Paper Info
Title
Comparative analysis of soft-error detection strategies: a case study with iterative methods
Abstract
ABSTRACTUndetected soft errors caused by transient bit flips can lead to silent data corruption (SDC), an undesirable outcome where invalid results pass for valid ones. This has motivated the design of soft error detectors to minimize SDCs. However, the detectors have been studied under different contexts, making comparative evaluation difficult. In this paper, we present the first comprehensive evaluation of four online soft error detection techniques in detecting the adverse impact of soft errors on iterative methods. We observe that, across five iterative methods, the detectors studied achieve high but not perfect detection rates. To understand the potential for improved detection, we evaluate a machine-learning based detector that takes as features that are the runtime features observed by the individual detectors to arrive at their conclusions. Our evaluation demonstrates improved but still far from perfect detection accuracy for the machine learning based detectors. This extensive evaluation demonstrates the need for designing error detectors to handle the evolutionary behavior exhibited by iterative solvers.
Year
DOI
Venue
2018
10.1145/3203217.3203240
CF
Field
DocType
Citations 
Silent data corruption,Soft error,Iterative method,Computer science,Real-time computing,Soft error detection,Computer engineering,Detector
Conference
2
PageRank 
References 
Authors
0.49
33
6
Name
Order
Citations
PageRank
Gokcen Kestor114814.25
Burcu Ozcelik Mutlu241.88
Joseph Manzano3378.63
Omer Subasi4416.34
Osman Unsal516414.33
Sriram Krishnamoorthy6120286.68