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Large-scale parallel computing is relying increasingly on clusters with thousands of processors. At such large counts of compute nodes, faults are becoming common place. Current techniques to tolerate faults focus on reactive schemes to recover from faults and generally rely on a checkpoint/restart mechanism. Yet, in today's systems, node failures can often be anticipated by detecting a deteriorating health status.Instead of a reactive scheme for fault tolerance (FT), we are promoting a proactive one where processes automatically migrate from "unhealthy" nodes to healthy ones. Our approach relies on operating system virtualization techniques exemplified by but not limited to Xen. This paper contributes an automatic and transparent mechanism for proactive FT for arbitrary MPI applications. It leverages virtualization techniques combined with health monitoring and load-based migration. We exploit Xen's live migration mechanism for a guest operating system (OS) to migrate an MPI task from a health-deteriorating node to a healthy one without stopping the MPI task during most of the migration. Our proactive FT daemon orchestrates the tasks of health monitoring, load determination and initiation of guest OS migration. Experimental results demonstrate that live migration hides migration costs and limits the overhead to only a few seconds making it an attractive approach to realize FT in HPC systems. Overall, our enhancements make proactive FT a valuable asset for long-running MPI application that is complementary to reactive FT using full checkpoint/restart schemes since checkpoint frequencies can be reduced as fewer unanticipated failures are encountered. In the context of OS virtualization, we believe that this is the first comprehensive study of proactive fault tolerance where live migration is actually triggered by health monitoring.

Abstract-Faults have become the norm rather than the exception for high-end computing on clusters with 10s/100s of thousands of cores. Exacerbating this situation, some of these faults remain undetected, manifesting themselves as silent errors that corrupt memory while applications continue to operate and report incorrect results. This paper studies the potential for redundancy to both detect and correct soft errors in MPI message-passing applications. Our study investigates the challenges inherent to detecting soft errors within MPI application while providing transparent MPI redundancy. By assuming a model wherein corruption in application data manifests itself by producing differing MPI message data between replicas, we study the best suited protocols for detecting and correcting MPI data that is the result of corruption.To experimentally validate our proposed detection and correction protocols, we introduce RedMPI, an MPI library which resides in the MPI profiling layer. RedMPI is capable of both online detection and correction of soft errors that occur in MPI applications without requiring any modifications to the application source by utilizing either double or triple redundancy.Our results indicate that our most efficient consistency protocol can successfully protect applications experiencing even high rates of silent data corruption with runtime overheads between 0% and 30% as compared to unprotected applications without redundancy.Using our fault injector within RedMPI, we observe that even a single soft error can have profound effects on running applications, causing a cascading pattern of corruption in most cases causes that spreads to all other processes. RedMPI's protection has been shown to successfully mitigate the effects of soft errors while allowing applications to complete with correct results even in the face of errors.

We present here a report produced by a workshop on 'Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach.The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.

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