Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12232
Title: Predicting the soft error vulnerability of GPGPU applications
Authors: Topçu, Burak
Öz, Işıl
01. Izmir Institute of Technology
01. Izmir Institute of Technology
Keywords: Computer graphics
Computer graphics equipment
Soft error
Radiation hardening
Issue Date: 2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: As Graphics Processing Units (GPUs) have evolved to deliver performance increases for general-purpose computations as well as graphics and multimedia applications, soft error reliability becomes an important concern. The soft error vulnerability of the applications is evaluated via fault injection experiments. Since performing fault injection takes impractical times to cover the fault locations in complex GPU hardware structures, prediction-based techniques have been proposed to evaluate the soft error vulnerability of General-Purpose GPU (GPGPU) programs based on the hardware performance characteristics.In this work, we propose ML-based prediction models for the soft error vulnerability evaluation of GPGPU programs. We consider both program characteristics and hardware performance metrics collected from either the simulation or the profiling tools. While we utilize regression models for the prediction of the masked fault rates, we build classification models to specify the vulnerability level of the programs based on their silent data corruption (SDC) and crash rates. Our prediction models achieve maximum prediction accuracy rates of 96.6%, 82.6%, and 87% for masked fault rates, SDCs, and crashes, respectively.
Description: This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK), Grant No: 119E011.
URI: https://doi.org/10.1109/PDP55904.2022.00025
https://hdl.handle.net/11147/12232
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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