Self-stabilizing iterative solvers

Sao, Piyush; Vuduc, Richard

doi:10.1145/2530268.2530272

Cited by 76 publications

(96 citation statements)

References 23 publications

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“…Benson, Schmit and Schreiber [32] compare the result of a higher-order scheme with that of a lower-order one to detect errors in the numerical analysis of ODEs and PDEs. Sao and Vuduc [33] investigate self-stabilizing corrections after error detection in the conjugate gradient method. Bridges et al [34] propose linear solvers to tolerant soft faults using selective reliability.…”

Section: Related Workmentioning

confidence: 99%

Resilient N-Body Tree Computations with Algorithm-Based Focused Recovery: Model and Performance Analysis

Cavelan

Fang

Chien

et al. 2017

Lecture Notes in Computer Science

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This paper presents a model and performance study for Algorithm-Based Focused Recovery (ABFR) applied to N-body computations, subject to latent errors. We make a detailed comparison with the classical Checkpoint/Restart (CR) approach. While the model applies to general frameworks, the performance study is limited to perfect binary trees, due to the inherent difficulty of the analysis. With ABFR, the crucial parameter is the detection interval, which bounds the error latency. We show that the detection interval has a dramatic impact on the overhead, and that optimally choosing its value leads to significant gains over the CR approach.

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Section: Related Workmentioning

confidence: 99%

Resilient N-Body Tree Computations with Algorithm-Based Focused Recovery: Model and Performance Analysis

Cavelan

Fang

Chien

et al. 2017

Lecture Notes in Computer Science

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“… We employ a tuned variant of our multi-threaded implementations of the CG method equipped with an SS recovery mechanism [7] to cope with silent data corruption introduced by unreliable hardware. Following the experiments in [7], the SS part is activated once every 10 iterations in the CG method, and must be performed on reliable cores.…”

Section: Energy Cost Of Reliabilitymentioning

confidence: 99%

“…Following the experiments in [7], the SS part is activated once every 10 iterations in the CG method, and must be performed on reliable cores. From the computational point of view, the major difference between an SS iteration and a "normal" CG iteration (baseline routine) is that the former performs two matrix-vector products instead of only one.…”

Section: Energy Cost Of Reliabilitymentioning

confidence: 99%

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Evaluating fault tolerance on asymmetric multicore systems‐on‐chip using iso‐metrics

Chalios

Nikolopoulos

Catalán³

et al. 2016

IET Computers & Digital Techniques

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The end of Dennard scaling has promoted low power consumption into a firstorder concern for computing systems. However, conventional power conservation schemes such as voltage and frequency scaling are reaching their limits when used in performance-constrained environments. New technologies are required to break the power wall while sustaining performance on future processors. Low-power embedded processors and near-threshold voltage computing (NTVC) have been proposed as viable solutions to tackle the power wall in future computing systems. Unfortunately, these technologies may also compromise per-core performance and, in the case of NTVC, xreliability. These limitations would make them unsuitable for HPC systems and datacenters. In order to demonstrate that emerging low-power processing technologies can effectively replace conventional technologies, this study relies on ARM's big.LITTLE processors as both an actual and emulation platform, and state-of-the-art implementations of the CG solver. For NTVC in particular, the paper describes how efficient algorithm-based fault tolerance schemes preserve the power and energy benefits of very low voltage operation.

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“…These methods can only detect an error but do not correct it. Self-stabilizing corrections after error detection in the conjugate gradient method are investigated by Sao and Vuduc [65]. Also, Heroux and Hoemmen [44] design a fault-tolerant GMRES capable of converging despite silent errors, and Bronevetsky and de Supinski [17] provide a comparative study of detection costs for iterative methods.…”

Section: Other Approachesmentioning

confidence: 99%

Fault-Tolerance Techniques for High-Performance Computing

2015

Computer Communications and Networks

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This report provides an introduction to resilience methods. The emphasis is on checkpointing, the de-facto standard technique for resilience in High Performance Computing. We present the main two protocols, namely coordinated checkpointing and hierarchical checkpointing. Then we introduce performance models and use them to assess the performance of theses protocols. We cover the Young/Daly formula for the optimal period and much more! Next we explain how the efficiency of checkpointing can be improved via fault prediction or replication. Then we move to application-specific methods, such as ABFT. We conclude the report by discussing techniques to cope with silent errors (or silent data corruption).This report is a slightly modified version of the first chapter of the monograph Fault tolerance techniques for high-performance computing edited by Thomas Herault and Yves Robert, and to be published by Springer Verlag.

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Self-stabilizing iterative solvers

Cited by 76 publications

References 23 publications

Resilient N-Body Tree Computations with Algorithm-Based Focused Recovery: Model and Performance Analysis

Resilient N-Body Tree Computations with Algorithm-Based Focused Recovery: Model and Performance Analysis

Evaluating fault tolerance on asymmetric multicore systems‐on‐chip using iso‐metrics

Fault-Tolerance Techniques for High-Performance Computing

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