Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/45

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Browsing Bilgisayar Mühendisliği Bölümü Koleksiyonu by Author "Altuntaş, Serkan"

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    Citation Count: 7
    Big Data Platform Development with a Domain Specific Language for Telecom Industries
    (IEEE, 2013) Arsan, Taner; Bozkuş, Zeki; Bozkuş, Zeki; Arsan, Taner
    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. hi 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.
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    Citation Count: 5
    GPU Accelerated Molecular Docking Simulation with Genetic Algorithms
    (Springer International Publishing Ag, 2016) Bozkuş, Zeki; Bozkuş, Zeki; Fraguela, Basilio 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 similar to 14x with respect to a single CPU core. This makes it very productive to use GPU for small molecule docking cases.