Understanding Soft Error Resiliency of Blue Gene/Q Compute Chip through Hardware Proton Irradiation and Software Fault Injection

Cher, Chen-Yong; Gupta, Manav; Bose, Pradip; Müller, K.P.

doi:10.1109/sc.2014.53

Cited by 27 publications

(14 citation statements)

References 16 publications

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“…These breakdowns are usually defined as hard faults , they are in general reproducible and, if unaddressed, bring programs to halt. Soft faults are instead usually caused by fluctuations in radiation that introduce spurious modifications in the program data in the form of bit flips and are usually non-reproducible (Cher et al, 2014). Faults are further distinguished into detected and corrected, detected and uncorrectable, and undetected ones.…”

Section: Resilience Methodologiesmentioning

confidence: 99%

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Benacchio

Bonaventura

Altenbernd

et al. 2021

The International Journal of High Performance Computing Applica

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Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

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Section: Resilience Methodologiesmentioning

confidence: 99%

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Benacchio

Bonaventura

Altenbernd

et al. 2021

The International Journal of High Performance Computing Applica

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“…Cher et al [12] use both proton irradiation and SFI to study the soft error resilience of BlueGene/Q. Proton bombardment shows that BG/Q has a mean time between correctable errors of 1.5 days validating the need for detection mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Experimental and analytical study of Xeon Phi reliability

Oliveira

Pilla

DeBardeleben

et al. 2017

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

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We present an in-depth analysis of transient faults effects on HPC applications in Intel Xeon Phi processors based on radiation experiments and high-level fault injection. Besides measuring the realistic error rates of Xeon Phi, we quantify Silent Data Corruption (SDCs) by correlating the distribution of corrupted elements in the output to the application's characteristics. We evaluate the benefits of imprecise computing for reducing the programs' error rate. For example, for HotSpot a 0.5% tolerance in the output value reduces the error rate by 85%.We inject different fault models to analyze the sensitivity of given applications. We show that portions of applications can be graded by different criticalities. For example, faults occurring in the middle of LUD execution, or in the Sort and Tree portions of CLAMR, are more critical than the remaining portions. Mitigation techniques can then be relaxed or hardened based on the criticality of the particular portions.

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“…Also, a largescale study [28] of DRAM errors on Google datacenters was done some years ago, showing that error rates are higher than previously reported. Of particular relevance are these largescale studies of DRAM errors in production systems [21], [29] with a full characterization of hard and soft errors in production systems. These studies are important milestones given the scale of the analysis (i.e., millions of DIMM days, hundreds of Terabyte-years).…”

Section: Related Workmentioning

confidence: 99%

Unprotected Computing: A Large-Scale Study of DRAM Raw Error Rate on a Supercomputer

Bautista-Gomez

Zyulkyarov

Unsal

et al. 2016

SC16: International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-Supercomputers offer new opportunities for scientific computing as they grow in size. However, their growth also poses new challenges. Resilience has been recognized as one of the most pressing issues to solve for extreme scale computing. Transistor scaling in the single-digit nanometer era and power constraints might dramatically increase the failure rate of next generation machines. DRAM errors have been analyzed in the past for different supercomputers but those studies are usually based on job scheduler logs and counters produced by hardwarelevel error correcting codes. Consequently, little is known about errors escaping hardware checks, which lead to silent data corruption. This work attempts to fill that gap by analyzing memory errors for over a year on a cluster with about 1000 nodes featuring low-power memory without error correction. The study gathered millions of events recording detailed information of thousands of memory errors, many of them corrupting multiple bits. Several factors are analyzed, such as temporal and spatial correlation between errors, but also the influence of temperature and even the position of the sun in the sky. The study showed that most multi-bit errors corrupted non-adjacent bits in the memory word and that most errors flipped memory bits from 1 to 0. In addition, we observed thousands of cases of multiple single-bit errors occurring simultaneously in different regions of the memory. These new observations would not be possible by simply analyzing error correction counters on classical systems. We propose several directions in which the findings of this study can help the design of more reliable systems in the future.

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Understanding Soft Error Resiliency of Blue Gene/Q Compute Chip through Hardware Proton Irradiation and Software Fault Injection

Cited by 27 publications

References 16 publications

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Experimental and analytical study of Xeon Phi reliability

Unprotected Computing: A Large-Scale Study of DRAM Raw Error Rate on a Supercomputer

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