Bozkuş, Zeki

Bozkuş, Zeki

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.bozkus@khas.edu.tr

Full item page

Scholarly Output

30

Articles

7

Citation Count

0

Supervised Theses

9 Scholarly Output Search Results

Now showing 1 - 10 of 29

Citation Count: 8
GPU accelerated molecular docking simulation with genetic algorithms
(Springer Verlag, 2016) Altuntaş,S.; Bozkus,Z.; Fraguela,B.B.
Receptor-Ligand Molecular Docking is a very computationally expensive process used to predict possible drug candidates for many diseases. A faster docking technique would help life scientists to discover better therapeutics with less effort and time. The requirement of long execution times may mean using a less accurate evaluation of drug candidates potentially increasing the number of false-positive solutions, which require expensive chemical and biological procedures to be discarded. Thus the development of fast and accurate enough docking algorithms greatly reduces wasted drug development resources, helping life scientists discover better therapeutics with less effort and time. In this article we present the GPU-based acceleration of our recently developed molecular docking code. We focus on offloading the most computationally intensive part of any docking simulation, which is the genetic algorithm, to accelerators, as it is very well suited to them. We show how the main functions of the genetic algorithm can be mapped to the GPU. The GPU-accelerated system achieves a speedup of around ~ 14x with respect to a single CPU core. This makes it very productive to use GPU for small molecule docking cases. © Springer International Publishing Switzerland 2016.
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.
Citation Count: 0
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.
Citation Count: 0
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.
Citation Count: 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.
Citation Count: 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%.
Hybrid Kmeans Clustering Algorithm
(Kadir Has Üniversitesi, 2013) Çolakoğlu, Mustafa Alp; Bozkuş, Zeki
From the past up to the present size of the data is rapidly increasing day by day. Growing dimensions of this data can be held in databases is seen as a disadvantage. Companies have seen this information in databases as an excellent resource for increasing profitability. According to this source the profiles of the customers can be clustering and new products can be presented for cluster customers. So data mining algorithms are needed for rapidly examine these sources of information and obtaining meaningful information from resources.This project has been implemented K-means clustering algorithm with the hybrid programming method. This project suggested that data grouped with hybrid programming takes less time. Algorithm accelerated with hybrid programming method. Parallel programming used to solve K-means problem with using multi- processor and threads used for running operations at the same time. Hybrid version of K-means clustering algorithm was written using the C programming language. Existing parallel K-means source code used thread structure is added. Message Passing interface library and POSiX threads are used. Hybrid version of K-means algorithm and parallel K-means algorithm are run many times under the same conditions and comparisons were made. These comparisons were transferred to the tables and graphs. -- Abstract'tan.
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.
Citation Count: 1
Accelerating Brain Simulations on Graphical Processing Units
(IEEE, 2015) Kayraklıoğlu, Engin; El-Ghazawi, Tarek A.; Bozkuş, Zeki
NEural Simulation Tool(NEST) is a large scale spiking neuronal network simulator of the brain. In this work we present a CUDA(R) implementation of NEST. We were able to gain a speedup of factor 20 for the computational parts of NEST execution using a different data structure than NEST's default. Our partial implementation shows the potential gains and limitations of such possible port. We discuss possible novel approaches to be able to adapt generic spiking neural network simulators such as NEST to run on commodity or high-end GPGPUs.
Citation Count: 2
A Software Architecture for Inventory Management System
(2013) Arsan, Taner; Başkan, Emrah; Ar, Emrah; Bozkuş, Zeki
Inventory Management is one of the basic problems in almost every company. Before computer age and integration paper tables and paperwork solutions were being used as inventory management tools. These we very far from being a solution took so much time even needed employees just for this section of organization. There was no an efficient solution available in the many companies during these days. Every process was based on paperwork human fault rate was high the process and the tracing the inventory losses were not possible and there was no efficient logging systems. After the computer age every process is started to be integrated into electronic environment. And now we have qualified technology to implement new solutions to these problems. Software based systems bring the advantages of having the most efficient control with less effort and employees. These developments provide new solutions for also inventory management systems in this context. In this paper a new solution for Inventory Management System (IMS) is designed and implemented. Most importantly this system is designed for Kadir Has University and used as Inventory Management System. © 2013 Springer Science+Business Media.