Article

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Authors:

Ravishankar IyerAuthors Info & Claims

IPDPS '11: Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium

Pages 287 - 300

https://doi.org/10.1109/IPDPS.2011.36

Published: 16 May 2011 Publication History

Abstract

High performance and relatively low cost of GPU-based platforms provide an attractive alternative for general purpose high performance computing (HPC). However, the emerging HPC applications have usually stricter output cor-rectness requirements than typical GPU applications (i.e., 3D graphics). This paper first analyzes the error resiliency of GPGPU platforms using a fault injection tool we have devel-oped for commodity GPU devices. On average, 16-33% of in-jected faults cause silent data corruption (SDC) errors in the HPC programs executing on GPU. This SDC ratio is signifi-cantly higher than that measured in CPU programs (

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Santos FCarro LVella FRech P(2024)Assessing the Impact of Compiler Optimizations on GPUs ReliabilityACM Transactions on Architecture and Code Optimization10.1145/363824921:2(1-22)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3638249
Perez-Cerrolaza JAbella JKosmidis LCalderon ACazorla FFlores J(2022)GPU Devices for Safety-Critical Systems: A SurveyACM Computing Surveys10.1145/354952655:7(1-37)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3549526
Yang LNie BJog ASmirni E(2021)SUGARProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34473755:1(1-29)Online publication date: 22-Feb-2021
https://dl.acm.org/doi/10.1145/3447375
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IPDPS '11: Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium

May 2011

1285 pages

ISBN:9780769543857

Publisher

IEEE Computer Society

United States

Publication History

Published: 16 May 2011

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Santos FCarro LVella FRech P(2024)Assessing the Impact of Compiler Optimizations on GPUs ReliabilityACM Transactions on Architecture and Code Optimization10.1145/363824921:2(1-22)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3638249
Perez-Cerrolaza JAbella JKosmidis LCalderon ACazorla FFlores J(2022)GPU Devices for Safety-Critical Systems: A SurveyACM Computing Surveys10.1145/354952655:7(1-37)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3549526
Yang LNie BJog ASmirni E(2021)SUGARProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34473755:1(1-29)Online publication date: 22-Feb-2021
https://dl.acm.org/doi/10.1145/3447375
Krluku EGusev MZdraveski V(2019)Bi-Source Verification Against Silent Data Corruption in High Performance ComputingProceedings of the 9th Balkan Conference on Informatics10.1145/3351556.3351567(1-4)Online publication date: 26-Sep-2019
https://dl.acm.org/doi/10.1145/3351556.3351567
Bernholdt DElwasif WKartsaklis CLee SMintz T(2018)Programmer-guided reliability for extreme-scale applicationsInternational Journal of High Performance Computing Applications10.1177/109434201666762532:5(598-612)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1177/1094342016667625
Chen CEisenhauer GWolf MPande SZhao MChandra ARamakrishnan L(2018)LADRProceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3208040.3208043(156-167)Online publication date: 11-Jun-2018
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Nie BYang LJog ASmirni EOskin MInoue K(2018)Fault site pruning for practical reliability analysis of GPGPU applicationsProceedings of the 51st Annual IEEE/ACM International Symposium on Microarchitecture10.1109/MICRO.2018.00066(749-761)Online publication date: 20-Oct-2018
https://dl.acm.org/doi/10.1109/MICRO.2018.00066
Peña ABeltran VClauss CMoschny TGropp WBeckman PLi ZCazorla F(2017)Supporting automatic recovery in offloaded distributed programming models through MPI-3 techniquesProceedings of the International Conference on Supercomputing10.1145/3079079.3079093(1-10)Online publication date: 14-Jun-2017
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Ni XKale LWest J(2016)FlipBackProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3014904.3014943(1-12)Online publication date: 13-Nov-2016
https://dl.acm.org/doi/10.5555/3014904.3014943
Li GPattabiraman KCher CBose PWest J(2016)Understanding error propagation in GPGPU applicationsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3014904.3014932(1-12)Online publication date: 13-Nov-2016
https://dl.acm.org/doi/10.5555/3014904.3014932
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Evaluation of Rodinia Codes on Intel Xeon Phi

On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing

Vectorizing Unstructured Mesh Computations for Many-core Architectures

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