Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Community Detection for Large Graphs on GPUs With Unified Memory(Institute of Electrical and Electronics Engineers Inc., 2024) Öz, Işıl; Öz, Işıl; Oz, Isil; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyWhile GPUs accelerate applications from different domains with different characteristics, processing large datasets gets infeasible on target systems with limited device memory. Unified memory support makes it possible to work with data larger than available GPU memory. However, page migration overhead for executions with irregular memory access patterns, like graph processing workloads, induces severe performance degradation. While memory hints help to deal with page movements by keeping data in suitable memory spaces, coarse-grain configurations can still not avoid migrations for executions having diverse data structures. In this work, we target the state-of-the-art CUDA implementation of the Louvain community detection algorithm and evaluate the impacts of the fine-grained unified memory hints on the performance. Our experimental evaluation shows that memory hints configured for specific data structures reveal significant performance improvements and enable us to work efficiently with large graphs.Conference Object Citation - WoS: 1Citation - Scopus: 2Predicting the Soft Error Vulnerability of Gpgpu Applications(Institute of Electrical and Electronics Engineers Inc., 2022) Topçu, Burak; Öz, Işıl; Topçu, Burak; Öz, Işıl; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of EngineeringAs 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.