Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Evaluating Performance and Reliability of Selective Redundant Multithreading for Gpgpu Applications(CEUR-WS, 2021) Kaya,E.; Karadaş,O.F.; Öz,I.With the widespread use of GPU architectures in general-purpose computations, evaluating the soft error vulnerability of GPGPU programs and employing efficient fault tolerance techniques for more reliable execution becomes more prominent. Performing full redundancy, based on the redundant execution of the complete program, results in resource consumption and performance loss as well as energy inefficiency. Therefore, determining the most error-prone regions of the target program code and replicating only those parts maintains both high performance and acceptable error rates. In this study, we propose a partial redundant multithreading mechanism based on the soft error vulnerability of GPGPU applications and perform a trade-off analysis between performance and reliability. Firstly, we perform fault injection experiments to evaluate the SDC rates for each kernel function. Then, based on the outcome of the fault injection experiments, we determine the kernel function to-be-replicated. According to the pragmas denoting the redundancy points in the source code, our custom LLVM pass generates the code that enables the redundant execution for the specified code region. We evaluate both the reliability and performance of the redundant execution scenarios measuring the execution time of the redundant program generated by our compiler-managed redundancy technique. Our results demonstrate that protecting only the most vulnerable kernel functions enables high reliability without hurting the performance significantly. © 2021 The Authors.Article Citation - Scopus: 5Regional Soft Error Vulnerability and Error Propagation Analysis for Gpgpu Applications(Springer, 2022) Öz, I.; Karadaş, Ö.F.The wide use of GPUs for general-purpose computations as well as graphics programs makes soft errors a critical concern. Evaluating the soft error vulnerability of GPGPU programs and employing efficient fault tolerance techniques for more reliable execution become more important. Protecting only the most error-sensitive program regions maintains an acceptable reliability level by eliminating the large performance overheads due to redundant operations. Therefore, fine-grained regional soft error vulnerability analysis is crucial for the systems targeting both performance and reliability. In this work, we present a regional fault injection framework and perform a detailed error propagation analysis to evaluate the soft error vulnerability of GPGPU applications. We evaluate both intra-kernel and inter-kernel vulnerabilities for a set of programs and quantify the severity of the data corruptions by considering metrics other than SDC rates. Our experimental study demonstrates that the code regions inside GPGPU programs exhibit different characteristics in terms of soft error vulnerability and the soft errors corrupting the variables propagate into the program output in several ways. We present the potential impact of our analysis by discussing the usage scenarios after we compile our observations acquired from our empirical work. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Article Citation - WoS: 4Citation - Scopus: 4Predicting the Soft Error Vulnerability of Parallel Applications Using Machine Learning(Springer, 2021) Öz, Işıl; Arslan, SanemWith the widespread use of the multicore systems having smaller transistor sizes, soft errors become an important issue for parallel program execution. Fault injection is a prevalent method to quantify the soft error rates of the applications. However, it is very time consuming to perform detailed fault injection experiments. Therefore, prediction-based techniques have been proposed to evaluate the soft error vulnerability in a faster way. In this work, we present a soft error vulnerability prediction approach for parallel applications using machine learning algorithms. We define a set of features including thread communication, data sharing, parallel programming, and performance characteristics; and train our models based on three ML algorithms. This study uses the parallel programming features, as well as the combination of all features for the first time in vulnerability prediction of parallel programs. We propose two models for the soft error vulnerability prediction: (1) A regression model with rigorous feature selection analysis that estimates correct execution rates, (2) A novel classification model that predicts the vulnerability level of the target programs. We get maximum prediction accuracy rate of 73.2% for the regression-based model, and achieve 89% F-score for our classification model.Article Citation - WoS: 5Citation - Scopus: 5Regional Soft Error Vulnerability and Error Propagation Analysis for Gpgpu Applications(Springer, 2021) Öz, Işıl; Karadaş, Ömer FarukThe wide use of GPUs for general-purpose computations as well as graphics programs makes soft errors a critical concern. Evaluating the soft error vulnerability of GPGPU programs and employing efficient fault tolerance techniques for more reliable execution become more important. Protecting only the most error-sensitive program regions maintains an acceptable reliability level by eliminating the large performance overheads due to redundant operations. Therefore, fine-grained regional soft error vulnerability analysis is crucial for the systems targeting both performance and reliability. In this work, we present a regional fault injection framework and perform a detailed error propagation analysis to evaluate the soft error vulnerability of GPGPU applications. We evaluate both intra-kernel and inter-kernel vulnerabilities for a set of programs and quantify the severity of the data corruptions by considering metrics other than SDC rates. Our experimental study demonstrates that the code regions inside GPGPU programs exhibit different characteristics in terms of soft error vulnerability and the soft errors corrupting the variables propagate into the program output in several ways. We present the potential impact of our analysis by discussing the usage scenarios after we compile our observations acquired from our empirical work.Article Citation - WoS: 1Citation - Scopus: 1A User-Assisted Thread-Level Vulnerability Assessment Tool(Wiley, 2019) Öz, Işıl; Topçuoğlu, Haluk Rahmi; Tosun, OğuzThe system reliability becomes a critical concern in modern architectures with the scale down of circuits. To deal with soft errors, the replication of system resources has been used at both hardware and software levels. Since the redundancy causes performance degradation, it is required to explore partial redundancy techniques that replicate the most vulnerable parts of the code. The redundancy level of user applications depends on user preferences and may be different for the users with different requirements. In this work, we propose a user-assisted reliability assessment tool based on critical thread analysis for redundancy in parallel architectures. Our analysis evaluates the application threads of a parallel program by considering their criticality in the execution and selects the most critical thread or threads to be replicated. Moreover, we extend our analysis by exploring critical regions of individual threads and execute redundantly only those regions to reduce redundancy overhead. Our experimental evaluation indicates that the replication of the most critical thread improves the system reliability more (up to 10% for blackscholes application) than the replication of any other thread. The partial thread replication based on critical region analysis also reduces the vulnerability of the system by considering a fine-grained approach.