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Soft error vulnerability prediction of GPGPU applications

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Abstract

As graphics processing units (GPUs) evolve to offer high performance for general-purpose computations in addition to inherently fault-tolerant graphics applications, soft error reliability becomes a significant concern. Fault injection provides a method of evaluating the soft error vulnerability of target programs. Since performing fault injection experiments for complex GPU hardware structures takes impractical times, the prediction-based techniques to evaluate the soft error vulnerability of general-purpose GPU (GPGPU) programs based on metrics from different domains get crucial for both HPC developers and GPU vendors. In this work, we propose machine learning (ML)-based prediction frameworks for the soft error vulnerability evaluation of GPGPU programs. We consider program characteristics, hardware usage and performance metrics collected from the simulation and the profiling tools. While we utilize regression models to predict the masked fault rates, we build classification models to specify the vulnerability level of the GPGPU programs based on their silent data corruption (SDC) and crash rates. Our prediction models achieve maximum prediction accuracy rates of 95.9, 88.46, and 85.7% for masked fault rates, SDCs, and crashes, respectively.

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Data availibility

We share all source codes related to our prediction environment, our configuration files, and collected metrics from both the profiler and the simulator in our GitHub repository https://github.com/topcuburak/FaultPredictionOnGPGPUs.

Notes

  1. https://github.com/topcuburak/FaultPredictionOnGPGPUs.

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Acknowledgements

This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK), Grant No: 119E011.

Funding

This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK), Grant No: 119E011.

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Authors and Affiliations

  1. Computer Engineering Department, Izmir Institute of Technology, Izmir, Turkey

    Burak Topçu & Işıl Öz

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  1. Burak Topçu
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  2. Işıl Öz
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Contributions

BT: run the experiments and wrote the main manuscript. IO: defined the methodology, analyzed the results, and contributed in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Işıl Öz.

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Topçu, B., Öz, I. Soft error vulnerability prediction of GPGPU applications. J Supercomput 79, 6965–6990 (2023). https://doi.org/10.1007/s11227-022-04933-2

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