Mobile 3D Game Engine

This project explores the feasibility of a mobile 3D game engine on a smart device. Using OpenGL ES, a graphics library for embedded devices, we have developed Tubetris, a unique and entertaining fully three dimensional puzzle game which runs on a HP iPaq PDA . The game demonstrates the 3D capabilities of mobile devices and that fully 3D applications are indeed possible on mobile devices

Evaluation of the parallel computational capabilities of embedded platforms for critical systems

Modern critical systems need higher performance which cannot be delivered by the simple architectures used so far. Latest embedded architectures feature multi-cores and GPUs, which can be used to satisfy this need. In this thesis we parallelise relevant applications from multiple critical domains represented in the GPU4S benchmark suite, and perform a comparison of the parallel capabilities of candidate platforms for use in critical systems. In particular, we port the open source GPU4S Bench benchmarking suite in the OpenMP programming model, and we benchmark the candidate embedded heterogeneous multi-core platforms of the H2020 UP2DATE project, NVIDIA TX2, NVIDIA Xavier and Xilinx Zynq Ultrascale+, in order to drive the selection of the research platform which will be used in the next phases of the project. Our result indicate that in terms of CPU and GPU performance, the NVIDIA Xavier is the highest performing platform

Analysis and Segmentation of Face Images using Point Annotations and Linear Subspace Techniques

This report provides an analysis of 37 annotated frontal face images. All results presented have been obtained using our freely available Active Appearance Model (AAM) implementation. To ensure the reproducibility of the presented experiments, the data set has also been made available. As such, the data and this report may serve as a point of reference to compare other AAM implementations against. In addition, we address the problem of AAM model truncation using parallel analysis along with a comparable study of the two prevalent AAM learning methods; principal component regression and estimation of fixed Jacobian matrices. To assess applicability and efficiency, timings for model building, warping and optimisation are given together with a description of ho

Parallel Rendering and Large Data Visualization

We are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.Comment: PhD thesi

A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering. In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract Foreword and Acknowledgements Overview and Contributions Part 1 - Introduction 1 Fluorescence Microscopy 2 Introduction to Visual Processing 3 A Short Introduction to Cross Reality 4 Eye Tracking and Gaze-based Interaction Part 2 - VR and AR for System Biology 5 scenery — VR/AR for Systems Biology 6 Rendering 7 Input Handling and Integration of External Hardware 8 Distributed Rendering 9 Miscellaneous Subsystems 10 Future Development Directions Part III - Case Studies C A S E S T U D I E S 11 Bionic Tracking: Using Eye Tracking for Cell Tracking 12 Towards Interactive Virtual Reality Laser Ablation 13 Rendering the Adaptive Particle Representation 14 sciview — Integrating scenery into ImageJ2 & Fiji Part IV - Conclusion 15 Conclusions and Outlook Backmatter & Appendices A Questionnaire for VR Ablation User Study B Full Correlations in VR Ablation Questionnaire C Questionnaire for Bionic Tracking User Study List of Tables List of Figures Bibliography Selbstständigkeitserklärun

Hardware Certification for Real-time Safety-critical Systems: State of the Art

This paper discusses issues related to the RTCA document DO-254 Design Assurance Guidance for Airborne Electronic Hardware and its consequences for hardware certification. In particular, problems related to circuits’ compliance with DO-254 in avionics and other industries are considered. Extensive literature review of the subject is given, including current views on and experiences of chip manufacturers and EDA industry with qualification of hardware design tools, including formal approaches to hardware verification. Some results of the authors’ own study on tool qualification are presented

360º Indoors image processing for 3D model reconstruction

In this modern age of computer technology we are pushing the unimaginable limits of our reality. One of the human desires with these advances is to digitise the vast amount of information that is present in our reality. An important source of information is the 3-dimensional space in which we live. Especially indoors environments that we frequently occupy, for example, living places. With the proliferation of photographing devices, the development of cheap omnidirectional cameras has been one of the interests. So nowadays it is quite easy to obtain spatial data of interior spaces in form of equirectangular images. In this project we study the problem of 3D Indoors Model Reconstruction from Spherical Images. Though, we study it under perspective based methods as it is possible to perform the conversion from one to other. We first formally specify the problem to be solved. We find many different specifications and describe reconstruction methods for some of them. We choose one specification for our use case. Most of the methods require feature extraction and matching, and then performing multi-view geometry estimation. We continue the study of these methods in the experimentation phase. We propose different hypothesis relevant to different steps, perform experiments and form our conclusions. We finish our work by implementing a very simple system solving this problem, making use of ASIFT feature extractor, FLANN kD-Tree feature matcher, and OpenCV's essential matrix estimation algorithm.En aquesta era moderna de la tecnologia de computadors estem empenyent els líımits inimaginables de la nostra realitat. Una de les aspiracions humanes amb aquests avenços és la digitalització de l’enorme quantitat d’informació present en la nostra realitat. Una de les fonts importants d’informació és l’espai 3-dimensional en el que vivim. Especialment, els entorns interiors que habitem, per exemple, els habitatges. Amb la proliferació dels dispositius fotogràfics, el desenvolupament de càmeres omnidireccionals barates ha estat un dels interessos. Per aquest motiu, avui en dia és molt fàcil obtenir dades espacials dels espais interiors en forma d’imatges equirectangulars. En aquest projecte estudiem el problema de la Reconstrucció de Models 3D d’Interiors a partir d’Imatges Esfèriques. Tanmateix, estudiem el problema fent ús de mètodes basats en la perspectiva ja que és possible fer la conversió d’un a l’altre. En primer lloc, especifiquem formalment el problema a resoldre. A continuació, trobem diverses especificacions i descrivim mètodes de reconstruccions per algunes d’elles. Seleccionem una especificaciò pel nostre cas d’ús. La majoria de mètodes utilitzen feature extraction, feature matching i epipolar geometry. Continuem l’estudi amb la fase d’experimentació. Proposem hipòtesis rellevants a diferents passos, realitzem els experiments i extraiem conclusions. Acabem el treball implementant un sistema simple resolent el problema, fent ús de ASIFT feature extractor, FLANN kD- Tree feature matcher, i l’algorisme d’OpenCV per l’aproximació de la matriu essencialEn esta era moderna de tecnología de computadores estamos empujando los límites inimaginables de nuestra realidad. Una de las aspiraciones humanas con estos avances es la digitalización de la tremenda cantidad de información presente en nuestra realidad. Una de las importantes fuentes de información es el espacio 3-dimensional en que vivimos. Especialmente los entornos interiores que habitamos, por ejemplo, las viviendas. Con la proliferación de los dispositivos fotográficos, el desarrollo de cámaras omnidireccionales baratas ha sido uno de los intereses. Por ello, hoy en día es muy fácil de obtener datos espaciales de los espacios interiores en forma de imágenes equirectangulares. En este proyecto estudiamos el problema de Reconstrucción de Modelos 3D de Interiores desde Imágenes Esféricas. Sin embargo, estudiamos el problema bajo métodos basados en la perspectiva ya que es posible hacer la conversión de uno al otro. Primero especificamos formalmente el problema a resolver. Encontramos distintas especificaciones y describimos métodos de reconstruccion para algunas de ellas. Seleccionamos una especificación para nuestro caso de uso. La mayoría de métodos utilizan feature extraction, feature matching, y epipolar geometry. Continuamos el estudio en la fase de experimentación. Proponemos hipótesis relevantes a diferentes pasos, realizamos los experimentos y sacamos conclusiones. Acabamos el trabajo implementando un sistema simple resolviendo el problema, haciendo uso de ASIFT feature extractor, FLANN kD-Tree feature matcher, y el algoritmo de OpenCV para la aproximación de la matriz esencial

Mental vision:a computer graphics platform for virtual reality, science and education

Despite the wide amount of computer graphics frameworks and solutions available for virtual reality, it is still difficult to find a perfect one fitting at the same time the many constraints of research and educational contexts. Advanced functionalities and user-friendliness, rendering speed and portability, or scalability and image quality are opposite characteristics rarely found into a same approach. Furthermore, fruition of virtual reality specific devices like CAVEs or wearable systems is limited by their costs and accessibility, being most of these innovations reserved to institutions and specialists able to afford and manage them through strong background knowledge in programming. Finally, computer graphics and virtual reality are a complex and difficult matter to learn, due to the heterogeneity of notions a developer needs to practice with before attempting to implement a full virtual environment. In this thesis we describe our contributions to these topics, assembled in what we called the Mental Vision platform. Mental Vision is a framework composed of three main entities. First, a teaching/research oriented graphics engine, simplifying access to 2D/3D real-time rendering on mobile devices, personal computers and CAVE systems. Second, a series of pedagogical modules to introduce and practice computer graphics and virtual reality techniques. Third, two advanced VR systems: a wearable, lightweight and handsfree mixed reality setup, and a four sides CAVE designed through off the shelf hardware. In this dissertation we explain our conceptual, architectural and technical approach, pointing out how we managed to create a robust and coherent solution reducing complexity related to cross-platform and multi-device 3D rendering, and answering simultaneously to contradictory common needs of computer graphics and virtual reality for researchers and students. A series of case studies evaluates how Mental Vision concretely satisfies these needs and achieves its goals on in vitro benchmarks and in vivo scientific and educational projects

Energy-efficient mobile GPU systems

The design of mobile GPUs is all about saving energy. Smartphones and tablets are battery-operated and thus any type of rendering needs to use as little energy as possible. Furthermore, smartphones do not include sophisticated cooling systems due to their small size, making heat dissipation a primary concern. Improving the energy-efficiency of mobile GPUs will be absolutely necessary to achieve the performance required to satisfy consumer expectations, while maintaining operating time per battery charge and keeping the GPU in its thermal limits. The first step in optimizing energy consumption is to identify the sources of energy drain. Previous studies have demonstrated that the register file is one of the main sources of energy consumption in a GPU. As graphics workloads are highly data- and memory-parallel, GPUs rely on massive multithreading to hide the memory latency and keep the functional units busy. However, aggressive multithreading requires a huge register file to keep the registers of thousands of simultaneous threads. Such a big register file exceeds the power budget typically available for an embedded graphics processors and, hence, more energy-efficient memory latency tolerance techniques are necessary. On the other hand, prior research showed that the off-chip accesses to system memory are one of the most expensive operations in terms of energy in a mobile GPU. Therefore, optimizing memory bandwidth usage is a primary concern in mobile GPU design. Many bandwidth saving techniques, such as texture compression or ARM's transaction elimination, have been proposed in both industry and academia. The purpose of this thesis is to study the characteristics of mobile graphics processors and mobile workloads in order to propose different energy saving techniques specifically tailored for the low-power segment. Firstly, we focus on energy-efficient memory latency tolerance. We analyze several techniques such as multithreading and prefetching and conclude that they are effective but not energy-efficient. Next, we propose an architecture for the fragment processors of a mobile GPU that is based on the decoupled access/execute paradigm. The results obtained by using a cycle-accurate mobile GPU simulator and several commercial Android games show that the decoupled architecture combined with a small degree of multithreading provides the most energy efficient solution for hiding memory latency. More specifically, the decoupled access/execute-like design with just 4 SIMD threads/processor is able to achieve 97% of the performance of a larger GPU with 16 SIMD threads/processor, while providing 20.5% energy savings on average. Secondly, we focus on optimizing memory bandwidth in a mobile GPU. We analyze the bandwidth usage in a set of commercial Android games and find that most of the bandwidth is employed for fetching textures, and also that consecutive frames share most of the texture dataset as they tend to be very similar. However, the GPU cannot capture inter-frame texture re-use due to the big size of the texture dataset for one frame. Based on this analysis, we propose Parallel Frame Rendering (PFR), a technique that overlaps the processing of multiple frames in order to exploit inter-frame texture re-use and save bandwidth. By processing multiple frames in parallel textures are fetched once every two frames instead of being fetched in a frame basis as in conventional GPUs. PFR provides 23.8% memory bandwidth savings on average in our set of Android games, that result in 12% speedup and 20.1% energy savings. Finally, we improve PFR by introducing a hardware memoization system on top. We analyze the redundancy in mobile games and find that more than 38% of the Fragment Program executions are redundant on average. We thus propose a task-level hardware-based memoization system that provides 15% speedup and 12% energy savings on average over a PFR-enabled GPU.El diseño de las GPUs (Graphics Procesing Units) móviles se centra fundamentalmente en el ahorro energético. Los smartphones y las tabletas son dispositivos alimentados mediante baterías y, por lo tanto, cualquier tipo de renderizado debe utilizar la menor cantidad de energía posible. Mejorar la eficiencia energética de las GPUs móviles será absolutamente necesario para alcanzar el rendimiento requirido para satisfacer las expectativas de los usuarios, sin reducir el tiempo de vida de la batería. El primer paso para optimizar el consumo energético consiste en identificar qué componentes son los principales consumidores de la batería. Estudios anteriores han identificado al banco de registros y a los accessos a memoria principal como las mayores fuentes de consumo energético en una GPU. El propósito de esta tesis es estudiar las características de los procesadores gráficos móviles y de las aplicaciones móviles con el objetivo de proponer distintas técnicas de ahorro energético. En primer lugar, la investigación se centra en desarrollar métodos energéticamente eficientes para ocultar la latencia de la memoria principal. El resultado de la investigación es una arquitectura desacoplada para los Fragment Processors de la GPU. Los resultados experimentales utilizando un simulador de ciclo y distintos juegos de Android muestran que una arquitectura desacoplada, combinada con un nivel de multithreading moderado, proporciona la solución más eficiente desde el punto de vista energético para ocultar la latencia de la memoria prinicipal. Más específicamente, la arquitectura desacoplada con sólo 4 SIMD threads/processor es capaz de alcanzar el 97% del rendimiento de una GPU más grande con 16 SIMD threads/processor, al tiempo que se reduce el consumo energético en un 20.5%. En segundo lugar, el trabajo de investigación se centró en optimizar el ancho de banda en una GPU móvil. Se realizó un estudio del uso del ancho de banda en distintos juegos de Android y se observó que la mayor parte del ancho de banda se utiliza para leer texturas. Además, se observó que frames consecutivos comparten una gran parte de las texturas. Sin embargo, la GPU no puede capturar el reuso de texturas entre frames dado que el tamaño de las texturas utilizadas por un frame es mucho mayor que la caché de segundo nivel. Basándose en este análisis, se desarrolló Parallel Frame Rendering (PFR), una técnica que solapa el procesado de multiples frames consecutivos con el objetivo de explotar el reuso de texturas entre frames y ahorrar así ancho de bando. Al procesar múltiples frames en paralelo las texturas se leen de memoria principal una vez cada dos frames en lugar de leerse en cada frame como sucede en una GPU convencional. PFR proporciona un ahorro del 23.8% en ancho de banda en promedio para distintos juegos de Android, este ahorro de ancho de banda redunda en un incremento del rendimiento del 12% y un ahorro energético del 20.1%. Por último, se mejoró PFR introduciendo un sistema hardware capaz de evitar cómputos redundantes. Un análisis de distintos juegos de Android reveló que más de un 38% de las ejecuciones del Fragment Program eran redundantes en promedio. Así pues, se propuso un sistema hardware capaz de identificar y eliminar parte de los cómputos y accessos a memoria redundantes, dicho sistema proporciona un incremento del rendimiento del 15% y un ahorro energético del 12% en promedio con respecto a una GPU móvil basada en PFR

High-performance and hardware-aware computing: proceedings of the first International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC\u2708)

The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the full hardware potential if all features on all levels are taken into account in a holistic approach