Practical global illumination for interactive particle visualization

ManuscriptParticle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualizations of the resulting state will communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We present two algorithms targeting upcoming, highly parallel multicore desktop systems to enable interactive navigation and exploration of large particle datasets with global illumination effects. Monte Carlo path tracing and texture mapping are used to capture computationally expensive illumination effects such as soft shadows and diffuse interreflection. The first approach is based on precomputation of luminance textures and removes expensive illumination calculations from the interactive rendering pipeline. The second approach is based on dynamic luminance texture generation and decouples interactive rendering from the computation of global illumination effects. These algorithms provide visual cues that enhance the ability to perform analysis and feature detection tasks while interrogating the data at interactive rates. We explore the performance of these algorithms and demonstrate their effectiveness using several large datasets

Efficient Object-Based Hierarchical Radiosity Methods

The efficient generation of photorealistic images is one of the main subjects in the field of computer graphics. In contrast to simple image generation which is directly supported by standard 3D graphics hardware, photorealistic image synthesis strongly adheres to the physics describing the flow of light in a given environment. By simulating the energy flow in a 3D scene global effects like shadows and inter-reflections can be rendered accurately. The hierarchical radiosity method is one way of computing the global illumination in a scene. Due to its limitation to purely diffuse surfaces solutions computed by this method are view independent and can be examined in real-time walkthroughs. Additionally, the physically based algorithm makes it well suited for lighting design and architectural visualization. The focus of this thesis is the application of object-oriented methods to the radiosity problem. By consequently keeping and using object information throughout all stages of the algorithms several contributions to the field of radiosity rendering could be made. By introducing a new meshing scheme, it is shown how curved objects can be treated efficiently by hierarchical radiosity algorithms. Using the same paradigm the radiosity computation can be distributed in a network of computers. A parallel implementation is presented that minimizes communication costs while obtaining an efficient speedup. Radiosity solutions for very large scenes became possible by the use of clustering algorithms. Groups of objects are combined to clusters to simulate the energy exchange on a higher abstraction level. It is shown how the clustering technique can be improved without loss in image quality by applying the same data-structure for both, the visibility computations and the efficient radiosity simulation.Eines der Schwerpunktthemen in der Computergraphik ist die effiziente Erzeugung von fotorealistischen Bildern. Im Gegensatz zur einfachen Bilderzeugung, die bereits durch gaengige 3D-Grafikhardware unterstuetzt wird, gehorcht die fotorealistische Bildsynthese physikalischen Gesetzen, die die Lichtausbreitung innerhalb einer bestimmten Umgebung beschreiben. Durch die Simulation der Energieausbreitung in einer dreidimensionalen Szene koennen globale Effekte wie Schatten und mehrfache Reflektionen wirklichkeitstreu dargestellt werden. Die hierarchische Radiositymethode (Hierarchical Radiosity) ist eine Moeglichkeit, um die globale Beleuchtung innerhalb einer Szene zu berechnen. Da diese Methode auf die Verwendung von rein diffus reflektierenden Oberflaechen beschraenkt ist, sind damit errechnete Loesungen blickwinkelunabhaengig und lassen sich in Echtzeit am Bildschirm durchwandern. Zudem ist dieser Algorithmus aufgrund der verwendeten physikalischen Grundlagen sehr gut zur Beleuchtungssimulation und Architekturvisualisierung geeignet. Den Schwerpunkt dieser Doktorarbeit stellt die Anwendung objektbasierter Methoden auf das Radiosityproblem dar. Durch konsequente Ausnutzung von Objektinformationen waehrend aller Berechnungsschritte konnten verschiedene Verbesserungen im Rahmen der hierarchischen Radiositymethode erzielt werden. Gekruemmte Objekte koennen aufgrund eines neuen Flaechenunterteilungsverfahrens nun effizient durch den hierarchischen Radiosityalgorithmus dargestellt werden. Dieses Verfahren ermoeglicht ebenso eine effiziente Parallelisierung des hierarchischen Radiosityalgorithmus. Es wird ein parallele Implementierung vorgestellt, die unter Minimierung der Kommunikationskosten eine effiziente Geschwindigkeitssteigerung erzielt. Radiosityberechnungen fuer sehr grosse Szenen sind nur durch Verwendung sogenannter Clustering-Algorithmen moeglich. Dabei werden Gruppen von Objekten zu Clustern kombiniert um den Energieaustausch zwischen Oberflaechen stellvertretend auf einem hoeheren Abstraktionsniveau durchzufuehren. Durch Verwendung derselben Datenstruktur fuer Sichtbarkeitsberechnungen und fuer die Steuerung der Radiositysimulation wird gezeigt, wie das Clusteringverfahren ohne Qualitaetsverluste verbessert werden kann

Técnicas de altas prestaciones para métodos de iluminación global

[Resumen] El gran interés en los métodos de iluminación global se debe a sus múltiples aplicaciones y al realismo de sus imágenes resultantes. La investigación presentada en esta memoria se centra en mejorar computacionalmente el algoritmo de radiosidad, planteando estrategias tanto para métodos determinísticos como estocásticos. Respecto de los métodos determinísticos, se expondrán nuestras implementaciones en un sistema distribuido del algoritmo de radiosidad progresiva, utilizando el paradigma de paso de mensajes. Estas implementaciones están basadas en la división de la escena de una manera uniforme o no uniforme. Además, se usa la técnica de las máscaras de visibilidad para el cálculo de visibilidad entre elementos de distintos subescenas. También se demuestra que estas metodologías pueden reducir el tiempo de ejecución secuencial. Relativo a las soluciones estocásticas, presentamos dos implementaciones del método de relajación estocástica de Monte Carlo para radiosidad: en un sistema distribuido y en una Graphics Processing Unit (GPU). La primera se basa en tres técnicas: partición de la escena, empaquetamiento de rayos y determinación distribuida del fin de iteración. En la implementación GPU, además de la partición de la escena se empleó la simplificación de la malla de elementos y una organización eficiente de la ejecución de las tareas.[Resumo] O grande interese nos métodos de iluminación global débese ás súas múltiples aplicacións e ao realismo das súas imaxes resultantes. A investigación presentada nesta memoria céntrase en mellorar computacionalmente o algoritmo de radiosidade, formulando estratexias tanto para métodos determinísticos como estocásticos. Respecto dos métodos determinísticos, exporanse as nosas implementacións nun sistema distribuído do algoritmo de radiosidade progresiva, utilizando o paradigma de paso de mensaxes. Estas implementacións están baseadas na división da escena dunha maneira uniforme ou non uniforme. Ademais, úsase a técnica das máscaras de visibilidade para o cálculo de visibilidade entre elementos de distintas subescenas. Tamén se demostra que estas metodoloxías poden reducir o tempo de execución secuencial. Relativo as solucións estocásticas, presentamos dúas implementacións do método de relaxación estocástica de Monte Carlo para radiosidade: nun sistema distribuído e nunha Graphics Processing Unit (GPU). A primeira baséase en tres técnicas: partición da escena, empaquetamento de raios e determinación distribuída do fin de iteración. Na implementación GPU, ademais da partición da escena empregouse a simplificación da malla de elementos e unha organización eficiente da execución das tarefas.[Abstract] The great interest in global illumination methods is due to their multiple applications and the realism of the resulting images. The research presented in the present thesis focuses on computationally improving the radiosity algorithm, proposing strategies for both deterministic and stochastic approaches. For deterministic approaches, our implementations of the progressive radiosity algorithm will be demonstrated in a distributed system , using the message passing paradigm. These implementations are based on the partitioning of the scene in a uniform or non uniform manner. Furthermore, the technique of visibility masks is employed to calculate the visibility between elements in different subscenes. It is also shown that these methods are capable of reducing the sequential execution time. With regard to stochastic solutions, we present two implementations of the stochastic relaxation method for Monte Carlo radiosity: in a distributed system and in a Graphics Processing Unit (GPU). The first is based on three techniques: partition of the scene, ray packing strategy and distributed testing of the end of each iteration. In the GPU implementation, as well as the partition of the scene a simplified mesh of the elements was used along with an efficient thread scheduling

On thermal sensor calibration and software techniques for many-core thermal management

The high power density of a many-core processor results in increased temperature which negatively impacts system reliability and performance. Dynamic thermal management applies thermal-aware techniques at run time to avoid overheating using temperature information collected from on-chip thermal sensors. Temperature sensing and thermal control schemes are two critical technologies for successfully maintaining thermal safety. In this dissertation, on-line thermal sensor calibration schemes are developed to provide accurate temperature information. Software-based dynamic thermal management techniques are proposed using calibrated thermal sensors. Due to process variation and silicon aging, on-chip thermal sensors require periodic calibration before use in DTM. However, the calibration cost for thermal sensors can be prohibitively high as the number of on-chip sensors increases. Linear models which are suitable for on-line calculation are employed to estimate temperatures at multiple sensor locations using performance counters. The estimated temperature and the actual sensor thermal profile show a very high similarity with correlation coefficient ~0.9 for SPLASH2 and SPEC2000 benchmarks. A calibration approach is proposed to combine potentially inaccurate temperature values obtained from two sources: thermal sensor readings and temperature estimations. A data fusion strategy based on Bayesian inference, which combines information from these two sources, is demonstrated. The result shows the strategy can effectively recalibrate sensor readings in response to inaccuracies caused by process variation and environmental noise. The average absolute error of the corrected sensor temperature readings is A dynamic task allocation strategy is proposed to address localized overheating in many-core systems. Our approach employs reinforcement learning, a dynamic machine learning algorithm that performs task allocation based on current temperatures and a prediction regarding which assignment will minimize the peak temperature. Our results show that the proposed technique is fast (scheduling performed in \u3c1 \u3ems) and can efficiently reduce peak temperature by up to 8 degree C in a 49-core processor (6% on average) versus a leading competing task allocation approach for a series of SPLASH-2 benchmarks. Reinforcement learning has also been applied to 3D integrated circuits to allocate tasks with thermal awareness

Visual Computing Tools for Studying Micro-scale Diffusion

In this dissertation, we present novel visual computing tools and techniques to facilitate the exploration, simulation, and visualization of micro-scale diffusion. Our research builds upon the latest advances in visualization, high-performance computing, medical imaging, and human perception. We validate our research using the driving applications of nano-assembly and diffusion kurtosis imaging (DKI). In both of these applications, diffusion plays a central role. In the former it mediates the process of transporting micron-sized particles through moving lasers, and in the latter it conveys brain micro-geometry. Nanocomponent-based devices, such as bio-sensors, electronic components, photonic devices, solar cells, and batteries, are expected to revolutionize health care, energy, communications, and the computing industry. However, in order to build such useful devices, nanoscale components need to be properly assembled together. We have developed a hybrid CPU/GPU-based computing tool to understand complex interactions between lasers, optical beads, and the suspension medium. We demonstrate how a high-performance visual computing tool can be used to accelerate an optical tweezers simulation to compute the force applied by a laser onto micro particles and study shadowing (refraction) behavior. This represents the first steps toward building a real-time nano-assembly planning system. A challenge in building such a system, however, is that optical tweezers systems typically lack stereo depth cues. We have developed a visual tool to provide an enhanced perception of a scene's 3D structure using the kinetic depth effect. The design of our tool has been informed by user studies of stereo perception using the kinetic-depth effect on monocular displays. Diffusion kurtosis imaging is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared with the traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injuries, where DTI is often found to be inadequate. However, the highly detailed spatio-angular fields in DKI datasets present a special challenge for visualization. Traditional techniques that use glyphs are often inadequate for expressing subtle changes in the DKI fields. In this dissertation, we outline a systematic way to manage, analyze, and visualize spatio-angular fields using spherical harmonics lighting functions to facilitate insights into the micro-structural properties of the brain