This report summarizes the work done to develop a 3D teleconferencing system, which enables remote participants anywhere in the world to be scanned in 3D, transmitted and displayed on a constructed 3D display with correct vertical and horizontal parallax, correct eye contact and eye gaze. The main focus of this report is the development of this system and especially how to in an efficient and general manner render to the novel 3D display. The 3D display is built out of modified commodity hardware and show a 3D scene for observers in up to 360 degrees around it and all heights. The result is a fully working 3D Teleconferencing system, resembling communication envisioned in movies such as holograms from Star Wars. The system transmits over the internet, at similar bandwidth requirements as concurrent 2D videoconferencing systems. / Project done at USC Institute for Creative Technologies, LA, USA. Presented at SIGGRAPH09.

Fluid simulation is today a hot topic in computer graphics. New highly optimized algorithms have allowed complex systems to be simulated in high speed. This master thesis describes how the graphics processing unit, found in most computer workstations, can be used to optimize the rendering of volumetric fluids. The main aim of the work has been to develop a software that is capable of rendering fluids in high quality and with high performance using OpenGL. The software was developed at Filmgate, a digital effects company in Göteborg, and much time was spent making the interface and the workflow easy to use for people familiar with Autodesk Maya. The project resulted in a standalone rendering application, together with a set of plugins to exchange data between Maya and our renderer. Most of the goals have been reached when it comes to rendering features. The performance bottleneck turned out to be reading data from disc and this is an area suitable for future development of the software.

Context. Many occlusion culling algorithms have to cope with the task of balancing performance and accuracy. While it is desirable to accurately identify all occluded scene objects, settling with a rough estimate is often more beneficial for the overall performance. Algorithms that rely on a depth buffer can often gain a lot of performance by performing the occlusion culling at a lower resolution than the resolution of the screen. This calls for more advanced methods to render the depth buffer as the standard rasterizer will not guarantee inner coverage. Objectives. The goal of this thesis is to find a solution to generate a depth buffer where all rasterized pixels are fully covered by overlapping occluders. An algorithm is proposed that is based on previous work on inner conservative rasterization. The algorithm addresses some of the problems existing methods are suffering from, but also has some flaws of its own. Methods. The proposed algorithm is tested by comparing it to two methods that also produce conservative results. A GPU-based occlusion culling system is developed to conduct an experiment. The experiment is performed by measuring performance and culling efficiency in two different views of a scene. The scene is set up to represent an average setting in a game. Results. The results from the experiment show that the proposed algorithm outperforms its competitors in many cases. In the first scene view, the total frame time is 5% faster at a full screen resolution of 1366x768 pixels and 8% faster at a full screen resolution of 1920x1080 pixels. The depth buffer generated by the proposed algorithm is culling atleast as many occludees as its competitors and often surpasses them. In the second scene view, the total frame time is 2% faster at a full screen resolution of 1366x768 pixels and 3% faster at a full screen resolution of 1920x1080 pixels. The depth buffer generated by the proposed algorithm is often culling more occludees than its competitors, but is at lower resolutions less efficient, up to 3%. Conclusions. The conclusions show that the goal has been reached. The proposed algorithm lacks flexibility, but provides good performance and accuracy. Future work to improve the proposed algorithm is outlined.

Det här examensarbetet utvärderar om det är möjligt att använda en eller flera GPUs för att under realtidsförhållanden utföra radarsignalbehandling i ett pulsdopplerradarsystem. En kedja med radarsignalbehandlingsalgoritmer som används för att utföra detektion har implementerats med CUDA och sedan prestandaanalyserats med fokus på låg exekveringstid. Två CFAR-detektionsalgoritmer, CA- och OS-CFAR, har inkluderats i analysen. För CFAR-algoritmerna har flera alternativ formulerats och implementerats för att utvärdera hur de bäst kan anpassas för att exekvera på en GPU. Prestandaanalysen av de implementerade algoritmerna visar att det är möjligt för det tänkta systemet att använda grafikkort för att utföra radarsignalbehandlingen i realtid. Implementationslösningar har presenterats både för CA- och OS-CFAR som uppfyller tidskraven för systemet, i vissa fall med god marginal. Lägst exekveringstider erhölls när vissa kompromisser gjordes med algoritmernas flexibilitet. För CA-CFAR erhölls lägst exekveringstider när ett Summed Area Table användes för tröskelvärdesberäkningen. För OS-CFAR uppmättes de lägsta exekveringstiderna när en rankjämförelse gjordes istället för en full sortering. Prestandaanalysen visar även att det på ett effektivt sätt går att skala upp implementationen för att utnyttja fler än en GPU.

The Modelica language is a modelling and programming  language for modelling cyber-physical systems using equations and  algorithms. In this thesis two suggested extensions of the Modelica  language are covered. Those are Partial Differential Equations (PDE)  and explicit parallelism in algorithmic code.  While PDEs are not  yet supported by the Modelica language, this thesis presents a  framework for solving PDEs using the algorithmic part of the  Modelica language, including parallel extensions. Different  numerical solvers have been implemented using the explicit parallel  constructs suggested for Modelica by the ParModelica language  extensions, and implemented as part of OpenModelica. The solvers  have been evaluated using different models, and it can be seen how  bigger models are suitable for a parallel solver. The intention has  been to write a framework suitable for modelling and parallel  simulation of PDEs. This work can, however, also be seen as a case  study of how to write a custom solver using parallel algorithmic  Modelica and how to evaluate the performance of a parallel solver.

Context. Embedded platforms GPUs are reaching a level of perfor-mance comparable to desktop hardware. Therefore it becomes inter-esting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat gen-eration can be an issue depending on load distribution on the device. Objectives. We evaluate the viability of a feature detector and de-scriptor on the Xperia Z3. Specifically we evaluate the the pair basedon real-time execution, heat generation and performance. Methods. We implement the feature detection and feature descrip-tor pair Harris-Hessian/FREAK for GPU execution using OpenCL,focusing on embedded platforms. We then study the heat generationof the application, its execution time and compare our method to twoother methods, FAST/BRISK and ORB, to evaluate the vision per-formance. Results. Execution time data for the Xperia Z3 and desktop GeForceGTX660 is presented. Run time temperature values for a run ofnearly an hour are presented with correlating CPU and GPU ac-tivity. Images containing comparison data for BRISK, ORB andHarris-Hessian/FREAK is shown with performance data and discus-sion around notable aspects. Conclusion. Execution times on Xperia Z3 is deemed insufficientfor real-time applications while desktop execution shows that there isfuture potential. Heat generation is not a problem for the implemen-tation. Implementation improvements are discussed to great lengthfor future work. Performance comparisons of Harris-Hessian/FREAKsuggest that the solution is very vulnerable to rotation, but superiorin scale variant images. Generally appears suitable for near duplicatecomparisons, delivering much greater number of keypoints. Finally,insight to OpenCL application development on Android is given

In this master thesis, algorithms for acoustic simulations in underwater environments are ported for GPU processing. The GPU parallel computing platforms used are CUDA, OpenCL and SkePU. The purpose of this master thesis is to adapt and evaluate the ported algorithms' performance on two modern NVIDIA GPUs, Tesla K20 and Quadro K5000. Several optimizations, described in existing literature for GPU processing (e.g. usage of shared memory, coalesced memory accesses), are implemented and multiple versions of each algorithm are created to study their trade-offs. Evaluation on two GPUs showed that different versions of the same algorithm have different performance characteristic and execution with the best performing version can give better performance than the original algorithm executing on 8 CPUs. A performance comparison between CUDA, OpenCL and SkePU versions of one algorithm is also made.

Digital maps can be represented as either raster (bitmap images) or vector data. Vector maps are often preferable as they can be stored more efficiently and rendered irrespective of screen resolution. Vector map rendering on demand can be a computationally intensive task and has to be implemented in an efficient manner to ensure good performance and a satisfied end-user, especially on mobile devices with limited computational resources. This thesis discusses different ways of utilizing the on-chip GPU to improve the vector map rendering performance of an existing iOS app. It describes an implementation that uses OpenGL ES 2.0 to achieve the same end-result as the old CPU-based implementation using the same underlying map infras- tructure. By using the OpenGL based map renderer as well as implementing other performance optimizations, the authors were able to achieve an almost fivefold increase in rendering performance on an iPad Air.

This work deals with crytpoanalysis, calculation performance and its distribution. It describes the methods of distributing the calculation performance for the needs of crypto analysis. Further it focuses on other methods allowing the speed increasing in breaking the cryptographic algorithms especially by means of the hash functions. The work explains the relatively new term of cloud computing and its consecutive use in cryptography. The examples of its practical utilisation follow. Also this work deals with possibility how to use grid computing for needs of cryptoanalysis. At last part of this work is system design using „cloud computing“ for breaking access password.

Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to process each individual query, which results in underutilization of GPU. When a single query data warehousing workload was run on an open source GPU query engine, the utilization of main GPU resources was found to be less than 25%. The low utilization then leads to low system throughput. To resolve this problem, this paper suggests a way to transfer all of the desired data into the global memory of GPU and keep it until all queries are executed as one batch. The PCIe transfer time from CPU to GPU is minimized, which results in better performance in less time of overall query processing. The execution time was improved by up to 40% when running multiple queries, compared to dedicated processing.