Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

Browse

Filters

20222

Settings

Subject: search.filters.subject.Computer graphics

Search Results

Now showing 1 - 2 of 2
  • Article
    Soft Error Vulnerability Prediction of Gpgpu Applications
    (Springer, 2022) Topçu, Burak; Öz, Işıl
    As graphics processing units (GPUs) evolve to offer high performance for general-purpose computations in addition to inherently fault-tolerant graphics applications, soft error reliability becomes a significant concern. Fault injection provides a method of evaluating the soft error vulnerability of target programs. Since performing fault injection experiments for complex GPU hardware structures takes impractical times, the prediction-based techniques to evaluate the soft error vulnerability of general-purpose GPU (GPGPU) programs based on metrics from different domains get crucial for both HPC developers and GPU vendors. In this work, we propose machine learning (ML)-based prediction frameworks for the soft error vulnerability evaluation of GPGPU programs. We consider program characteristics, hardware usage and performance metrics collected from the simulation and the profiling tools. While we utilize regression models to predict the masked fault rates, we build classification models to specify the vulnerability level of the GPGPU programs based on their silent data corruption (SDC) and crash rates. Our prediction models achieve maximum prediction accuracy rates of 95.9, 88.46, and 85.7% for masked fault rates, SDCs, and crashes, respectively
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Predicting the Soft Error Vulnerability of Gpgpu Applications
    (Institute of Electrical and Electronics Engineers Inc., 2022) Topçu, Burak; Öz, Işıl
    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.