This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.

This master's thesis is focused on GPU accelerated calculations on NVidia graphics card. CUDA technology is used and converted to implementation on a .NET platform. The problem is solved as a compiler from C# programing language to NVidia CUDA language with expression atrributes of C# language that preserves the same semantics of actions. Application is implemented in C# programing language and uses NRefactory, the open-source library.

This diploma thesis addresses the problem of liquid and gas simulation, it particularly deals with computer simulation of flow of viscous newtonian liquids with a free surface. A main goal of this work is to create an efficient simulation model, utilizing the benefits of current GPU parallel architecture for general-purpose computing. I chose to implement Smoothed Particle Hydrodynamics, a lagrangian particle-based method. A significant portion of this thesis consists of speed analysis of the implemented algorithm, comparison with other authors' achievements in the field and a demonstration of benefits brought by GPU involvement in the computation. As an output of the thesis I present an interactive computer program that allows for real-time simulation (and visualization) of water-like fluids.

This term project addresses the construction of kD tree acceleration structures and parallelization of this construction using GPU. At the beginning, there is an introduction of the reader into CUDA platform for parallel programming. There is a decription of generic principles as well as specific features that will be used in this thesis. Following that the reader is put into the issue of acceleration structures for Ray tracing. These structures are described and the kD tree acceleration structure and its variants are portrayed in detail. After that the analysis of chosen kD tree variant is broken down and the problems and issuse of its parallel implementation are adressed. As a part of implementation discription, there is a short descripton of CPU variant and detailed specifications of the CUDA kernels. The testing section brings the results of implementation in form of CPU vs GPU comparison, as well as evaluation of how much the metric set in design was fulfilled. In the end there is a summary of achieved goals and results followed by possible future improvements for the implementation.

This work is part of the k-Wave project, which is a toolbox designed for time ultrasound simulations in complex and heterogeneous media. The simulation functions are based on the k-space pseudospectral method. The goal of this work is to compute these simulations on graphics cards using local domain decompostion. Thanks to decomposition we could compute these simulations faster, and on larger data grids. The main goal of this work is to achieve efficiency and scalability.

Current research in deep learning is primarily focused on using Python as a support language. Go, an emerging language, that has many benefits including native support for concurrency has seen a rise in adoption over the past few years. However, this language is not widely used to develop learning models due to the lack of supporting libraries and frameworks for model development. In this thesis, the use of Go for the development of neural network models in general and convolution neural networks is explored. The proposed study is based on a Go-CUDA implementation of neural network models called GoCuNets. This implementation is then compared to a Go-CPU deep learning implementation that takes advantage of Go's built in concurrency called ConvNetGo. A comparison of these two implementations shows a significant performance gain when using GoCuNets compared to ConvNetGo.<br>

In den heutigen Algorithmen zu Soundlokalisierungsverfahren sind Matrizenberechnungen allgegenwärtig, aus diesem Grund befasst sich diese Arbeit mit der Analyse von Matrixberechnungen und deren möglichen Realisierung auf eingebetteten Systemen. Hierzu werden die gängigen Beschleunigungstechnologien wie Prozessoren, Grafikbeschleunigung und Parallelisierung mit der Hilfe von FPGAs analysiert. Die Ergebnisse zeigen, dass ein Grafikchip in der Lage ist eine solche Matrixvektormultiplikation im Gegensatz zu einer Implementierung auf einem Prozessor zu beschleunigen. Eine Implementierung auf einem FPGA, welche in ihrem Entwicklungsaufwand deutlich über der einer Beschleunigung durch einen Grafikchip liegt, ist hinsichtlich der Laufzeit durch eine GPU nicht zu erreichen.

Because of the increasing importance of signal processing in today's society, there is a need to easily experiment with new ways to process signals. Usually, fast-performing digital signal processing is done with special-purpose hardware that are difficult to develop for. GPUs pose an alternative for fast performing digital signal processing. The work in this thesis is an analysis and implementation of a GPU version of a digital signal processing chain provided by SAAB. Through an iterative process of development and testing, a final implementation was achieved. Two benchmarks, both comprised of 4.2 M test samples, were made to compare the CPU implementation with the GPU implementation. The benchmark was run on three different platforms: a desktop computer, a NVIDIA Jetson AGX Xavier and a NVIDIA Jetson TX2. The results show that the parallelized version can reach several magnitudes higher throughput than the CPU implementation.

Colonoscopy is an endoscopic technique that allows a physician to inspect the mucosa of the human colon. Previous methods and software solutions to detect informative frames in a colonoscopy video (a process called informative frame filtering or IFF) have been hugely ineffective in (1) covering the proper definition of an informative frame in the broadest sense and (2) striking an optimal balance between accuracy and speed of classification in both real-time and non real-time medical procedures. In my thesis, I propose a more effective method and faster software solutions for IFF which is more effective due to the introduction of a heuristic algorithm (derived from experimental analysis of typical colon features) for classification. It contributed to a 5-10% boost in various performance metrics for IFF. The software modules are faster due to the incorporation of sophisticated parallel-processing oriented coding techniques on modern microprocessors. Two IFF modules were created, one for post-procedure and the other for real-time. Code optimizations through NVIDIA CUDA for GPU processing and/or CPU multi-threading concepts embedded in two significant microprocessor design philosophies (multi-core design and many-core design) resulted a 5-fold acceleration for the post-procedure module and a 40-fold acceleration for the real-time module. Some innovative software modules, which are still in testing phase, have been recently created to exploit the power of multiple GPUs together.

Genome-wide association studies(GWAS) are used to find associations betweengenetic markers and diseases. One part of GWAS is to study interactions be-tween markers which can play an important role in the risk for the disease. Thesearch for interactions can be computationally intensive. The aim of this thesiswas to improve the performance of software used for gene-environment interac-tion by using parallel programming techniques on graphical processors. A studyof the new programs performance, speedup and efficiency was made using mul-tiple simulated datasets. The program shows significantly better performancecompared with the older program.