Bozkuş, Zeki

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Profile URL
https://hdl.handle.net/20.500.12469/6964
Name Variants
Bozkuş, Zeki
Z.,Bozkuş
Z. Bozkuş
Zeki, Bozkuş
Bozkus, Zeki
Z.,Bozkus
Z. Bozkus
Zeki, Bozkus
Bozkus,Z.
Job Title
Doç. Dr.
Email Address
Zekı[email protected]
Main Affiliation
Computer Engineering
Status
Former Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

3

GOOD HEALTH AND WELL-BEING
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1

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Scholarly Output

29

Articles

7

Views / Downloads

122/3029

Supervised MSc Theses

9

Supervised PhD Theses

0

WoS Citation Count

106

Scopus Citation Count

182

WoS h-index

5

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

3.66

Scopus Citations per Publication

6.28

Open Access Source

22

Supervised Theses

9

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JournalCount
Journal of Parallel and Distributed Computing2
Frontiers in Neuroinformatics2
2013 High Capacity Optical Networks and Emerging/Enabling Technologies, HONET-CNS 2013 -- 10th International Conference on High Capacity Optical Networks and Emerging/Enabling Technologies, HONET-CNS 2013 -- 11 December 2013 through 13 December 2013 -- Famagusta -- 1028061
2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing1
2015 International Conference on Emerging Technologies (ICET)1
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Now showing 1 - 10 of 29
  • Thumbnail Image
    Article
    Optimizing Neuron Simulation Environment Using Remote Memory Access With Recursive Doubling on Distributed Memory Systems
    (Hindawi Ltd, 2016) Shehzad, Danish; Bozkuş, Zeki
    Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.
  • Thumbnail Image
    Correction
    Software for Brain Network Simulations: a Comparative Study (vol 11, 46, 2017)
    (Frontiers Media Sa, 2018) Tikidji-Hamburyan, Ruben A.; Narayana, Vikram; Bozkus, Zeki; El-Ghazawi, Tarek A.
    [No Abstract Available]
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    Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Hybrid Mpi Plus Upc Parallel Programming Paradigm on an Smp Cluster
    (TUBITAK Scientific & Technical Research Council Turkey, 2012) Bozkuş, Zeki
    The symmetric multiprocessing (SMP) cluster system which consists of shared memory nodes with several multicore central processing units connected to a high-speed network to form a distributed memory system is the most widely available hardware architecture for the high-performance computing community. Today the Message Passing Interface (MPI) is the most widely used parallel programming paradigm for SMP clusters in which the MPI provides programming both for an SMP node and among nodes simultaneously. However Unified Parallel C (UPC) is an emerging alternative that supports the partitioned global address space model that can be again employed within and across the nodes of a cluster. In this paper we describe a hybrid parallel programming paradigm that was designed to combine MPI and UPC programming models. This paradigm's objective is to mix the MPI's data locality control and scalability strengths with UPC's fine-grain parallelism and ease of programming to achieve multiple-level parallelism at the SMP cluster which itself has multilevel parallel architecture. Utilizing a proposed hybrid model and comparing MPI-only to UPC-only implementations this paper presents a detailed description of Cannon's algorithm benchmark application with performance results of a random-access benchmark and the Barnes-Hut N-Body simulation. Experiments indicate that the hybrid MPI+UPC model can significantly provide performance increases of up to double in comparison with UPC-only implementation and up to 20% increases in comparison to MPI-only implementation. Furthermore an optimization was achieved that improved the hybrid performance by an additional 20%.
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    Master Thesis
    Use of Machine Learning Techniques for Diagnosis of Thyroid Glang Disorder
    (Kadir Has Üniversitesi, 2016) Mofek, İzdihar A.B. El; Bozkuş, Zeki
    The advancements of computer technologies have generated an incredible amount of data and information from numerous sources. Nowadays, the way of implementing health care are being changing by utilizing the benefits of advancements in computer technologies. It is believed that engineering this amount of data can assist in developing predictive tool that can help physicians to diagnosing and predicting some debilitating life-threatening illness such as thyroid gland disease. Our current work focuses on investigating python languages to diagnose thyroid gland disease based on machine learning, and involves developing a new tool to predict the diagnoses of thyroid gland diseases, which we have called as a MLTDD (Machine Learning App for thyroid Disease Diagnosis). MLTDD has been designed with Qt designer and programmed using PyDev, which is python IDE for Eclipse. MLTDD could diagnose with 99.81% accuracy. Decision tree algorithm has been used to create the ML model, in addition to training dataset to learn from. ML model can be used to get predictions on new data for which you do not know the target and that is what we did to predict the diagnosis of thyroid gland disease as a hyperthyroidism or hypothyroidism or a normal condition using CRT decision tree algorithm. MLTDD can minify the cost, the waiting time, and help physicians for more research, as well as decrease the errors and mistakes that can be made by humans on account of exhaustion and tiredness.
  • Thumbnail Image
    Master Thesis
    Development of Hybrid Mpi+upc Parallel Programming Model
    (Kadir Has Üniversitesi, 2011) Öztürk, Elif; Bozkuş, Zeki
    Parallel Computing is a form of computation that divides a large set of calculations into tasks and runs on multi-core machines simultaneously. Today, Message Passing Interface (MPI) is the most widely used parallel programming paradigm that provides programming both for symmetric multi-processors (SMPs) which consists of shared memory nodes with several multi-core CPUs connected to a high speed network and among nodes simultaneously. Unified Parallel C (UPC) is an alternative language that supports Partitioned Global Address Space (PGAS) that allows shared memory like programming on distributed memory systems.In this thesis, we describe the MPI, UPC and hybrid parallel programming paradigm which is designed to combine MPI and UPC programming models. The aim of the hybrid model is to utilize the advantages of MPI and UPC; these are, MPI?s data locality control and scalability strengths with UPC?s global address space, fine grain parallelism and ease of programming to achieve multiple level parallelism. This thesis presents a detailed description of hybrid model implementation comparing with pure MPI and pure UPC implementations. Experiments showed that the hybrid MPI+UPC model can significantly provide performance increases up to double with pure UPC implementation and up to 20% increases in comparison to pure MPI implementation. Furthermore, an optimization was achieved which improved the hybrid performance an additional 20%.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 15
    Improving OpenCL programmability with the Heterogeneous Programming Library
    (Elsevier B.V., 2015) Viñas,M.; Fraguela,B.B.; Bozkus,Z.; Andrade,D.
    The use of heterogeneous devices is becoming increasingly widespread. Their main drawback is their low programmability due to the large amount of details that must be handled. Another important problem is the reduced code portability, as most of the tools to program them are vendor or device-specific. The exception to this observation is OpenCL, which largely suffers from the reduced programmability problem mentioned, particularly in the host side. The Heterogeneous Programming Library (HPL) is a recent proposal to improve this situation, as it couples portability with good programmability. While the HPL kernels must be written in a language embedded in C++, users may prefer to use OpenCL kernels for several reasons such as their growing availability or a faster development from existing codes. In this paper we extend HPL to support the execution of native OpenCL kernels and we evaluate the resulting solution in terms of performance and programmability, achieving very good results. © The Authors. Published by Elsevier B.V.
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    Conference Object
    Citation - Scopus: 4
    A portable high-productivity approach to program heterogeneous systems
    (2012) Bozkus,Z.; Fraguela,B.B.
    The exploitation of heterogeneous resources is becoming increasingly important for general purpose computing. Unfortunately, heterogeneous systems require much more effort to be programmed than the traditional single or even multi-core computers most programmers are familiar with. Not only new concepts, but also new tools with different restrictions must be learned and applied. Additionally, many of these approaches are specific to one vendor or device, resulting in little portability or rapid obsolescence for the applications built on them. Open standards for programming he terogeneous systems such as OpenCL contribute to improve the situation, but the requirement of portability has led to a programming interface more complex than that of other approaches. In this paper we present a novel library-based approach to programming heterogeneous systems that couples portability with ease of use. Our evaluations indicate that while the performance of our library, called Heterogeneous Programming Library (HPL), is on par with that of OpenCL, the current standard for portable heterogeneous computing, the programming effort required by HPL is 3 to 10 times smaller than that of OpenCL based on the authors' implementation of five benchmarks. © 2012 IEEE.
  • Thumbnail Image
    Master Thesis
    Use of Machine Learning Techniques for Diagnosis of Thyroid Gland Disorder
    (Kadir Has Üniversitesi, 2016) Mofek, Izdihar; Bozkuş, Zeki
    The advancements of computer technologies have generated an incredible amount of data and information from numerous sources. Nowadays the way of implementing health care are being changing by utilizing the benefits of advancements in computer technologies. it is believed that engineering this amount of data can assist in developing predictive tool that can help physicians to diagnosing and predicting some debilitating life-threatening illness such as thyroid gland disease. Our current work focuses on investigating python languages to diagnose thyroid gland disease based on machine learning and involves developing a new tool to predict the diagnoses of thyroid gland diseases which we have called as a MLTDD (Machine Learning App for thyroid Disease Diagnosis). MLTDD has been designed with Qt designer and programmed using PyDev which is python iDE for Eclipse. MLTDD could diagnose with 99.81% accuracy. Decision tree algorithm has been used to create the ML model in addition to training dataset to learn from. ML model can be used to get predictions on new data for which you do not know the target and that is what we did to predict the diagnosis of thyroid gland disease as a hyperthyroidism or hypothyroidism or a normal condition using CRT decision tree algorithm. MLTDD can minify the cost the waiting time and help physicians for more research as well as decrease the errors and mistakes that can be made by humans on account of exhaustion and tiredness.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 5
    Optimizing Neuron Brain Simulator With Remote Memory Access on Distributed Memory Systems
    (Institute of Electrical and Electronics Engineers Inc., 2016) Shehzad, Danish; Bozkuş, Zeki
    The Complex neuronal network models require support from simulation environment for efficient network simulations. To compute the models increasing complexity necessitated the efforts to parallelize the NEURON simulation environment. The computational neuroscientists have extended NEURON by dividing the equations for its subnet among multiple processors for increasing the competence of hardware. For spiking neuronal networks inter-processor spikes exchange consume significant portion of overall simulation time on parallel machines. In NEURON Message Passing Interface (MPI) is used for inter processor spikes exchange MPI-Allgather collective operation is used for spikes exchange generated after each interval across distributed memory systems. However as the number of processors become larger and larger MPI-Allgather method become bottleneck and needs efficient exchange method to reduce the spike exchange time. This work has improved MPI-Allgather method to Remote Memory Access (RMA) based on MPI-3.0 for NEURON simulation environment MPI based on RMA provides significant advantages through increased communication concurrency in consequence enhances efficiency of NEURON and scaling the overall run time for the simulation of large network models.1 © 2015 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 10
    Big Data Platform Development With a Domain Specific Language for Telecom Industries
    (IEEE Computer Society, 2013) Senbalci,C.; Altuntas,S.; Bozkus,Z.; Arsan,T.
    This paper introduces a system that offer a special big data analysis platform with Domain Specific Language for telecom industries. This platform has three main parts that suggests a new kind of domain specific system for processing and visualization of large data files for telecom organizations. These parts are Domain Specific Language (DSL), Parallel Processing/Analyzing Platform for Big Data and an Integrated Result Viewer. In addition to these main parts, Distributed File Descriptor (DFD) is designed for passing information between these modules and organizing communication. To find out benefits of this domain specific solution, standard framework of big data concept is examined carefully. Big data concept has special infrastructure and tools to perform for data storing, processing, analyzing operations. This infrastructure can be grouped as four different parts, these are infrastructure, programming models, high performance schema free databases, and processing-analyzing. Although there are lots of advantages of Big Data concept, it is still very difficult to manage these systems for many enterprises. Therefore, this study suggest a new higher level language, called as DSL which helps enterprises to process big data without writing any complex low level traditional parallel processing codes, a new kind of result viewer and this paper also presents a Big Data solution system that is called Petaminer. © 2013 IEEE.