Exploring the capabilities of support vector machines in detecting silent data corruptions

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo; Balaprakash, Prasanna; Unsal, Osman; Labarta, Jesus; Cristal, Adrian; Krishnamoorthy, Sriram; Cappello, Franck

doi:10.1016/J.SUSCOM.2018.01.004

Title: Exploring the capabilities of support vector machines in detecting silent data corruptions

Abstract

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.

Authors:

Subasi, Omer ^[1]; Di, Sheng ^[2]; Bautista-Gomez, Leonardo ^[3]; Balaprakash, Prasanna ^[2]; Unsal, Osman ^[3]; Labarta, Jesus ^[3]; Cristal, Adrian ^[4]; Krishnamoorthy, Sriram ^[1]; Cappello, Franck ^[2]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Barcelona Supercomputing Center (Spain)
Barcelona Supercomputing Center (Spain); Spanish National Research Council (CSIC), Madrid (Spain). IIIA - Artificial Intelligence Research Inst.

Publication Date:: Thu Feb 01 00:00:00 EST 2018

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:: USDOE; National Science Foundation (NSF); European Union (EU)

OSTI Identifier:: 1422782

Report Number(s):: PNNL-SA-131767
Journal ID: ISSN 2210-5379; PII: S2210537917300896

Grant/Contract Number:: 1619253; AC05-76RL01830; AC02-06CH11357; TIN2015-65316-P

Resource Type:: Accepted Manuscript

Journal Name:: Sustainable Computing

Additional Journal Information:: Journal Volume: 19; Journal ID: ISSN 2210-5379

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Silent data corruptions; Support vector machines; HPC applications

Citation Formats


                    Subasi, Omer, Di, Sheng, Bautista-Gomez, Leonardo, Balaprakash, Prasanna, Unsal, Osman, Labarta, Jesus, Cristal, Adrian, Krishnamoorthy, Sriram, and Cappello, Franck. Exploring the capabilities of support vector machines in detecting silent data corruptions.  United States: N. p., 2018. 
Web.  doi:10.1016/J.SUSCOM.2018.01.004.

Copy to clipboard


                    Subasi, Omer, Di, Sheng, Bautista-Gomez, Leonardo, Balaprakash, Prasanna, Unsal, Osman, Labarta, Jesus, Cristal, Adrian, Krishnamoorthy, Sriram, & Cappello, Franck. Exploring the capabilities of support vector machines in detecting silent data corruptions.  United States.  https://doi.org/10.1016/J.SUSCOM.2018.01.004

Copy to clipboard


                    Subasi, Omer, Di, Sheng, Bautista-Gomez, Leonardo, Balaprakash, Prasanna, Unsal, Osman, Labarta, Jesus, Cristal, Adrian, Krishnamoorthy, Sriram, and Cappello, Franck. Thu .  
"Exploring the capabilities of support vector machines in detecting silent data corruptions".  United States.  https://doi.org/10.1016/J.SUSCOM.2018.01.004.  https://www.osti.gov/servlets/purl/1422782.

Copy to clipboard


                    
@article{osti_1422782,

  title        = {Exploring the capabilities of support vector machines in detecting silent data corruptions},

  author       = {Subasi, Omer and Di, Sheng and Bautista-Gomez, Leonardo and Balaprakash, Prasanna and Unsal, Osman and Labarta, Jesus and Cristal, Adrian and Krishnamoorthy, Sriram and Cappello, Franck},

  abstractNote = {As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based on different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.},

  doi          = {10.1016/J.SUSCOM.2018.01.004},

  journal      = {Sustainable Computing},

  number       = ,

  volume       = 19,

  place        = {United States},

  year         = {Thu Feb 01 00:00:00 EST 2018},

  month        = {Thu Feb 01 00:00:00 EST 2018}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1016/J.SUSCOM.2018.01.004

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 9 works

Citation information provided by
Web of Science

Figures / Tables:

Fig. 1: SVM classification compared with other linear classifiers.

All figures and tables (18 total)

Save / Share:

Export Metadata

Save to My Library

Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

Similar Records in DOE PAGES and OSTI.GOV collections:

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

Conference Subasi, Omer ; Di, Sheng ; Bautista-Gomez, Leonardo ; ...

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data pointmore »« less
https://doi.org/10.1109/CCGrid.2016.33
Adaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications

Journal Article Di, Sheng ; Cappello, Franck - IEEE Transactions on Parallel and Distributed Systems

For exascale HPC applications, silent data corruption (SDC) is one of the most dangerous problems because there is no indication that there are errors during the execution. We propose an adaptive impact-driven method that can detect SDCs dynamically. The key contributions are threefold. (1) We carefully characterize 18 real-world HPC applications and discuss the runtime data features, as well as the impact of the SDCs on their execution results. (2) We propose an impact-driven detection model that does not blindly improve the prediction accuracy, but instead detects only influential SDCs to guarantee user-acceptable execution results. (3) Our solution can adaptmore »« less
https://doi.org/10.1109/TPDS.2016.2517639
Toward General Software Level Silent Data Corruption Detection for Parallel Applications

Journal Article Berrocal, Eduardo ; Bautista-Gomez, Leonardo ; Di, Sheng ; ... - IEEE Transactions on Parallel and Distributed Systems

Silent data corruption (SDC) poses a great challenge for high-performance computing (HPC) applications as we move to extreme-scale systems. Mechanisms have been proposed that are able to detect SDC in HPC applications by using the peculiarities of the data (more specifically, its “smoothness” in time and space) to make predictions. However, these data-analytic solutions are still far from fully protecting applications to a level comparable with more expensive solutions such as full replication. In this work, we propose partial replication to overcome this limitation. More specifically, we have observed that not all processes of an MPI application experience the samemore »« less
Cited by 11
https://doi.org/10.1109/TPDS.2017.2735971

Full Text Available
MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection

Conference Subasi, Omer ; Di, Sheng ; Balaprakash, Prasanna ; ...

Future HPC systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs are rather harmful and dangerous in that they cannot be detected by hardware. We propose an online machine-learning based silent data corruption detection framework (abbreviated as MACORD) for detecting SDCs in HPC applications. In particular, we comprehensively investigate the prediction ability of a multitude of machine-learning algorithms in our study, and enable the detector to automatically selectmore »« less
https://doi.org/10.1109/CLUSTER.2017.128
An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

Conference Di, Sheng ; Berrocal, Eduardo ; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore »« less
https://doi.org/10.1109/CCGrid.2015.17

Similar Records