Topçu, Burak

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https://hdl.handle.net/11147/15893
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Main Affiliation
01. Izmir Institute of Technology
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External
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Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
1
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
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Scholarly Output

3

Articles

1

Views / Downloads

1649/676

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

1

Scopus Citation Count

2

Patents

0

Projects

0

WoS Citations per Publication

0.33

Scopus Citations per Publication

0.67

Open Access Source

2

Supervised Theses

1

JournalCount
Journal of Supercomputing1
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Scopus Quartile Distribution

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Scholarly Output Search Results

Now showing 1 - 3 of 3
  • 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; Topçu, Burak; Öz, Işıl; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of Engineering
    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.
  • Master Thesis
    Evaluating Impacts of Micro-Architectural Metrics on Error Resilience and Performance of General Purpose Gpu Applications
    (01. Izmir Institute of Technology, 2023) Topçu, Burak; Öz, Işıl; Öz, Işıl; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of Engineering
    Rapidly growing data processing tasks require powerful and energy-efficient heterogeneous computing systems, and GPUs take on a significant mission for those systems in accelerating heavy workloads by executing multiple parallel tasks concurrently. Increasing architectural complexity and widening employment of GPUs bring error resiliency concerns for safety-critical applications. Furthermore, approaches that enhance performance and reduce energy dissipation handle error resiliency on GPUs through approximate computing solutions. Evaluating error resiliency in terms of either identifying error proneness of a system or investigating approximations without much disturbing the output necessities robust knowledge about the execution of a program on a device. In this thesis, we develop a runtime performance and power monitoring tool visualizing the execution with detailed micro-architectural metrics. By utilizing the tool, we acquire several fundamental understandings about runtime performance bottlenecks and how perturbations affect output quality. Afterward, we propose a framework predicting fault vulnerability for error-resilient GPU applications. The framework can accurately estimate error tolerance and saves from analyzing the fault occurrence probability requiring significant effort. Depending on the performance bottlenecks observed with the tool and the error propagation gained during prediction experiments, we introduce a hardware-based approximation computing approach targeting to improve the performance and power of GPU programs, especially memory-bound ones. The approximation method, which resolves memory utilization bottlenecks at runtime, enhances performance by 1.49× (up to 2.1×) and diminishes energy consumption by 28.4% (up to 52.6%) while maintaining the accuracy on the output above 98%.
  • Article
    Soft Error Vulnerability Prediction of Gpgpu Applications
    (Springer, 2022) Topçu, Burak; Öz, Işıl; Öz, Işıl; Topçu, Burak; 01. Izmir Institute of Technology; 03.04. Department of Computer Engineering; 03. Faculty of Engineering
    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