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Evaluating low-level software-based hardening techniques for configurable GPU architectures

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Abstract

The high processing power of GPUs makes them attractive for safety-critical applications, where transient effects are a major concern, and resilience must be enforced without compromising performance. Configurable softcore GPUs are a recent technology that allows detailed reliability assessment capable of bringing directions to the design of reliable GPU applications. This work investigates the reliability of the register files and the pipeline of a softcore GPU under radiation-induced faults. It proposes software-based fault tolerance techniques to mitigate errors. Faults are simulated at the register transfer level in four case-study algorithms, and the Architectural Vulnerability Factor (AVF) and Mean Workload to Failure (MWTF) are checked over different GPU configurations. Results indicate that software-based techniques efficiently reduce AVF. In terms of MWTF, results show that the best cases depend on an optimized balance between GPU configuration, application runtime, and AVF.

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Acknowledgements

This work has been partially supported by the European Commission through the Horizon 2020 RESCUE-ETN project under grant 722325, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and Fundação de Amparo à pesquisa do Estado do RS (FAPERGS).

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Authors and Affiliations

  1. Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil

    Marcio M. Goncalves & Jose Rodrigo Azambuja

  2. Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Turin, Italy

    Josie E. Rodriguez Condia, Matteo Sonza Reorda & Luca Sterpone

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  1. Marcio M. Goncalves
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  2. Josie E. Rodriguez Condia
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  3. Matteo Sonza Reorda
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  4. Luca Sterpone
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  5. Jose Rodrigo Azambuja
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Correspondence to Marcio M. Goncalves.

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Goncalves, M.M., Condia, J.E.R., Reorda, M.S. et al. Evaluating low-level software-based hardening techniques for configurable GPU architectures. J Supercomput 78, 8081–8105 (2022). https://doi.org/10.1007/s11227-021-04154-z

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