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Functional Testing with STLs: A Step Towards Reliable RISC-V-based HPC Commodity Clusters

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High Performance Computing (ISC High Performance 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13999))

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Abstract

The reliability of High-Performance Computing (HPC) systems is an essential concern due to their massive size and the complexity of their operation. Thus, functional tests have been extensively used to monitor HPC systems and use software routines to verify the software stack’s operation, mainly focusing on high-level abstraction features. However, the miniaturization of transistor technologies and the increment of computational resources (to face the performance and computation capabilities of HPC systems for the exascale generation) impose new reliability challenges that involve the development of clever testing strategies considering the underlying hardware characteristics. Interestingly, resorting to open-hardware architectures (such as RISC-V-based platforms) in the HPC domain offers a unique opportunity to effectively combine traditional HPC functional testing techniques with the adoption of effective fine-grain hardware testing solutions, such as those based on the Software-Based Self-Test (SBST) strategy.

This work proposes the SBST strategy as an enhanced and complementary technique for functional testing of RISC-V platforms for HPC systems. The method provides fine-grain evaluations of the CPU cores, including quantitative information on the state of the CPU cores and the presence of faults. For the experiments, we resort to two RISC-V cores (RI5CY and ibex) to develop and verify the effectiveness of the SBST strategy. In total, we developed 11 STLs (SBST routines) showing that a considerable percentage of hardware faults (from about 82% and up to 90%) can be detected with minimal overhead, thus, allowing their use during empty time intervals or in combination with other in-field functional testing approaches for HPC clusters.

This work has been supported by the National Resilience and Recovery Plan (PNRR) through the National Center for HPC, Big Data and Quantum Computing.

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Authors and Affiliations

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

    Josie E. Rodriguez Condia, Nikolaos I. Deligiannis, Jacopo Sini, Riccardo Cantoro & Matteo Sonza Reorda

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  1. Josie E. Rodriguez Condia
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  2. Nikolaos I. Deligiannis
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  3. Jacopo Sini
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  4. Riccardo Cantoro
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  5. Matteo Sonza Reorda
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Correspondence to Josie E. Rodriguez Condia .

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Editors and Affiliations

  1. University of New Mexico, Albuquerque, NM, USA

    Amanda Bienz

  2. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  3. Université Paris-Saclay, Gif sur Yvette, France

    Marc Baboulin

  4. CERFACS, Toulouse, France

    Carola Kruse

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Condia, J.E.R., Deligiannis, N.I., Sini, J., Cantoro, R., Reorda, M.S. (2023). Functional Testing with STLs: A Step Towards Reliable RISC-V-based HPC Commodity Clusters. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham. https://doi.org/10.1007/978-3-031-40843-4_33

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  • DOI: https://doi.org/10.1007/978-3-031-40843-4_33

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