667 research outputs found
Checkpoint restart support for heterogeneous HPC applications
As we approach the era of exa-scale computing, fault tolerance is of growing importance. The increasing number of cores as well as the increased complexity of modern heterogenous systems result in substantial decrease of the expected mean time between failures. Among the different fault tolerance techniques, checkpoint/restart is vastly adopted in supercomputing systems. Although many supercomputers in the TOP 500 list use GPUs, only a few checkpoint restart mechanism support GPUs.In this paper, we extend an application level checkpoint library, called fault tolerance interface (FTI), to support multi-node/multi-GPU checkpoints. In contrast to previous work, our library includes a memory manager, which upon a checkpoint invocation tracks the actual location of the data to be stored and handles the data accordingly. We analyze the overhead of the checkpoint/restart procedure and we present a series of optimization steps to massively decrease the checkpoint and recovery time of our implementation. To further reduce the checkpoint time we present a differential checkpoint approach which writes only the updated data to the checkpoint file. Our approach is evaluated and, in the best case scenario, the execution time of a normal checkpoint is reduced by 15x in contrast with a non-optimized version, in the case of differential checkpoint the overhead can drop to 2.6% when checkpointing every 30s.The research leading to these results has received funding from the European Union’s Horizon 2020 Programme under the LEGaTO Project (www.legato-project.eu), grant agreement #780681.Peer ReviewedPostprint (author's final draft
Algorithm-Directed Crash Consistence in Non-Volatile Memory for HPC
Fault tolerance is one of the major design goals for HPC. The emergence of
non-volatile memories (NVM) provides a solution to build fault tolerant HPC.
Data in NVM-based main memory are not lost when the system crashes because of
the non-volatility nature of NVM. However, because of volatile caches, data
must be logged and explicitly flushed from caches into NVM to ensure
consistence and correctness before crashes, which can cause large runtime
overhead.
In this paper, we introduce an algorithm-based method to establish crash
consistence in NVM for HPC applications. We slightly extend application data
structures or sparsely flush cache blocks, which introduce ignorable runtime
overhead. Such extension or cache flushing allows us to use algorithm knowledge
to \textit{reason} data consistence or correct inconsistent data when the
application crashes. We demonstrate the effectiveness of our method for three
algorithms, including an iterative solver, dense matrix multiplication, and
Monte-Carlo simulation. Based on comprehensive performance evaluation on a
variety of test environments, we demonstrate that our approach has very small
runtime overhead (at most 8.2\% and less than 3\% in most cases), much smaller
than that of traditional checkpoint, while having the same or less
recomputation cost.Comment: 12 page
Checkpointing as a Service in Heterogeneous Cloud Environments
A non-invasive, cloud-agnostic approach is demonstrated for extending
existing cloud platforms to include checkpoint-restart capability. Most cloud
platforms currently rely on each application to provide its own fault
tolerance. A uniform mechanism within the cloud itself serves two purposes: (a)
direct support for long-running jobs, which would otherwise require a custom
fault-tolerant mechanism for each application; and (b) the administrative
capability to manage an over-subscribed cloud by temporarily swapping out jobs
when higher priority jobs arrive. An advantage of this uniform approach is that
it also supports parallel and distributed computations, over both TCP and
InfiniBand, thus allowing traditional HPC applications to take advantage of an
existing cloud infrastructure. Additionally, an integrated health-monitoring
mechanism detects when long-running jobs either fail or incur exceptionally low
performance, perhaps due to resource starvation, and proactively suspends the
job. The cloud-agnostic feature is demonstrated by applying the implementation
to two very different cloud platforms: Snooze and OpenStack. The use of a
cloud-agnostic architecture also enables, for the first time, migration of
applications from one cloud platform to another.Comment: 20 pages, 11 figures, appears in CCGrid, 201
CRAFT: A library for easier application-level Checkpoint/Restart and Automatic Fault Tolerance
In order to efficiently use the future generations of supercomputers, fault
tolerance and power consumption are two of the prime challenges anticipated by
the High Performance Computing (HPC) community. Checkpoint/Restart (CR) has
been and still is the most widely used technique to deal with hard failures.
Application-level CR is the most effective CR technique in terms of overhead
efficiency but it takes a lot of implementation effort. This work presents the
implementation of our C++ based library CRAFT (Checkpoint-Restart and Automatic
Fault Tolerance), which serves two purposes. First, it provides an extendable
library that significantly eases the implementation of application-level
checkpointing. The most basic and frequently used checkpoint data types are
already part of CRAFT and can be directly used out of the box. The library can
be easily extended to add more data types. As means of overhead reduction, the
library offers a build-in asynchronous checkpointing mechanism and also
supports the Scalable Checkpoint/Restart (SCR) library for node level
checkpointing. Second, CRAFT provides an easier interface for User-Level
Failure Mitigation (ULFM) based dynamic process recovery, which significantly
reduces the complexity and effort of failure detection and communication
recovery mechanism. By utilizing both functionalities together, applications
can write application-level checkpoints and recover dynamically from process
failures with very limited programming effort. This work presents the design
and use of our library in detail. The associated overheads are thoroughly
analyzed using several benchmarks
What does fault tolerant Deep Learning need from MPI?
Deep Learning (DL) algorithms have become the de facto Machine Learning (ML)
algorithm for large scale data analysis. DL algorithms are computationally
expensive - even distributed DL implementations which use MPI require days of
training (model learning) time on commonly studied datasets. Long running DL
applications become susceptible to faults - requiring development of a fault
tolerant system infrastructure, in addition to fault tolerant DL algorithms.
This raises an important question: What is needed from MPI for de- signing
fault tolerant DL implementations? In this paper, we address this problem for
permanent faults. We motivate the need for a fault tolerant MPI specification
by an in-depth consideration of recent innovations in DL algorithms and their
properties, which drive the need for specific fault tolerance features. We
present an in-depth discussion on the suitability of different parallelism
types (model, data and hybrid); a need (or lack thereof) for check-pointing of
any critical data structures; and most importantly, consideration for several
fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI
and their applicability to fault tolerant DL implementations. We leverage a
distributed memory implementation of Caffe, currently available under the
Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches
by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation
using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies
demonstrates the effectiveness of the proposed fault tolerant DL implementation
using OpenMPI based ULFM
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
A runtime heuristic to selectively replicate tasks for application-specific reliability targets
In this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App_FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.This work was supported by 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 in part by the
European Union (FEDER funds) under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft
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