京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23544号 / 情博第774号 / 新制||情||132(附属図書館) / 京都大学大学院情報学研究科先端数理科学専攻 / (主査)准教授 藤原 宏志, 教授 磯 祐介, 教授 田口 智清 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

As GPGPU computing becomes more popular, it will be used to tackle a wider range of problems. However, due to the current state of GPGPU programming, programmers are typically required to be familiar with the architecture of the GPU in order to effectively program it. Fortunately, there are software packages that attempt to simplify GPGPU programming in higher-level languages such as Java and Python. However, these software packages do not attempt to abstract the GPU-acceleration process completely. Instead, they require programmers to be somewhat familiar with the traditional GPGPU programming model which involves some understanding of GPU threads and kernels. In addition, prior to using these software packages, programmers are required to transform the data they would like to operate on into arrays of primitive data. Typically, such software packages restrict the use of object-oriented programming when implementing the code to operate on this data. This thesis presents GPUMap, which is a proof-of-concept GPU-accelerated map function for Python. GPUMap aims to hide all the details of the GPU from the programmer, and allows the programmer to accelerate programs written in normal Python code that operate on arbitrarily nested objects using a majority of Python syntax. Using GPUMap, certain types of Python programs are able to be accelerated up to 100 times over normal Python code. There are also software packages that provide simplified GPU acceleration to distributed computing frameworks such as MapReduce and Spark. Unfortunately, these packages do not provide a completely abstracted GPU programming experience, which conflicts with the purpose of the distributed computing frameworks: to abstract the underlying distributed system. This thesis also presents GPU-accelerated RDD (GPURDD), which is a type of Spark Resilient Distributed Dataset (RDD) which incorporates GPUMap into its map, filter, and foreach methods in order to allow Spark applicatons to make use of the abstracted GPU acceleration provided by GPUMap.

Tato diplomová práce se zabývá vývojem a implementací algoritmu pro detekci QR kódů s integrací do platformy ROS a výpočty běžícími na grafické kartě. Z rešerše současně dostupných nástrojů a technik je vybrán vhodný postup a algoritmus je napsán jako modul v programovacím jazyce Python, který je snadno integrovatelný do ROS. Ke zprostředkování výpočtů na vícejádrovém hardware, jako jsou grafické karty či vícejádrové procesory, je využita knihovna OpenCL.

The main objective of this research is to propose optimization of the defect detection algorithm in the production of nonwoven textile. The algorithm was developed by CAMEA spol. s.r.o. As a consequence of upgrading the current camera system to a more powerful one, it will be necessary to optimize the current algorithm and choose the hardware with the appropriate architecture on which the calculations will be performed. This work will describe a usefull programming techniques of CUDA software architecture and OpenCL framework in details. Using these tools, we proposed to implement a parallel equivalent of the current algorithm, describe various optimization methods, and we designed a GUI to test these methods.

This thesis explores potential use of GPGPU libraries to accelerate k-Wave toolkit's acoustic wave propagation simulation. Firstly, the thesis researches and assesses available high level GPGPU libraries. Afterwards, an insight into k-Wave toolkit's current state of simulation acceleration is provided. Based on that, an approach to enhance currently available code for processors into a heterogeneous application, that is capable of being run on graphics card, is proposed. The outcome of this thesis is an application that can utilize graphics card. If graphics card is unavailable, a fallback into thread and SIMD based acceleration for processor is executed. The product of this thesis is then evaluated based on its performance, maintenance difficulty and usability.

The thesis deals with the acceleration of backpropagation neural networks using graphics chips. To solve this problem it was used the OpenCL technology that allows work with graphics chips from different manufacturers. The main goal was to accelerate the time-consuming learning process and classification process. The acceleration was achieved by training a large amount of neural networks simultaneously. The speed gain was used to find the best settings and topology of neural network for a given task using genetic algorithm.

This work describes a relatively new technique designed to write highly parallel programs, that name is OpenCL. It is intended for both GPU and CPU and other parallel processors. Libraries used by the processor architecture, which includes a large number of small cores. These cores are not as comprehensive as conventional processors and is therefore suitable for calculations, which are many and they are simple. It is this property could, under certain conditions, accelerate the calculation of the hologram, namely the calculation of the optical field. While the calculation itself is simple, but the amount of processed data is large and therefore slow. The work also contain the basic concepts of explanation of optical and digital holography.

Subject of this thesis is optimalization of storage function of water management system. The work is based on dissertation thesis of Ing. Pavel Menšík Ph.D. Automatization of   storage function of water management system. As optimalization method was chosen diferential evolution. Sequential version of the method will be implemented as a first step, followed by CPU accelerated and   GPU accelerated versions.

This work deals with the task of image scaling using GPU paralelization. Portion of text is devoted to signal processing and his affection of whole result including measuring it's quality. Describtion of the most important methods including super-resolution is given further in the text. An important part of this thesis is library implementing choosen methods with usage of paralelization on graphic chip. Achieved results of paralelization are demonstrated on set of speed tests.

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.