Unified fault-tolerance framework for hybrid task-parallel message-passing applications

We present a unified fault-tolerance framework for task-parallel message-passing applications to mitigate transient errors. First, we propose a fault-tolerant message-logging protocol that only requires the restart of the task that experienced the error and transparently handles any message passing interface calls inside the task. In our experiments we demonstrate that our fault-tolerant solution has a reasonable overhead, with a maximum observed overhead of 4.5%. We also show that fine-grained parallelization is important for hiding the overheads related to the protocol as well as the recovery of tasks. Secondly, we develop a mathematical model to unify task-level checkpointing and our protocol with system-wide checkpointing in order to provide complete failure coverage. We provide closed formulas for the optimal checkpointing interval and the performance score of the unified scheme. Experimental results show that the performance improvement can be as high as 98% with the unified scheme.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the FI-DGR 2013 scholarship and the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402 and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

Space Reclamation for Uncoordinated Checkpointing in Message-Passing Systems

Checkpointing and rollback recovery are techniques that can provide efficient recovery from transient process failures. In a message-passing system, the rollback of a message sender may cause the rollback of the corresponding receiver, and the system needs to roll back to a consistent set of checkpoints called recovery line. If the processes are allowed to take uncoordinated checkpoints, the above rollback propagation may result in the domino effect which prevents recovery line progression. Traditionally, only obsolete checkpoints before the global recovery line can be discarded, and the necessary and sufficient condition for identifying all garbage checkpoints has remained an open problem. A necessary and sufficient condition for achieving optimal garbage collection is derived and it is proved that the number of useful checkpoints is bounded by N(N+1)/2, where N is the number of processes. The approach is based on the maximum-sized antichain model of consistent global checkpoints and the technique of recovery line transformation and decomposition. It is also shown that, for systems requiring message logging to record in-transit messages, the same approach can be used to achieve optimal message log reclamation. As a final topic, a unifying framework is described by considering checkpoint coordination and exploiting piecewise determinism as mechanisms for bounding rollback propagation, and the applicability of the optimal garbage collection algorithm to domino-free recovery protocols is demonstrated

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

RADIC II : a fault tolerant architecture with flexible dynamic redundancy

The demand for computational power has been leading the improvement of the High Performance Computing (HPC) area, generally represented by the use of distributed systems like clusters of computers running parallel applications. In this area, fault tolerance plays an important role in order to provide high availability isolating the application from the faults effects. Performance and availability form an undissociable binomial for some kind of applications. Therefore, the fault tolerant solutions must take into consideration these two constraints when it has been designed. In this dissertation, we present a few side-effects that some fault tolerant solutions may presents when recovering a failed process. These effects may causes degradation of the system, affecting mainly the overall performance and availability. We introduce RADIC-II, a fault tolerant architecture for message passing based on RADIC (Redundant Array of Distributed Independent Fault Tolerance Controllers) architecture. RADIC-II keeps as maximum as possible the RADIC features of transparency, decentralization, flexibility and scalability, incorporating a flexible dynamic redundancy feature, allowing to mitigate or to avoid some recovery side-effects.La demanda de computadores más veloces ha provocado el incremento del área de computación de altas prestaciones, generalmente representado por el uso de sistemas distribuidos como los clusters de computadores ejecutando aplicaciones paralelas. En esta área, la tolerancia a fallos juega un papel muy importante a la hora de proveer alta disponibilidad, aislando los efectos causados por los fallos. Prestaciones y disponibilidad componen un binomio indisociable para algunos tipos de aplicaciones. Por eso, las soluciones de tolerancia a fallos deben tener en consideración estas dos restricciones desde el momento de su diseño. En esta disertación, presentamos algunos efectos colaterales que se puede presentar en ciertas soluciones tolerantes a fallos cuando recuperan un proceso fallado. Estos efectos pueden causar una degradación del sistema, afectando las prestaciones y disponibilidad finales. Presentamos RADIC-II, una arquitectura tolerante a fallos para paso de mensajes basada en la arquitectura RADIC (Redundant Array of Distributed Independent Fault Tolerance Controllers). RADIC-II mantiene al máximo posible las características de transparencia, descentralización, flexibilidad y escalabilidad existentes en RADIC, e incorpora una flexible funcionalidad de redundancia dinámica, que permite mitigar o evitar algunos efectos colaterales en la recuperación

Application-level Fault Tolerance and Resilience in HPC Applications

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] As necesidades computacionais das distintas ramas da ciencia medraron enormemente nos últimos anos, o que provocou un gran crecemento no rendemento proporcionado polos supercomputadores. Cada vez constrúense sistemas de computación de altas prestacións de maior tamaño, con máis recursos hardware de distintos tipos, o que fai que as taxas de fallo destes sistemas tamén medren. Polo tanto, o estudo de técnicas de tolerancia a fallos eficientes é indispensábel para garantires que os programas científicos poidan completar a súa execución, evitando ademais que se dispare o consumo de enerxía. O checkpoint/restart é unha das técnicas máis populares. Sen embargo, a maioría da investigación levada a cabo nas últimas décadas céntrase en estratexias stop-and-restart para aplicacións de memoria distribuída tralo acontecemento dun fallo-parada. Esta tese propón técnicas checkpoint/restart a nivel de aplicación para os modelos de programación paralela roáis populares en supercomputación. Implementáronse protocolos de checkpointing para aplicacións híbridas MPI-OpenMP e aplicacións heteroxéneas baseadas en OpenCL, en ámbolos dous casos prestando especial coidado á portabilidade e maleabilidade da solución. En canto a aplicacións de memoria distribuída, proponse unha solución de resiliencia que pode ser empregada de forma xenérica en aplicacións MPI SPMD, permitindo detectar e reaccionar a fallos-parada sen abortar a execución. Neste caso, os procesos fallidos vólvense a lanzar e o estado da aplicación recupérase cunha volta atrás global. A maiores, esta solución de resiliencia optimizouse implementando unha volta atrás local, na que só os procesos fallidos volven atrás, empregando un protocolo de almacenaxe de mensaxes para garantires a consistencia e o progreso da execución. Por último, propónse a extensión dunha librería de checkpointing para facilitares a implementación de estratexias de recuperación ad hoc ante conupcións de memoria. En moitas ocasións, estos erros poden ser xestionados a nivel de aplicación, evitando desencadear un fallo-parada e permitindo unha recuperación máis eficiente.[Resumen] El rápido aumento de las necesidades de cómputo de distintas ramas de la ciencia ha provocado un gran crecimiento en el rendimiento ofrecido por los supercomputadores. Cada vez se construyen sistemas de computación de altas prestaciones mayores, con más recursos hardware de distintos tipos, lo que hace que las tasas de fallo del sistema aumenten. Por tanto, el estudio de técnicas de tolerancia a fallos eficientes resulta indispensable para garantizar que los programas científicos puedan completar su ejecución, evitando además que se dispare el consumo de energía. La técnica checkpoint/restart es una de las más populares. Sin embargo, la mayor parte de la investigación en este campo se ha centrado en estrategias stop-and-restart para aplicaciones de memoria distribuida tras la ocurrencia de fallos-parada. Esta tesis propone técnicas checkpoint/restart a nivel de aplicación para los modelos de programación paralela más populares en supercomputación. Se han implementado protocolos de checkpointing para aplicaciones híbridas MPI-OpenMP y aplicaciones heterogéneas basadas en OpenCL, prestando en ambos casos especial atención a la portabilidad y la maleabilidad de la solución. Con respecto a aplicaciones de memoria distribuida, se propone una solución de resiliencia que puede ser usada de forma genérica en aplicaciones MPI SPMD, permitiendo detectar y reaccionar a fallosparada sin abortar la ejecución. En su lugar, se vuelven a lanzar los procesos fallidos y se recupera el estado de la aplicación con una vuelta atrás global. A mayores, esta solución de resiliencia ha sido optimizada implementando una vuelta atrás local, en la que solo los procesos fallidos vuelven atrás, empleando un protocolo de almacenaje de mensajes para garantizar la consistencia y el progreso de la ejecución. Por último, se propone una extensión de una librería de checkpointing para facilitar la implementación de estrategias de recuperación ad hoc ante corrupciones de memoria. Muchas veces, este tipo de errores puede gestionarse a nivel de aplicación, evitando desencadenar un fallo-parada y permitiendo una recuperación más eficiente.[Abstract] The rapid increase in the computational demands of science has lead to a pronounced growth in the performance offered by supercomputers. As High Performance Computing (HPC) systems grow larger, including more hardware components of different types, the system's failure rate becomes higher. Efficient fault tolerance techniques are essential not only to ensure the execution completion but also to save energy. Checkpoint/restart is one of the most popular fault tolerance techniques. However, most of the research in this field is focused on stop-and-restart strategies for distributed-memory applications in the event of fail-stop failures. Thís thesis focuses on the implementation of application-level checkpoint/restart solutions for the most popular parallel programming models used in HPC. Hence, we have implemented checkpointing solutions to cope with fail-stop failures in hybrid MPI-OpenMP applications and OpenCL-based programs. Both strategies maximize the restart portability and malleability, ie., the recovery can take place on machines with different CPU / accelerator architectures, and/ or operating systems, and can be adapted to the available resources (number of cores/accelerators). Regarding distributed-memory applications, we propose a resilience solution that can be generally applied to SPMD MPI programs. Resilient applications can detect and react to failures without aborting their execution upon fail-stop failures. Instead, failed processes are re-spawned, and the application state is recovered through a global rollback. Moreover, we have optimized this resilience proposal by implementing a local rollback protocol, in which only failed processes rollback to a previous state, while message logging enables global consistency and further progress of the computation. Finally, we have extended a checkpointing library to facilitate the implementation of ad hoc recovery strategies in the event of soft errors) caused by memory corruptions. Many times, these errors can be handled at the software-Ievel, tIms, avoiding fail-stop failures and enabling a more efficient recovery

Using Rollback Avoidance to Mitigate Failures in Next-Generation Extreme-Scale Systems

High-performance computing (HPC) systems enable scientists to numerically model complex phenomena in many important physical systems. The next major milestone in the development of HPC systems is the construction of the first supercomputer capable executing more than an exaflop, 10^18 floating point operations per second. On systems of this scale, failures will occur much more frequently than on current systems. As a result, resilience is a key obstacle to building next-generation extreme-scale systems. Coordinated checkpointing is currently the most widely-used mechanism for handling failures on HPC systems. Although coordinated checkpointing remains effective on current systems, increasing the scale of today\u27s systems to build next-generation systems will increase the cost of fault tolerance as more and more time is taken away from the application to protect against or recover from failure. Rollback avoidance techniques seek to mitigate the cost of checkpoint/restart by allowing an application to continue its execution rather than rolling back to an earlier checkpoint when failures occur. These techniques include failure prediction and preventive migration, replicated computation, fault-tolerant algorithms, and software-based memory fault correction. In this thesis, I examine how rollback avoidance techniques can be used to address failures on extreme-scale systems. Using a combination of analytic modeling and simulation, I evaluate the potential impact of rollback avoidance on these systems. I then present a novel rollback avoidance technique that exploits similarities in application memory. Finally, I examine the feasibility of using this technique to protect against memory faults in kernel memory

Lazy Fault Recovery for Redundant Mpi

Distributed Systems (DS) where multiple computers share a workload across a network, are used everywhere, from data intensive computations to storage and machine learning. DS provide a relatively cheap and efficient solution that allows stability with improved performance for computational intensive applications. In a DS faults and failures are the norm not the exception. At any moment data corruption can occur especially since a DS usually consists of hundred to thousands of units of commodity hardware. The large number and quality of components guarantees, by probability, that at any given time some of the components will not be working and some of them will not recover from failure. DS can experience problems caused by application bugs, operating systems bugs, failures with disks, memory, connectors, networking, power supply, and other components; therefore, constant monitoring and failure detection are fundamental. Automatic recovery must be integral to the system. One of the most commonly used programming languages for DS is Message Passing Interface (MPI). Unfortunately MPI does not support fault detection or recovery. In this thesis, we build a recovery mechanism based on replicas that works on top of the asynchronous fault detection implemented in previous work. Results shows that our recovery implementation is successful and the overhead in execution time is minimal

Algorithm Based Fault Tolerance: A Perspective from Algorithmic and Communication Characteristics of Parallel Algorithms

Checkpoint and recovery cost imposed by checkpoint/restart (CP/R) is a crucial performance issue for high-performance computing (HPC) applications. In comparison, Algorithm-Based Fault Tolerance (ABFT) is a promising fault tolerance method with low recovery overhead, but it suffers from the inadequacy of universal applicability, i.e., tied to a specific application or algorithm. Till date, providing fault tolerance for matrix-based algorithms for linear systems has been the research focus of ABFT schemes. As a consequence, it necessitates a comprehensive exploration of ABFT research to widen its scope to other types of parallel algorithms and applications. In this thesis, we go beyond traditional ABFT and focus on other types of parallel applications not covered by traditional ABFT. In that regard, rather than an emphasis on a single application at a time, we consider the algorithmic and communication characteristics of a class of parallel applications to design efficient fault tolerance and recovery strategies for that class of parallel applications. The communication characteristics determine how to distributively replicate the fault recovery data (we call it the {\em critical data}) of a process, and the algorithmic characteristics determine what the application-specific data is to be replicated to minimize fault tolerance and recovery cost. Based on communication characteristics, parallel algorithms can be broadly classified as (i) embarrassingly parallel algorithms, where processes have infrequent or rare interactions, and (ii) communication-intensive parallel algorithms, where processes have significant interactions. In this thesis, through different case studies, we design ABFT for these two categories of algorithms by considering their algorithmic and communication characteristics. Analysis of these parallel algorithms reveals that a process contains sufficient information that can help to rebuild a computational state if any failure occurs during the computation. We define this information as critical data, the minimal application-level data required to be saved (securely) so that a failed process can be fully recovered from a most recent consistent state using this fault recovery data. How the communication dependencies among processes are utilized to replicate fault recovery data is directly related to the system’s fault tolerance performance. We propose ABFT for parallel search algorithms, which belong to the class of embarrassingly parallel algorithms. Parallel search algorithms are the well-known solution techniques for discrete optimization problems (DOP). DOP covers a broad class of (parallel) applications from search problems in AI to computer games, e.g., Chess and various games, traveling salesman problem, various AI search problems. As a case study, we choose the parallel iterative deepening A* (PIDA*) algorithm and integrate application-level fault tolerance with the algorithm by replicating critical data periodically to make it resilient. In the category of communication-intensive algorithms, we choose Dynamic programming (DP) which is a widely used algorithm paradigm for optimization problems. We choose parallel DP algorithm as a case study and propose ABFT for such applications. We present a detailed analysis of the characteristics of parallel DP algorithms and show that the algorithmic features reduce the cardinality of critical data into a single data in case of $n$-data dependent task. We demonstrate the idea with two popular DP class of applications: (i) the traveling salesman problem (TSP), and (ii) the longest common subsequence (LCS) problem. Minimal storage and recovery overhead are the prime concern in FT design. On that regard, we demonstrate that further optimization in critical data is possible for particular DP class of problems, where the degree of dependency for a subproblem is small and fixed at each iteration. We discuss it with the 0/1 knapsack problem as a case study and propose an ABFT scheme where, instead of replicating the critical data, we replicate a bit-vector flag in peer process's memory which is later used to rebuild the lost data of a failed process. Theoretical and experimental results demonstrate that our proposed methods perform significantly better than the conventional CP/R in terms of fault tolerance and recovery overheads, and also in storage overhead in the presence of single and multiple simultaneous failures

Checkpointing of parallel applications in a Grid environment

The Grid environment is generic, heterogeneous, and dynamic with lots of unreliable resources making it very exposed to failures. The environment is unreliable because it is geographically dispersed involving multiple autonomous administrative domains and it is composed of a large number of components. Examples of failures in the Grid environment can be: application crash, Grid node crash, network failures, and Grid system component failures. These types of failures can affect the execution of parallel/distributed application in the Grid environment and so, protections against these faults are crucial. Therefore, it is essential to develop efficient fault tolerant mechanisms to allow users to successfully execute Grid applications. One of the research challenges in Grid computing is to be able to develop a fault tolerant solution that will ensure Grid applications are executed reliably with minimum overhead incurred. While checkpointing is the most common method to achieve fault tolerance, there is still a lot of work to be done to improve the efficiency of the mechanism. This thesis provides an in-depth description of a novel solution for checkpointing parallel applications executed on a Grid. The checkpointing mechanism implemented allows to checkpoint an application at regions where there is no interprocess communication involved and therefore reducing the checkpointing overhead and checkpoint size

Mitigation of failures in high performance computing via runtime techniques

As machines increase in scale, it is predicted that failure rates of supercomputers will correspondingly increase. Even though the mean time to failure (MTTF) of individual component is high, the large number of components significantly decreases the system MTTF. Meanwhile, the decreasing size of transistors has been critical to the increase in capacity of supercomputers. The smaller the transistors are, silent data corruptions (SDC) are likely to occur more frequently. SDCs do not inhibit execution, but may silently lead to incorrect results. In this thesis, we leverage runtime system and compiler techniques to mitigate a significant fraction of failures automatically with low overhead. The main goals of various system-level fault tolerance strategies designed in this thesis are: reducing the extra cost added to application execution while improving system reliability; automatically adjusting fault tolerance decisions without user intervention based on environmental changes; protecting applications not only from fail-stop failures but also from silent data corruptions. The main contributions of this thesis are development of a semi-blocking checkpoint protocol that overlaps application execution with fault tolerance operation to reduce the overhead of checkpointing, a runtime system technique for automatic checkpoint and restart without user intervention, a holistic framework (ACR) for automatically detecting and recovering from silent data corruptions and a framework called FlipBack that provides targeted protection against silent data corruption with low cost