Conference proceeding
A Tale of Two Injectors: End-to-End Comparison of IR-Level and Assembly-Level Fault Injection
2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), Vol.2019-, pp.151-162
10/2019
DOI: 10.1109/ISSRE.2019.00024
Abstract
Fault injection (FI) is a commonly used experimental technique to evaluate the resilience of software techniques for tolerating hardware faults. Software-implemented FI can be performed at different levels of abstraction in the system stack; FI performed at the compiler's intermediate representation (IR) level has the advantage that it is closer to the program being evaluated and is hence easier to derive insights from for the design of software fault-tolerance mechanisms. Unfortunately, it is not clear how accurate IR-level FI is vis-a-vis FI performed at the assembly code level, and prior work has presented contradictory findings. In this paper, we perform an analysis of said prior work, find an inconsistency in the FI methodology used in one study, and show that it results in a flawed comparison between IR-level and assembly-level FI. We further confirm this finding by performing a comprehensive evaluation of the accuracy of IR-level FI across a range of benchmark programs and compiler optimization levels. Our results show that IR-level FI is as accurate as assembly-level FI for silent data corruptions (SDCs) across different benchmarks and optimization levels.
Details
- Title: Subtitle
- A Tale of Two Injectors: End-to-End Comparison of IR-Level and Assembly-Level Fault Injection
- Creators
- Lucas Palazzi - University of British ColumbiaGuanpeng Li - University of British ColumbiaBo Fang - University of British ColumbiaKarthik Pattabiraman - University of British Columbia
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), Vol.2019-, pp.151-162
- Publisher
- IEEE
- DOI
- 10.1109/ISSRE.2019.00024
- ISSN
- 1071-9458
- eISSN
- 2332-6549
- Language
- English
- Date published
- 10/2019
- Academic Unit
- Computer Science
- Record Identifier
- 9984259421202771
Metrics
3 Record Views