Comparative analysis of soft-error detection strategies

Kestor, Gokcen; Mutlu, Burcu O.; Manzano, Joseph; Subaşi, Ömer; Ünsal, Osman; Krishnamoorthy, Sriram

doi:10.1145/3203217.3203240

Cited by 8 publications

(10 citation statements)

References 34 publications

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“…However, the detection rates and overheads were extremely conservative. The comparative analysis by Kestor et al [31] reports significant slowdown at a maximum of 1.4× for AID when snapshots were taken for every time step. We observed similar overheads when taking snapshots at every step.…”

Section: Soft Error Detectionmentioning

confidence: 99%

FPD etect

Das

Krishnamoorthy

Briggs

et al. 2020

ACM Trans. Archit. Code Optim.

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We present FPDetect, a low-overhead approach for detecting logical errors and soft errors affecting stencil computations without generating false positives. We develop an offline analysis that tightly estimates the number of floating-point bits preserved across stencil applications. This estimate rigorously bounds the values expected in the data space of the computation. Violations of this bound can be attributed with certainty to errors. FPDetect helps synthesize error detectors customized for user-specified levels of accuracy and coverage. FPDetect also enables overhead reduction techniques based on deploying these detectors coarsely in space and time. Experimental evaluations demonstrate the practicality of our approach. CCS Concepts: • Hardware → Error detection and error correction; • Computer systems organization → Reliability; • Software and its engineering → Software verification; • Mathematics of computing → Numerical analysis;

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Section: Soft Error Detectionmentioning

confidence: 99%

FPD etect

Das

Krishnamoorthy

Briggs

et al. 2020

ACM Trans. Archit. Code Optim.

Self Cite

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“…Our error injection framework instruments the source code to simulate an error. The framework decides on where to inject the error based on the inputs provided by the user (similar to our previous works [1], [2]). These inputs are: iteration number, statement number, vector name, position in the vector, list of bit positions to flip in the vector element.…”

Section: B Error Injection Mechanismmentioning

confidence: 99%

“…To be consistent in the rest of the section, we equivalently looked at the masked ratio, which is the fraction of error injections that result in a masked outcome. Note that these are complementary and one can directly derive one from the other 1 . Figure 3 plots the masked ratio computed using ground truth (y-axis) versus the masked ratio computed using various prediction strategies.…”

Section: B Evaluating Solver Vulnerabilitymentioning

confidence: 99%

“…With semiconductor device scaling and the need to limit power consumption to achieve exascale efficiency, soft errors are becoming a significant concern. Specifically, undetected soft errors can lead to application/system crash or even to silent data corruption [1].…”

Section: Introductionmentioning

confidence: 99%

“…However, soft error impact analysis has demonstrated that errors can have a wide variety of outcomes [2]. This has motivated the continued development of novel strategies that observe the execution progress of iterative methods to identify anomalies in their execution [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ground-Truth Prediction to Accelerate Soft-Error Impact Analysis for Iterative Methods

Mutlu

Kestor

Cristal

et al. 2019

2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC)

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Understanding the impact of soft errors on applications can be expensive. Often, it requires an extensive error injection campaign involving numerous runs of the full application in the presence of errors. In this paper, we present a novel approach to arrive at the ground truth-the true impact of an error on the final output-for iterative methods by observing a small number of iterations to learn deviations between normal and error-impacted execution. We develop a machine learning based predictor for three iterative methods to generate groundtruth results without running them to completion for every error injected. We demonstrate that this approach achieves greater accuracy than alternative prediction strategies, including three existing soft error detection strategies. We demonstrate the effectiveness of the ground truth prediction model in evaluating vulnerability and the effectiveness of soft error detection strategies in the context of iterative methods.

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Detection and correction of silent errors in the conjugate gradient algorithm

Meurant¹

2022

Numer Algor

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Comparative analysis of soft-error detection strategies

Cited by 8 publications

References 34 publications

FPD etect

FPD etect

Ground-Truth Prediction to Accelerate Soft-Error Impact Analysis for Iterative Methods

Detection and correction of silent errors in the conjugate gradient algorithm

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