Title
Versatile Prediction And Fast Estimation Of Architectural Vulnerability Factor From Processor Performance Metrics
Abstract
The shrinking processor feature size, lower threshold voltage and increasing clock frequency make modern processors highly vulnerable to transient faults. Architectural Vulnerability Factor (AVF) reflects the possibility that a transient fault eventually causes a visible error in the program output, and it indicates a system's susceptibility to transient faults. Therefore, the awareness of the AVF especially at early design stage is greatly helpful to achieve a trade-off between system performance and reliability. However, tracking the AVF during program execution is extremely costly, which makes accurate AVF prediction extraordinarily attractive to computer architects.In this paper, we propose to use Boosted Regression Trees, a nonparametric tree-based predictive modeling scheme, to identify the correlation across workloads, execution phases and processor configurations between a key processor structure's AVF and various performance metrics. The proposed method not only makes an accurate prediction but quantitatively illustrates individual performance variable's importance to the AVF Moreover, to reduce the prediction complexity, we also utilize a technique named Patient Rule Induction Method to extract some simple selecting rules on important metrics. Applying these rules during run time can fast identify execution intervals with a relatively high AVF.
Year
DOI
Venue
2009
10.1109/HPCA.2009.4798244
HPCA-15 2009: FIFTEENTH INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE, PROCEEDINGS
Keywords
Field
DocType
measurement,threshold voltage,benchmark testing,system performance,predictive models,regression analysis,prediction model,regression tree
Regression,Regression analysis,Computer science,Parallel computing,Real-time computing,Nonparametric statistics,Rule induction,Transient analysis,Clock rate,Benchmark (computing),Vulnerability
Conference
ISSN
Citations 
PageRank 
1530-0897
34
1.11
References 
Authors
18
3
Name
Order
Citations
PageRank
Lide Duan19410.82
Bin Li21209.22
Peng Lu312617.62