Configurable Detection of SDC-causing Errors in Programs
Abstract
Silent Data Corruption (SDC) is a serious reliability issue in many domains, including embedded systems. However, current protection techniques are brittle and do not allow programmers to trade off performance for SDC coverage. Further, many require tens of thousands of fault-injection experiments, which are highly time- and resource-intensive. In this article, we propose two empirical models, SDCTune and SDCAuto , to predict the SDC proneness of a program’s data. Both models are based on static and dynamic features of the program alone and do not require fault injections to be performed. The main difference between them is that SDCTune requires manual tuning while SDCAuto is completely automated, using machine-learning algorithms.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 28, 2017
- Source ID
- 10.1145/3014586
Entities
People
- Guanpeng Li
- Jude A. Rivers
- Karthik Pattabiraman
- Meeta S. Gupta
- Qining Lu
Organizations
- Defense Advanced Research Projects Agency
- Natural Sciences and Engineering Research Council
- University of British Columbia