price of possibly reduced performance. The main source of performance degradation in Transactional Memory is the overhead of transactional execution. Our work on parallelizing Quake game engine is a clear example of this problem. We show that Software Transactional Memory is superior in terms of programmability compared to lock based programming, but that performance is hindered due to extreme amount of overhead introduced by transactional execution. In the meantime, a significant research effort has been invested in overcoming this problem. Our approach is aimed towards improving the performance of transactional code by reducing transactional data conflicts. The idea is based on the organization of the code in which highly conflicting data is promoted to dataflow tokens that coordinate the execution of transactions. The main contribution of this thesis is Atomic Dataflow model (ADF), a new task-based parallel programming model for C/C++ that integrates dataflow abstractions into the shared memory programming model. The ADF model provides language constructs that allow a programmer to delineate a program into a set of tasks and to explicitly define data dependencies for each task. The task dependency information is conveyed to the ADF runtime system that constructs a dataflow task graph that governs the execution of a program. Additionally, the ADF model allows tasks to share data. The key idea is that computation is triggered by dataflow between tasks but that, within a task, execution occurs by making atomic updates to common mutable state. To that end, the ADF model employs transactional memory, which guarantees atomicity of shared memory updates. The second contribution of this thesis is DaSH - the first comprehensive benchmark suite for hybrid dataflow and shared memory programming models. DaSH features 11 benchmarks, each representing one of the Berkeley dwarfs that capture patterns of communication and computation common to a wide range of emerging applications. DaSH includes sequential and shared-memory implementations based on OpenMP and TBB to facilitate easy comparison between hybrid dataflow implementations and traditional shared memory implementations. We use DaSH not only to evaluate the ADF model, but to also compare it with other two hybrid dataflow models in order to identify the advantages and shortcomings of such models, and motivate further research on their characteristics. Finally, we study applicability of hybrid dataflow models for parallelization of the game engine. We show that hybrid dataflow models decrease the complexity of the parallel game engine implementation by eliminating or restructuring the explicit synchronization that is necessary in shared memory implementations. The corresponding implementations also exhibit good scalability and better speedup than the shared memory parallel implementations, especially in the case of a highly congested game world that contains a large number of game objects. Ultimately, on an eight core machine we were able to obtain 4.72x speedup compared to the sequential baseline, and to improve 49% over the lock-based parallel implementation based on work-sharing.Con el reciente cambio en el diseño de los procesadores de propósito general pasando del aumento de frecuencia al incremento del número de núcleos, la programación paralela se ha convertido en importante no solo para la comunidad científica sino también para la programación en general. Este hecho ha enfatizado la importancia de la programabilidad de los modelos actuales de programación paralela, cuyo objetivo era el rendimiento. Pronto se observó la necesidad de nuevos modelos de programación, para hacer factible la programación paralela a toda la comunidad. Transactional Memory (TM) es un ejemplo de dicho objetivo. Supone una gran mejora sobre cualquier método anterior de sincronización en términos de programabilidad, con una posible reducción del rendimiento como coste. La razón principal de dicha degradación es el sobrecoste de la ejecución transaccional. Nuestro trabajo en la paralelización del motor del juego Quake es un claro ejemplo de este problema. Demostramos que Software Transactional Memory es superior en términos de programabilidad a los modelos de programación basados en locks, pero que el rendimiento es entorpecido por el sobrecoste introducido por TM. Mientras tanto, se ha invertido un importante esfuerzo de investigación para superar dicho problema. Nuestra solución se dirige hacia la mejora del rendimiento del código transaccional reduciendo los conflictos con la información contenida en las transacciones. La idea se basa en la organización del código en el cual la información conflictiva es promocionada a señales del flujo de datos que coordinan la ejecución de las transacciones. La contribución principal de esta tesis es Atomic Dataflow Model (ADF), un nuevo modelo de programación para C/C++ basado en tareas que integra abstracciones de flujo de datos en el modelo de programación de la memoria compartida. El modelo ADF provee construcciones del lenguaje que permiten al programador la definición del programa como un conjunto de tareas, además de la definición explícita de las dependencias de datos para cada tarea. La información de dependencia de la tarea se transmite al runtime de ADF, que construye un grafo de tareas que es el que controla la ejecución de un programa. Adicionalmente, el modelo ADF permite que las tareas compartan información. La idea principal es que la computación es activada por el flujo de datos entre tareas, pero que dentro de una tarea la ejecución ocurre haciendo actualizaciones atómicas a un estado común mutable. Para conseguir este fin, el modelo ADF utiliza TM, que garantiza la atomicidad en las modificaciones de la memoria compartida. La segunda contribución es DaSH, el primer conjunto de benchmarks para los modelos de programación de flujo de datos híbridos y los de memoria compartida. DaSH contiene 11 benchmarks, cada uno representativo de uno de los Berkeley dwarfs que captura patrones de comunicaciones y procesamiento comunes en un amplio rango de aplicaciones emergentes. DaSH incluye implementaciones secuenciales y de memoria compartida basadas en OpenMP y TBB que facilitan la comparación entre los modelos híbridos de flujo de datos e implementaciones de memoria compartida. Nosotros usamos DaSH no solo para evaluar ADF, sino también para compararlo con otros dos modelos híbridos para identificar sus ventajas. Finalmente, estudiamos la aplicabilidad de dichos modelos híbridos para la paralelización del motor del juego. Mostramos que disminuyen la complejidad de la implementación paralela, eliminando o reestructurando la sincronización explícita que es necesaria en las implementaciones de memoria compartida. También se observa una buena escalabilidad y una aceleración mejor, especialmente en el caso de un ambiente de juego muy cargado. En última instancia, sobre una máquina con ocho núcleos se ha obtenido una aceleración del 4.72x comparado con el código secuencial, y una mejora del 49% sobre la implementación paralela basada en locks

Reliability for exascale computing : system modelling and error mitigation for task-parallel HPC applications

As high performance computing (HPC) systems continue to grow, their fault rate increases. Applications running on these systems have to deal with rates on the order of hours or days. Furthermore, some studies for future Exascale systems predict the rates to be on the order of minutes. As a result, efficient fault tolerance solutions are needed to be able to tolerate frequent failures. A fault tolerance solution for future HPC and Exascale systems must be low-cost, efficient and highly scalable. It should have low overhead in fault-free execution and provide fast restart because long-running applications are expected to experience many faults during the execution. Meanwhile task-based dataflow parallel programming models (PM) are becoming a popular paradigm in HPC applications at large scale. For instance, we see the adaptation of task-based dataflow parallelism in OpenMP 4.0, OmpSs PM, Argobots and Intel Threading Building Blocks. In this thesis we propose fault-tolerance solutions for task-parallel dataflow HPC applications. Specifically, first we design and implement a checkpoint/restart and message-logging framework to recover from errors. We then develop performance models to investigate the benefits of our task-level frameworks when integrated with system-wide checkpointing. Moreover, we design and implement selective task replication mechanisms to detect and recover from silent data corruptions in task-parallel dataflow HPC applications. Finally, we introduce a runtime-based coding scheme to detect and recover from memory errors in these applications. Considering the span of all of our schemes, we see that they provide a fairly high failure coverage where both computation and memory is protected against errors.A medida que los Sistemas de Cómputo de Alto rendimiento (HPC por sus siglas en inglés) siguen creciendo, también las tasas de fallos aumentan. Las aplicaciones que se ejecutan en estos sistemas tienen una tasa de fallos que pueden estar en el orden de horas o días. Además, algunos estudios predicen que los fallos estarán en el orden de minutos en los Sistemas Exascale. Por lo tanto, son necesarias soluciones eficientes para la tolerancia a fallos que puedan tolerar fallos frecuentes. Las soluciones para tolerancia a fallos en los Sistemas futuros de HPC y Exascale tienen que ser de bajo costo, eficientes y altamente escalable. El sobrecosto en la ejecución sin fallos debe ser bajo y también se debe proporcionar reinicio rápido, ya que se espera que las aplicaciones de larga duración experimenten muchos fallos durante la ejecución. Por otra parte, los modelos de programación paralelas basados en tareas ordenadas de acuerdo a sus dependencias de datos, se están convirtiendo en un paradigma popular en aplicaciones HPC a gran escala. Por ejemplo, los siguientes modelos de programación paralela incluyen este tipo de modelo de programación OpenMP 4.0, OmpSs, Argobots e Intel Threading Building Blocks. En esta tesis proponemos soluciones de tolerancia a fallos para aplicaciones de HPC programadas en un modelo de programación paralelo basado tareas. Específicamente, en primer lugar, diseñamos e implementamos mecanismos “checkpoint/restart” y “message-logging” para recuperarse de los errores. Para investigar los beneficios de nuestras herramientas a nivel de tarea cuando se integra con los “system-wide checkpointing” se han desarrollado modelos de rendimiento. Por otra parte, diseñamos e implementamos mecanismos de replicación selectiva de tareas que permiten detectar y recuperarse de daños de datos silenciosos en aplicaciones programadas siguiendo el modelo de programación paralela basadas en tareas. Por último, se introduce un esquema de codificación que funciona en tiempo de ejecución para detectar y recuperarse de los errores de la memoria en estas aplicaciones. Todos los esquemas propuestos, en conjunto, proporcionan una cobertura bastante alta a los fallos tanto si estos se producen el cálculo o en la memoria.Postprint (published version

Geomatics Applications to Contemporary Social and Environmental Problems in Mexico

Trends in geospatial technologies have led to the development of new powerful analysis and representation techniques that involve processing of massive datasets, some unstructured, some acquired from ubiquitous sources, and some others from remotely located sensors of different kinds, all of which complement the structured information produced on a regular basis by governmental and international agencies. In this chapter, we provide both an extensive revision of such techniques and an insight of the applications of some of these techniques in various study cases in Mexico for various scales of analysis: from regional migration flows of highly qualified people at the country level and the spatio-temporal analysis of unstructured information in geotagged tweets for sentiment assessment, to more local applications of participatory cartography for policy definitions jointly between local authorities and citizens, and an automated method for three dimensional (3D) modelling and visualisation of forest inventorying with laser scanner technology