Devrim Akgün

GPU accelerated dynamic functional connectivity analysis for functional MRI data.

Intense computations in engineering and science, especially bioinformatics have been made practical by the recent advances in Graphical Processing Unit (GPU) computing technology. In this study, implementation and performance evaluations for a GPU-accelerated dynamic functional connectivity (DFC) analysis, which is an analysis method for investigating dynamic interactions among different brain networks, is presented. Open Computing Library (OpenCL), which provides a general framework for GPU computing, is utilized, and it is shown to reduce the DFC analysis computation time. The parallel implementation with OpenCL provides up to 10x speed-up over sequential implementation.

GPU accelerated training of image convolution filter weights using genetic algorithms

This paper proposes a fast algorithm for training image filter using GPU.Parallelization of genetic algorithms is realized by master-slave method.Sub-image based (SBM) method is proposed to use the GPU efficiently.SBM is developed by discussing other alternative design considerations.Experimental results show about 50i? to 90i? acceleration using GeForce GTX 660. Genetic algorithms (GA) provide an efficient method for training filters to find proper weights using a fitness function where the input signal is filtered and compared with the desired output. In the case of image processing applications, the high computational cost of the fitness function that is evaluated repeatedly can cause training time to be relatively long. In this study, a new algorithm, called sub-image blocks based on graphical processing units (GPU), is developed to accelerate the training of mask weights using GA. The method is developed by discussing other alternative design considerations, including direct method (DM), population-based method (PBM), block-based method (BBM), and sub-images-based method (SBM). A comparative performance evaluation of the introduced methods is presented using sequential and other GPUs. Among the discussed designs, SBM provides the best performance by taking advantage of the block shared and thread local memories in GPU. According to execution duration and comparative acceleration graphs, SBM provides approximately 55-90 times more acceleration using GeForce GTX 660 over sequential implementation on a 3.5GHz processor.

A practical parallel implementation for TDLMS image filter on multi-core processor

In this study, parallel implementation of adaptive image filtering algorithm based on two-dimensional least mean square method (TDLMS) where the weights are continuously adjusted during filtering was realized by proposed design considerations. Despite its strictly sequential structure, the effect of a pixel on weights vanishes as the filter mask progresses. Based on this property, the load of filtering algorithm is allocated to threads by splitting the input image into sub-blocks. Due to the discontinuities, the crossing distortions between sub-blocks were eliminated using weight synchronization with the neighbor sub-block. Performance evaluations for various sizes of images were realized on a computer with multi-core processor using open multiprocessing library. In spite of the sequential nature of the algorithm, results show that the parallel implementation provides significant improvements in terms of both speedup and parallel efficiency.

GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL

Intense computations in engineering and science, especially bioinformatics have been made practical by the recent advances in Graphical Processing Unit (GPU) computing technology. In this study, implementation and performance evaluations for a GPU-accelerated dynamic functional connectivity (DFC) analysis, which is an analysis method for investigating dynamic interactions among different brain networks, is presented. Open Computing Library (OpenCL), which provides a general framework for GPU computing, is utilized, and it is shown to reduce the DFC analysis computation time. The parallel implementation with OpenCL provides up to 10x speed-up over sequential implementation.

A method for the Computational Frequency Sweep Analysis of Nonlinear ODEs using GPU Acceleration

ABSTRACT Computational sweep analysis of nonlinear ODEs (ordinary differential equations) is of importance in engineering system analysis and design. Sweep analyses usually demand intense computational power according to the number of points and the number of system parameters. This paper presents an efficient parallel algorithm for the sweep analysis of nonlinear ODEs based on graphical processing unit acceleration. The developed method preserves the jump phenomenon characteristics intrinsic to nonlinear ODEs and reduces the effects of irregular computational load. Experiments were realized using Duffing equation by sweeping frequency, amplitude, and equation coefficients. Directly, data parallel implementation and proposed implementations are compared to show the efficiency of the proposed method. Experimental results show that the new method provides significant reductions in the computational durations when compared to sequential implementation.

A performance analysis of seam carving algorithm based on energy function

Seam carving, which sometimes referred to as content aware resizing is one of the successful methods for resizing the images with small deteriorations in image quality. Apart from the size of the image to be processed, the performance of the seam carving algorithm is highly dependent on energy function, the number of seams and the image content. In this study a comparative analysis of the seam carving algorithm for image quality and computation durations has been realized using various energy functions. In the experiments, various images with different contents and sizes are used and the performance results have been presented for various resize ratios.

Performance Evaluations for OpenMP Accelerated Training Of Separable Image Filter

One of the widespread image processing applications is image filtering with two dimensional convolution. Determining the weights of image filters are of importance for the success of filtering operation. Heuristic algorithms such as genetic algorithms provide an efficient way of training these types of filters. Due to the high computational cost of repetitive image filtering operations, this process may take hours to implement using single core computing. OpenMP (Open Multi Processing) provides an efficient library for utilizing the computing power of multicore processors. In this study, OpenMP accelerated training of separable filters that are a subclass of convolution filters has been implemented based on genetic algorithms. Comparative speed-up results for various sizes of images using various sizes of filtering kernels were presented. Also the effect of population size of genetic algorithm and the number of working cores have been investigated.

BiLGiSAYAR KONTROLLÜ SERBEST DÜŞME DENEY SİSTEMİNİN TASARIMI

Bu calismada, serbest dusine deneyinin • gerceklestirilniesinde kullanilan bir bilgisayar kontrollu sistenlin yapisi yazilini ve donanim olarak gerceklestirilniistir. Deney sisteini., DELPHI gorsel progr amlania diH ilc yazihn1s kullanici ara yuzu ve bunun kontrolunde calisan ahs diizenegi icermektedir. Ser best d usnie d en e