Calculating Architectural Vulnerability Factors for Spatial Multi-Bit Transient Faults

Wilkening, Mark; Sridharan, Vilas; Li, Si; Previlon, Fritz; Gurumurthi, Sudhanva; Kaeli, David

doi:10.1109/micro.2014.15

Cited by 35 publications

(21 citation statements)

References 37 publications

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“…Using the AVF then requires to compute the occurrence rates and derating factors of multiple bit flips observed simultaneously [26], for every multi-bit fault pattern that can not be corrected by the ECC. Furthermore, those patterns are different for every ECC code.…”

Section: Linking Derating Factors To Program Outcomementioning

confidence: 99%

A Vulnerability Factor for ECC-protected Memory

Jaulmes

Moretó

Valero

et al. 2019

2019 IEEE 25th International Symposium on on-Line Testing and Robust System Design (IOLTS)

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Fault injection studies and vulnerability analyses have been used to estimate the reliability of data structures in memory. We survey these metrics and look at their adequacy to describe the data stored in ECC-protected memory. We also introduce FEA, a new metric improving on the memory derating factor by ignoring a class of false errors. We measure all metrics using simulations and compare them to the outcomes of injecting errors in real runs. This in-depth study reveals that FEA provides more accurate results than any state-of-the-art vulnerability metric. Furthermore, FEA gives an upper bound on the failure probability due to an error in memory, making this metric a tool of choice to quantify memory vulnerability. Finally, we show that ignoring these false errors reduces the failure rate on average by 12.75% and up to over 45%.

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Section: Linking Derating Factors To Program Outcomementioning

confidence: 99%

A Vulnerability Factor for ECC-protected Memory

Jaulmes

Moretó

Valero

et al. 2019

2019 IEEE 25th International Symposium on on-Line Testing and Robust System Design (IOLTS)

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“…AVF and PVF are the standard metrics to evaluate fault injection results at the hardware level or at the software level, respectively [36, 37]. AVF is the probability for a fault in an architectural state to propagate to the executed program output.…”

Section: Proposed Reliability Evaluation Metricsmentioning

confidence: 99%

Kernel and layer vulnerability factor to evaluate object detection reliability in GPUs

Santos

Carro

Rech

2018

IET Computers & Digital Techniques

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Video recognition applications running on Graphics Processing Unit are composed of heterogeneous software portions, such as kernels or layers for neural networks. The authors propose the concepts of kernel vulnerability factor (KVF) and layer vulnerability factor (LVF), which indicate the probability of faults in a kernel or layer to affect the computation. KVF and LVF indicate the high-level portions of code that are more likely, if corrupted, to impact the application's output. KVF and LVF restrict the architecture/program vulnerability factor analysis to specific portions of the algorithm, easing the criticality analysis and the implementation of selective hardening. We apply the proposed metrics to two Histogram of Oriented Gradients (HOG), and You Only Look Once (YOLO) benchmarks. We measure the KVF for HOG by using fault-injection at both the architectural level and high level. We propose for HOG an efficient selective hardening technique able to detect 85% of critical errors with an overhead in performance as low as 11.8%. For YOLO, we study the LVF with architectural-level fault-injection. We qualify the observed corrupted outputs, distinguishing between tolerable and critical errors. Then, we proposed a smart layer duplication that detects more than 90% of errors, with an overhead lower than 60%.

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“…Some recent works evaluate the AVF for various parallel algorithms executed on GPUs [17], [40], [43]. Fault injectors provide the user with access to only a limited set of GPU resources.…”

Section: Neutron Beam Test Experimental Setupmentioning

confidence: 99%

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

Oliveira

Pilla

Hanzich

et al. 2017

2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)

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Abstract-In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing (HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications' output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude.We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.

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Calculating Architectural Vulnerability Factors for Spatial Multi-Bit Transient Faults

Cited by 35 publications

References 37 publications

A Vulnerability Factor for ECC-protected Memory

A Vulnerability Factor for ECC-protected Memory

Kernel and layer vulnerability factor to evaluate object detection reliability in GPUs

Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators

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