PRISM: Predicting Resilience of GPU Applications Using Statistical Methods

Kalra, Charu; Previlon, Fritz; Li, Xiangyu; Rubin, Norman; Kaeli, David

doi:10.1109/sc.2018.00072

Cited by 22 publications

(10 citation statements)

References 33 publications

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“…Recently, there have been some works that propose machine-learning techniques to predict soft errors in GPU programs [23,37]. The proposed prediction frameworks utilize the program characteristics as input features in their ML models, similar to works based on failure prediction for HPC systems [21].…”

Section: Correlating Code Characteristics and Fault Behaviormentioning

confidence: 99%

Regional soft error vulnerability and error propagation analysis for GPGPU applications

Karadaş

2021

J Supercomput

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The wide use of GPUs for general-purpose computations as well as graphics programs makes soft errors a critical concern. Evaluating the soft error vulnerability of GPGPU programs and employing efficient fault tolerance techniques for more reliable execution become more important. Protecting only the most error-sensitive program regions maintains an acceptable reliability level by eliminating the large performance overheads due to redundant operations. Therefore, fine-grained regional soft error vulnerability analysis is crucial for the systems targeting both performance and reliability. In this work, we present a regional fault injection framework and perform a detailed error propagation analysis to evaluate the soft error vulnerability of GPGPU applications. We evaluate both intra-kernel and inter-kernel vulnerabilities for a set of programs and quantify the severity of the data corruptions by considering metrics other than SDC rates. Our experimental study demonstrates that the code regions inside GPGPU programs exhibit different characteristics in terms of soft error vulnerability and the soft errors corrupting the variables propagate into the program output in several ways. We present the potential impact of our analysis by discussing the usage scenarios after we compile our observations acquired from our empirical work.

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Section: Correlating Code Characteristics and Fault Behaviormentioning

confidence: 99%

Regional soft error vulnerability and error propagation analysis for GPGPU applications

Karadaş

2021

J Supercomput

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“…Recently, there have been many efforts to utilize machine learning [13], [35], [36], [37] to address resilience problems. IPAS [37] uses machine learning to decide on instructions that will likely to lead to corruption and duplicates them.…”

Section: Related Workmentioning

confidence: 99%

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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“…Minotaur can potentially be applicable to other hardware platforms as well. Although this work focuses on CPUs, recent resiliency analyses on GPUs [55,62,83], for example, can potentially benefit from the concepts of Minotaur to improve runtime and/or accuracy. Approximate computing: Many techniques have been proposed that leverage approximate computing at the level of software [8,11,23,71,85,100,106,110,114,121,125,127], programming languages [15,20,74,86,87,101,102] and hardware [5,11,16,33,42,46,53,56,77,103,105,112,126,128,133] for improved performance, energy, or reliability.…”

Section: Related Workmentioning

confidence: 99%

Minotaur

Mahmoud

Venkatagiri

Ahmed

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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With the end of conventional CMOS scaling, efficient resiliency solutions are needed to address the increased likelihood of hardware errors. Silent data corruptions (SDCs) are especially harmful because they can create unacceptable output without the user's knowledge. Several resiliency analysis techniques have been proposed to identify SDC-causing instructions, but they remain too slow for practical use and/or sacrifice accuracy to improve analysis speed. We develop Minotaur, a novel toolkit to improve the speed and accuracy of resiliency analysis. The key insight behind Minotaur is that modern resiliency analysis has many conceptual similarities to software testing; therefore, adapting techniques from the rich software testing literature can lead to principled and significant improvements in resiliency analysis. Minotaur identifies and adapts four concepts from software testing: 1) it introduces the concept of input quality criteria for resiliency analysis and identifies PC coverage as a simple but effective criterion; 2) it creates (fast) minimized inputs from (slow) standard benchmark inputs, using the input quality criteria to assess the goodness of the created input; 3) it adapts the concept of test case prioritization to prioritize error injections and invoke early termination for a given instruction to speed up error-injection campaigns; and 4) it further adapts test case or input prioritization to accelerate SDC discovery across multiple inputs. We evaluate Minotaur by applying it to Approxilyzer, a state-of-the-art resiliency analysis tool. Minotaur's first three techniques speed up Approxilyzer's resiliency analysis by

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PRISM: Predicting Resilience of GPU Applications Using Statistical Methods

Cited by 22 publications

References 33 publications

Regional soft error vulnerability and error propagation analysis for GPGPU applications

Regional soft error vulnerability and error propagation analysis for GPGPU applications

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

Minotaur

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