IPAS: intelligent protection against silent output corruption in scientific applications

Laguna, Ignacio; Schulz, Martin; Richards, David F.; Calhoun, Jon C.; Olson, Luke N.

doi:10.1145/2854038.2854059

Cited by 59 publications

(27 citation statements)

References 30 publications

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“…In the remaining cases, most control flow diverges within the injected loop structure, leaving some vector entries unmodified. To ensure correct execution of these loops, instruction duplication techniques such as IPAS [28] and FlipBack [36] or low-cost invariant checks [23], offer the ability to ensure correct control flow with minor overheads. An optimization to lower the overhead of algorithmic based SDC detection schemes is to assume static data -e.g., the matrix in a linear solver is sufficiently protected that it does not need explicit checks for consistency and leaving SDC checks to inspect dynamic data.…”

Section: Abnormalmentioning

confidence: 99%

“…To combat this, software based SDC detection schemes have been developed, with many leveraging application properties and heuristics [10,12,25]. Others have taken application agnostic approaches [5,6,8] or leverage various forms of redundancy [19,28,37].…”

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confidence: 99%

“…Prior work has explored how deviations from bit-flips in floatingpoint computation impact on convergence properties [16,17]. Other work has used corruption propagation in the development of lowlevel instruction based detection schemes that check invariants or create SDC detectors inside the compiler [23,38] or utilize code replication to detect corruption in instructions that are likely lead to and propagate corrupted state [18,27,28], and understanding long latency crashes [30,45]. This paper combines the latency of programmatic systems of corruption -e.g., segfaults, detection, control flow divergence, with corruption propagation in state variables to discuss the impact of detection latency on recovery options.…”

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confidence: 99%

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Towards a More Complete Understanding of SDC Propagation

Calhoun

Snir

Olson

et al. 2017

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

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With the rate of errors that can silently effect an application's state/output expected to increase on future HPC machines, numerous application-level detection and recovery schemes have been proposed. Recovery is more efficient when errors are contained and affect only part of the computation's state. Containment is usually achieved by verifying all information leaking out of a statically defined containment domain, which is an expensive procedure. Alternatively, error propagation can be analyzed to bound the domain that is affected by a detected error. This paper investigates how silent data corruption (SDC) due to soft errors propagates through three HPC applications: HPCCG, Jacobi, and CoMD. To allow for more detailed view of error propagation, the paper tracks propagation at the instruction and application variable level. The impact of detection latency on error propagation is shown along with an application's ability to recover. Finally, the impact of compiler optimizations are explored along with the impact of local problem size on error propagation.

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Section: Abnormalmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Towards a More Complete Understanding of SDC Propagation

Calhoun

Snir

Olson

et al. 2017

Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing

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“…There has been work at compiler level to selectively duplicate instructions that may cause user-visible errors [17,22]. Shoestring [17] uses the data flow and control flow graphs of programs to select vulnerable instructions for error detection by duplication.…”

Section: Related Workmentioning

confidence: 99%

A Runtime Heuristic to Selectively Replicate Tasks for Application-Specific Reliability Targets

Subaşi

Yalcin

Zyulkyarov

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-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.

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“…It is important to get an accurate picture of these proportions; for example, an application that experiences a large percentage of SDCs may require algorithmic error detection mechanisms, at the expense of runtime overhead. A concern in the HPC community is that a significant number of resilience studies have been based on this FI method [3,4,6,17,18,25,31,32,[34][35][36] (including our own work), which can potentially skew FI results and, in some cases, lead to incorrect conclusions. There has been research done in showing these inaccuracies.…”

Section: Introductionmentioning

confidence: 99%

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Georgakoudis

Laguna

Nikolopoulos

et al. 2017

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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Compiler-based fault injection (FI) has become a popular technique for resilience studies to understand the impact of soft errors in supercomputing systems. Compiler-based FI frameworks inject faults at a high intermediate-representation level. However, they are less accurate than machine code, binary-level FI because they lack access to all dynamic instructions, thus they fail to mimic certain fault manifestations. In this paper, we study the limitations of current practices in compiler-based FI and how they impact the interpretation of results in resilience studies.We propose REFINE, a novel framework that addresses these limitations, performing FI in a compiler backend. Our approach provides the portability and efficiency of compiler-based FI, while keeping accuracy comparable to binary-level FI methods. We demonstrate our approach in 14 HPC programs and show that, due to our unique design, its runtime overhead is significantly smaller than state-ofthe-art compiler-based FI frameworks, reducing the time for large FI experiments. CCS CONCEPTS• Computing methodologies → Simulation tools; Model verification and validation; • Software and its engineering → Compilers; • Hardware → Analysis and design of emerging devices and systems;

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IPAS: intelligent protection against silent output corruption in scientific applications

Cited by 59 publications

References 30 publications

Towards a More Complete Understanding of SDC Propagation

Towards a More Complete Understanding of SDC Propagation

A Runtime Heuristic to Selectively Replicate Tasks for Application-Specific Reliability Targets

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