Conference proceeding
Characterizing Runtime Performance Variation in Error Detection by Duplicating Instructions
2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp.730-741
10/09/2023
DOI: 10.1109/ISSRE59848.2023.00043
Abstract
Soft error rate has been increasing due to the shrinking size of transistors, leading to an elevated risk of catastrophic failures in modern computer systems. Error detection by duplicating instructions (EDDI) is a software-based technique to mitigate soft errors with a low runtime performance overhead and has been widely adopted in many safety- and mission-critical real-time systems such as space applications. However, these systems are commonly sensitive to runtime performance overheads the protection techniques incur. Few studies have investigated the performance of EDDI across various system designs and operational parameters, hence lacking a complete understanding in the literature. In this paper, we conduct comprehensive experiments to study the variation of EDDI runtime performance overhead and characterize the root causes. We find that there exist significant variations in performance overheads of EDDI, due to a few architectural and program-level factors. Based on the findings, we propose two practical techniques FuzzyB and Celer: FuzzyB uses an input searching technique to bound EDDI runtime performance overhead across different inputs for a given program; while Celer reduces EDDI run-time performance overheads using compiler transformation (by 25.08% reduction).
Details
- Title: Subtitle
- Characterizing Runtime Performance Variation in Error Detection by Duplicating Instructions
- Creators
- Yafan Huang - University of IowaZhengyang He - University of IowaLingda Li - Brookhaven National LaboratoryGuanpeng Li - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE), pp.730-741
- Publisher
- IEEE
- DOI
- 10.1109/ISSRE59848.2023.00043
- eISSN
- 2332-6549
- Language
- English
- Date published
- 10/09/2023
- Academic Unit
- Computer Science
- Record Identifier
- 9984507156302771
Metrics
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