Working With Incremental Spatial Data During Parallel (GPU) Computation

Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data stored within a fixed radius of the search origin. The work within this thesis answers the question: What techniques can be used for improving the performance of FRNN searches during complex system simulations on Graphics Processing Units (GPUs)? It is generally agreed that Uniform Spatial Partitioning (USP) is the most suitable data structure for providing FRNN search on GPUs. However, due to the architectural complexities of GPUs, the performance is constrained such that FRNN search remains one of the most expensive common stages between complex systems models. Existing innovations to USP highlight a need to take advantage of recent GPU advances, reducing the levels of divergence and limiting redundant memory accesses as viable routes to improve the performance of FRNN search. This thesis addresses these with three separate optimisations that can be used simultaneously. Experiments have assessed the impact of optimisations to the general case of FRNN search found within complex system simulations and demonstrated their impact in practice when applied to full complex system models. Results presented show the performance of the construction and query stages of FRNN search can be improved by over 2x and 1.3x respectively. These improvements allow complex system simulations to be executed faster, enabling increases in scale and model complexity

Exploiting Graphics Processing Units for Massively Parallel Multi-Dimensional Indexing

Department of Computer EngineeringScientific applications process truly large amounts of multi-dimensional datasets. To efficiently navigate such datasets, various multi-dimensional indexing structures, such as the R-tree, have been extensively studied for the past couple of decades. Since the GPU has emerged as a new cost-effective performance accelerator, now it is common to leverage the massive parallelism of the GPU in various applications such as medical image processing, computational chemistry, and particle physics. However, hierarchical multi-dimensional indexing structures are inherently not well suited for parallel processing because their irregular memory access patterns make it difficult to exploit massive parallelism. Moreover, recursive tree traversal often fails due to the small run-time stack and cache memory in the GPU. First, we propose Massively Parallel Three-phase Scanning (MPTS) R-tree traversal algorithm to avoid the irregular memory access patterns and recursive tree traversal so that the GPU can access tree nodes in a sequential manner. The experimental study shows that MPTS R-tree traversal algorithm consistently outperforms traditional recursive R-Tree search algorithm for multi-dimensional range query processing. Next, we focus on reducing the query response time and extending n-ary multi-dimensional indexing structures - R-tree, so that a large number of GPU threads cooperate to process a single query in parallel. Because the number of submitted concurrent queries in scientific data analysis applications is relatively smaller than that of enterprise database systems and ray tracing in computer graphics. Hence, we propose a novel variant of R-trees Massively Parallel Hilbert R-Tree (MPHR-Tree), which is designed for a novel parallel tree traversal algorithm Massively Parallel Restart Scanning (MPRS). The MPRS algorithm traverses the MPHR-Tree in mostly contiguous memory access patterns without recursion, which offers more chances to optimize the parallel SIMD algorithm. Our extensive experimental results show that the MPRS algorithm outperforms the other stackless tree traversal algorithms, which are designed for efficient ray tracing in computer graphics community. Furthermore, we develop query co-processing scheme that makes use of both the CPU and GPU. In this approach, we store the internal and leaf nodes of upper tree in CPU host memory and GPU device memory, respectively. We let the CPU traverse internal nodes because the conditional branches in hierarchical tree structures often cause a serious warp divergence problem in the GPU. For leaf nodes, the GPU scans a large number of leaf nodes in parallel based on the selection ratio of a given range query. It is well known that the GPU is superior to the CPU for parallel scanning. The experimental results show that our proposed multi-dimensional range query co-processing scheme improves the query response time by up to 12x and query throughput by up to 4x compared to the state-of-the-art GPU tree traversal algorithm.ope

GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP

Full detector simulation was among the largest CPU consumer in all CERN experiment software stacks for the first two runs of the Large Hadron Collider (LHC). In the early 2010's, the projections were that simulation demands would scale linearly with luminosity increase, compensated only partially by an increase of computing resources. The extension of fast simulation approaches to more use cases, covering a larger fraction of the simulation budget, is only part of the solution due to intrinsic precision limitations. The remainder corresponds to speeding-up the simulation software by several factors, which is out of reach using simple optimizations on the current code base. In this context, the GeantV R&D project was launched, aiming to redesign the legacy particle transport codes in order to make them benefit from fine-grained parallelism features such as vectorization, but also from increased code and data locality. This paper presents extensively the results and achievements of this R&D, as well as the conclusions and lessons learnt from the beta prototype.Comment: 34 pages, 26 figures, 24 table

Acceleration of physics simulation engine through OpenCL

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaNowadays, physics simulation is a relevant topic in several domains, from scientific areas like medicine to entertainment purposes such as movie’s effects, computer animation and games. To make easier the production of faster simulations, developers are using physics engines because they provide a variety of features like rigid and deformable body simulation, fluids dynamics and collision detection. Computer game and film industries use increasingly more physics engines in order to introduce realism in their products. In these areas, speed is more important than accuracy and efforts have been made to achieve high performance simulations. Besides faster physical simulation algorithms, GPUs’ performance improvement in the past few years have lead developers to transfers heavy calculation work to these devices instead of doing it in the Central Processing Unit (CPU). Some engines already provide GPU implementations of several key features, particularly on rigid body collision detection. In this work we want to accelerate a feature present in most of the current physics engines: cloth simulation. Since collision detection is one of the major bottlenecks in this kind of simulation,we will focus specifically in improving this phase. To achieve a considerably speed-up we plan to exploit the massive parallelism of the Graphics Processing Unit (GPU) by designing an efficient algorithm using the Open Computing Language (OpenCL) framework. Finally,a study will be made to compare the performance of a sequential CPU approach against the parallel GPU proposed solution

Toward human-like pathfinding with hierarchical approaches and the GPS of the brain theory

Pathfinding for autonomous agents and robots has been traditionally driven by finding optimal paths. Where typically optimality means finding the shortest path between two points in a given environment. However, optimality may not always be strictly necessary. For example, in the case of video games, often computing the paths for non-player characters (NPC) must be done under strict time constraints to guarantee real time simulation. In those cases, performance is more important than finding the shortest path, specially because often a sub-optimal path can be just as convincing from the point of view of the player. When simulating virtual humanoids, pathfinding has also been used with the same goal: finding the shortest path. However, humans very rarely follow precise shortest paths, and thus there are other aspects of human decision making and path planning strategies that should be incorporated in current simulation models. In this thesis we first focus on improving performance optimallity to handle as many virtual agents as possible, and then introduce neuroscience research to propose pathfinding algorithms that attempt to mimic humans in a more realistic manner.In the case of simulating NPCs for video games, one of the main challenges is to compute paths as efficiently as possible for groups of agents. As both the size of the environments and the number of autonomous agents increase, it becomes harder to obtain results in real time under the constraints of memory and computing resources. For this purpose we explored hierarchical approaches for two reasons: (1) they have shown important performance improvements for regular grids and other abstract problems, and (2) humans tend to plan trajectories also following an topbottom abstraction, focusing first on high level location and then refining the path as they move between those high level locations. Therefore, we believe that hierarchical approaches combine the best of our two goals: improving performance for multi-agent pathfinding and achieving more human-like pathfinding. Hierarchical approaches, such as HNA* (Hierarchical A* for Navigation Meshes) can compute paths more efficiently, although only for certain configurations of the hierarchy. For other configurations, the method suffers from a bottleneck in the step that connects the Start and Goal positions with the hierarchy. This bottleneck can drop performance drastically.In this thesis we present different approaches to solve the HNA* bottleneck and thus obtain a performance boost for all hierarchical configurations. The first method relies on further memory storage, and the second one uses parallelism on the GPU. Our comparative evaluation shows that both approaches offer speed-ups as high as 9x faster than A*, and show no limitations based on hierarchical configuration. Then we further exploit the potential of CUDA parallelism, to extend our implementation to HNA* for multi-agent path finding. Our method can now compute paths for over 500K agents simultaneously in real-time, with speed-ups above 15x faster than a parallel multi-agent implementation using A*. We then focus on studying neurosience research to learn about the way that humans build mental maps, in order to propose novel algorithms that take those finding into account when simulating virtual humans. We propose a novel algorithm for path finding that is inspired by neuroscience research on how the brain learns and builds cognitive maps. Our method represents the space as a hexagonal grid, based on the GPS of the brain theory, and fires memory cells as counters. Our path finder then combines a method for exploring unknown environments while building such a cognitive map, with an A* search using a modified heuristic that takes into account the GPS of the brain cognitive map.El problema de Pathfinding para agentes autónomos o robots, ha consistido tradicionalmente en encontrar un camino óptimo, donde por óptimo se entiende el camino de distancia mínima entre dos posiciones de un entorno. Sin embargo, en ocasiones puede que no sea estrictamente necesario encontrar una solución óptima. Para ofrecer la simulación de multitudes de agentes autónomos moviéndose en tiempo real, es necesario calcular sus trayectorias bajo condiciones estrictas de tiempo de computación, pero no es fundamental que las soluciones sean las de distancia mínima ya que, con frecuencia, el observador no apreciará la diferencia entre un camino óptimo y un sub-óptimo. Por tanto, suele ser suficiente con que la solución encontrada sea visualmente creíble para el observado. Cuando se simulan humanoides virtuales en aplicaciones de realidad virtual que requieren avatares que simulen el comportamiento de los humanos, se tiende a emplear los mismos algoritmos que en video juegos, con el objetivo de encontrar caminos de distancia mínima. Pero si realmente queremos que los avatares imiten el comportamiento humano, tenemos que tener en cuenta que, en el mundo real, los humanos rara vez seguimos precisamente el camino más corto, y por tanto se deben considerar otros aspectos que influyen en la toma de decisiones de los humanos y la selección de rutas en el mundo real. En esta tesis nos centraremos primero en mejorar el rendimiento de la búsqueda de caminos para poder simular grandes números de humanoides virtuales autónomos, y a continuación introduciremos conceptos de investigación con mamíferos en neurociencia, para proponer soluciones al problema de pathfinding que intenten imitar con mayor realismo, el modo en el que los humanos navegan el entorno que les rodea. A medida que aumentan tanto el tamaño de los entornos virtuales como el número de agentes autónomos, resulta más difícil obtener soluciones en tiempo real, debido a las limitaciones de memoria y recursos informáticos. Para resolver este problema, en esta tesis exploramos enfoques jerárquicos porque consideramos que combinan dos objetivos fundamentales: mejorar el rendimiento en la búsqueda de caminos para multitudes de agentes y lograr una búsqueda de caminos similar a la de los humanos. El primer método presentado en esta tesis se basa en mejorar el rendimiento del algoritmo HNA* (Hierarchical A* for Navigation Meshes) incrementando almacenamiento de datos en memoria, y el segundo utiliza el paralelismo para mejorar drásticamente el rendimiento. La evaluación cuantitativa realizada en esta tesis, muestra que ambos enfoques ofrecen aceleraciones que pueden llegar a ser hasta 9 veces más rápidas que el algoritmo A* y no presentan limitaciones debidas a la configuración jerárquica. A continuación, aprovechamos aún más el potencial del paralelismo ofrecido por CUDA para extender nuestra implementación de HNA* a sistemas multi-agentes. Nuestro método permite calcular caminos simultáneamente y en tiempo real para más de 500.000 agentes, con una aceleración superior a 15 veces la obtenida por una implementación paralela del algoritmo A*. Por último, en esta tesis nos hemos centrado en estudiar los últimos avances realizados en el ámbito de la neurociencia, para comprender la manera en la que los humanos construyen mapas mentales y poder así proponer nuevos algoritmos que imiten de forma más real el modo en el que navegamos los humanos. Nuestro método representa el espacio como una red hexagonal, basada en la distribución de ¿place cells¿ existente en el cerebro, e imita las activaciones neuronales como contadores en dichas celdas. Nuestro buscador de rutas combina un método para explorar entornos desconocidos mientras construye un mapa cognitivo hexagonal, utilizando una búsqueda A* con una nueva heurística adaptada al mapa cognitivo del cerebro y sus contadores

Generalized database index structures on massively parallel processor architectures

Height-balanced search trees are ubiquitous in database management systems as well as in other applications that require efficient access methods in order to identify entries in large data volumes. They can be configured with various strategies for structuring the search space for a given data set and for pruning it when different kinds of search queries are answered. In order to facilitate the development of application-specific tree variants, index frameworks, such as GiST, exist that provide a reusable library of commonly shared tree management functionality. By specializing internal data organization strategies, the framework can be customized to create an index that is efficient for an application's data access characteristics. Because the majority of the framework's code can be reused development and testing efforts are significantly lower, compared to an implementation from scratch. However, none of the existing frameworks supports the execution of index operations on massively parallel processor architectures, such as GPUs. Enabling the use of such processors for generalized index frameworks is the goal of this thesis. By compiling state-of-the-art techniques from a wide range of CPU- and GPU-optimized indexes, a GiST extension is developed that abstracts the physical execution aspect of generic, tree-based search queries. Tree traversals are broken-down into vectorized processing primitives that can be scheduled to one of the available (co-)processors for execution. Further, a CPU-based implementation is provided as well as a new GPU-based algorithm that, unlike prior art in this area, does not require that the index is fully stored inside a GPU's main memory buffer. The applicability of the extended framework is assessed for image rendering engines and, based on microbenchmarks, the parallelized algorithm performance is compared for different CPU and GPU generations. It will be shown that cases exist, where the GPU clearly outperforms the CPU and vice versa. In order to leverage the strengths of each processor type, an adaptive scheduler is presented that can be calibrated to schedule index operations to the best-fitting device in a hybrid system. With the help of a tree traversal simulation different scheduling strategies are evaluated and it will be shown that the adaptive scheduler can be used to make near-optimal decisions.Suchbäume sind allgegenwärtig in Datenbanksystemen und anderen Anwendungen, die eine effiziente Möglichkeit benötigen um in großen Datensätzen nach Einträgen zu suchen, die bestimmte Suchkriterien erfüllen. Sie können mit verschiedenen Strategien konfiguriert werden um den Suchraum zu strukturieren und die für ein Suchergebnis irrelevante Bereiche von der Bearbeitung auszuschließen. Die Entwicklung von anwendungsspezifischen Indexen wird durch Frameworks wie GiST unterstützt. Jedoch unterstützt keines der heute bereits existierenden Frameworks die Verwendung von hochgradig parallelen Prozessorarchitekturen wie GPUs. Solche Prozessoren für generische Index Frameworks nutzbar zu machen, ist Ziel dieser Arbeit. Dazu werden Techniken aus verschiedensten CPU- und GPU-optimierten Indexen analysiert und für die Entwicklung einer GiST-Erweiterung verwendet, welche die für eine Suche in Suchbäumen nötigen Berechnungen abstrahiert. Traversierungsoperationen werden dabei auf vektorisierte Primitive abgebildet, die auf parallelen Prozessoren implementiert werden können. Die Verwendung dieser Erweiterung wird beispielhaft an einem CPU Algorithmus demonstriert. Weiterhin wird ein neuer GPU-basierter Algorithmus vorgestellt, der im Vergleich zu bisherigen Verfahren, ein dynamisches Nachladen der Index Daten in den Hauptspeicher der GPU unterstützt. Die Praktikabilität des erweiterten Frameworks wird am Beispiel von Anwendungen aus der Computergrafik untersucht und die Performanz der verwendeten Algorithmen mit Hilfe eines Benchmarks auf verschiedenen CPU- und GPU-Modellen analysiert. Dabei wird gezeigt, unter welchen Bedingungen die parallele GPU-basierte Ausführung schneller ist als die CPU-basierte Variante - und umgekehrt. Um die Stärken beider Prozessortypen in einem hybriden System ausnutzen zu können, wird ein Scheduler entwickelt, der nach einer Kalibrierungsphase für eine gegebene Operation den geeignetsten Prozessor wählen kann. Mit Hilfe eines Simulators für Baumtraversierungen werden verschiedenste Scheduling Strategien verglichen. Dabei wird gezeigt, dass die Entscheidungen des Schedulers kaum vom Optimum abweichen und, abhängig von der simulierten Last, die erzielbaren Durchsätze für die parallele Ausführung mehrerer Suchoperationen durch hybrides Scheduling um eine Größenordnung und mehr erhöht werden können

Irregular alignment of arbitrarily long DNA sequences on GPU

The use of Graphics Processing Units to accelerate computational applications is increasingly being adopted due to its affordability, flexibility and performance. However, achieving top performance comes at the price of restricted data-parallelism models. In the case of sequence alignment, most GPU-based approaches focus on accelerating the Smith-Waterman dynamic programming algorithm due to its regularity. Nevertheless, because of its quadratic complexity, it becomes impractical when comparing long sequences, and therefore heuristic methods are required to reduce the search space. We present GPUGECKO, a CUDA implementation for the sequential, seed-and-extend sequence-comparison algorithm, GECKO. Our proposal includes optimized kernels based on collective operations capable of producing arbitrarily long alignments while dealing with heterogeneous and unpredictable load. Contrary to other state-of-the-art methods, GPUGECKO employs a batching mechanism that prevents memory exhaustion by not requiring to fit all alignments at once into the device memory, therefore enabling to run massive comparisons exhaustively with improved sensitivity while also providing up to 6x average speedup w.r.t. the CUDA acceleration of BLASTN.Funding for open access publishing: Universidad Málaga/CBUA /// This work has been partially supported by the European project ELIXIR-EXCELERATE (grant no. 676559), the Spanish national project Plataforma de Recursos Biomoleculares y Bioinformáticos (ISCIII-PT13.0001.0012 and ISCIII-PT17.0009.0022), the Fondo Europeo de Desarrollo Regional (UMA18-FEDERJA-156, UMA20-FEDERJA-059), the Junta de Andalucía (P18-FR-3130), the Instituto de Investigación Biomédica de Málaga IBIMA and the University of Málaga

Enhancing detailed haptic relief for real-time interaction

The present document exposes a different approach for haptic rendering, defined as the simulation of force interactions to reproduce the sensation of surface relief in dense models. Current research shows open issues in timely haptic interaction involving large meshes, with several problems affecting performance and fidelity, and without a dominant technique to treat these issues properly. Relying in pure geometric collisions when rendering highly dense mesh models (hundreds of thousands of triangles) sensibly degrades haptic rates due to the sheer number of collisions that must be tracked between the mesh's faces and a haptic probe. Several bottlenecks were identified in order to enhance haptic performance: software architecture and data structures, collision detection, and accurate rendering of surface relief. To account for overall software architecture and data structures, it was derived a complete component framework for transforming standalone VR applications into full-fledged multi-threaded Collaborative Virtual Reality Environments (CVREs), after characterizing existing implementations into a feature-rich superset. Enhancements include: a scalable arbitrated peer-to-peer topology for scene sharing; multi-threaded components for graphics rendering, user interaction and network communications; a collaborative user interface model for session handling; and interchangeable user roles with multi-camera perspectives, avatar awareness and shared annotations. We validate the framework by converting the existing ALICE VR Navigator into a complete CVRE, showing good performance in collaborative manipulation of complex models. To specifically address collision detection computation, we derive a conformal algebra treatment for collisions among points, segments, areas, and volumes, based on collision detection in conformal R{4,1} (5D) space, and implemented in GPU for faster parallel queries. Results show orders of magnitude time reductions in collisions computations, allowing interactive rates. Finally, the main core of the research is the haptic rendering of surface mesostructure in large meshes. Initially, a method for surface haptic rendering was proposed, using image-based Hybrid Rugosity Mesostructures (HRMs) of per-face heightfield displacements and normalmaps layered on top of a simpler mesh, adding greater surface detail than actually present. Haptic perception is achieved modulating the haptic probe's force response using the HRM coat. A usability testbed framework was built to measure experimental performance with a common set tests, meshes and HRMs. Trial results show the goodness of the proposed technique, rendering accurate 3D surface detail at high sampling rates. This local per-face method is extended into a fast global approach for haptic rendering, building a mesostructure-based atlas of depth/normal textures (HyRMA), computed out of surface differences of the same mesh object at two different resolutions: original and simplified. For each triangle in the simplified mesh, an irregular prism is considered defined by the triangle's vertices and their normals. This prism completely covers the original mesh relief over the triangle. Depth distances and surfaces normals within each prism are warped from object volume space to orthogonal tangent space, by means of a novel and fast method for computing barycentric coordinates at the prism, and storing normals and relief in a sorted atlas. Haptic rendering is effected by colliding the probe against the atlas, and effecting a modulated force response at the haptic probe. The method is validated numerically, statistically and perceptually in user testing controlled trials, achieving accurate haptic sensation of large meshes' fine features at interactive rendering rates, with some minute loss of mesostructure detail.En aquesta tesi es presenta un novedós enfocament per a la percepció hàptica del relleu de models virtuals complexes mitjançant la simulació de les forces d'interacció entre la superfície i un element de contacte. La proposta contribueix a l'estat de l'art de la recerca en aquesta àrea incrementant l'eficiència i la fidelitat de la interacció hàptica amb grans malles de triangles. La detecció de col·lisions amb malles denses (centenars de milers de triangles) limita la velocitat de resposta hàptica degut al gran nombre d'avaluacions d'intersecció cara-dispositiu hàptic que s'han de realitzar. Es van identificar diferents alternatives per a incrementar el rendiment hàptic: arquitectures de software i estructures de dades específiques, algorismes de detecció de col·lisions i reproducció hàptica de relleu superficial. En aquesta tesi es presenten contribucions en alguns d'aquests aspectes. S'ha proposat una estructura completa de components per a transformar aplicacions de Realitat Virtual en Ambients Col·laboratius de Realitat Virtual (CRVEs) multithread en xarxa. L'arquitectura proposada inclou: una topologia escalable punt a punt per a compartir escenes; components multithread per a visualització gràfica, interacció amb usuaris i comunicació en xarxa; un model d'interfície d'usuari col·laboratiu per a la gestió de sessions; i rols intercanviables de l'usuari amb perspectives de múltiples càmeres, presència d'avatars i anotacions compartides. L'estructura s'ha validat convertint el navegador ALICE en un CVRE completament funcional, mostrant un bon rendiment en la manipulació col·laborativa de models complexes. Per a incrementar l'eficiència del càlcul de col·lisions, s'ha proposat un algorisme que treballa en un espai conforme R{4,1} (5D) que permet detectar col·lisions entre punts, segments, triangles i volums. Aquest algorisme s'ha implementat en GPU per obtenir una execució paral·lela més ràpida. Els resultats mostren reduccions en el temps de càlcul de col·lisions permetent interactivitat. Per a la percepció hàptica de malles complexes que modelen objectes rugosos, s'han proposat diferents algorismes i estructures de dades. Les denominades Mesoestructures Híbrides de Rugositat (HRM) permeten substituir els detalls geomètrics d'una cara (rugositats) per dues textures: de normals i d'alçades. La percepció hàptica s'aconsegueix modulant la força de resposta entre el dispositiu hàptic i la HRM. Els tests per avaluar experimentalment l'eficiència del càlcul de col·lisions i la percepció hàptica utilitzant HRM respecte a modelar les rugositats amb geometria, van mostrar que la tècnica proposada va ser encertada, permetent percebre detalls 3D correctes a altes tases de mostreig. El mètode es va estendre per a representar rugositats d'objectes. Es proposa substituir l'objecte per un model simplificat i un atles de mesoestructures en el que s'usen textures de normals i de relleus (HyRMA). Aquest atles s'obté a partir de la diferència en el detall de la superfície entre dos malles del mateix objecte: l'original i la simplificada. A partir d'un triangle de la malla simplificada es construeix un prisma, definit pels vèrtexs del triangle i les seves normals, que engloba el relleu de la malla original sobre el triangle. Les alçades i normals dins del prisma es transformen des de l'espai de volum a l'espai ortogonal tangent, amb mètode novedós i eficient que calcula les coordenades baricèntriques relatives al prisma, per a guardar el mapa de textures transformat en un atles ordenat. La percepció hàptica s'assoleix detectant les col·lisions entre el dispositiu hàptic i l'atles, i modulant la força de resposta d'acord al resultat de la col·lisió. El mètode s'ha validat numèricament, estadística i perceptual en tests amb usuaris, aconseguint una correcta i interactiva sensació tàctil dels objectes simulats mitjançant la mesoestructura de les mallesEn esta tesis se presenta un enfoque novedoso para la percepción háptica del relieve de modelos virtuales complejos mediante la simulación de las fuerzas de interacción entre la superficie y un elemento de contacto. La propuesta contribuye al estado del arte de investigación en este área incrementando la eficiencia y fidelidad de interacción háptica con grandes mallas de triángulos. La detección de colisiones con mallas geométricas densas (cientos de miles de triángulos) limita la velocidad de respuesta háptica debido al elevado número de evaluaciones de intersección cara-dispositivo háptico que deben realizarse. Se identificaron diferentes alternativas para incrementar el rendimiento háptico: arquitecturas de software y estructuras de datos específicas, algoritmos de detección de colisiones y reproducción háptica de relieve superficial. En esta tesis se presentan contribuciones en algunos de estos aspectos. Se ha propuesto una estructura completa de componentes para transformar aplicaciones aisladas de Realidad Virtual en Ambientes Colaborativos de Realidad Virtual (CRVEs) multithread en red. La arquitectura propuesta incluye: una topología escalable punto a punto para compartir escenas; componentes multithread para visualización gráfica, interacción con usuarios y comunicación en red; un modelo de interfaz de usuario colaborativo para la gestión de sesiones; y roles intercambiables del usuario con perspectivas de múltiples cámaras, presencia de avatares y anotaciones compartidas. La estructura se ha validado convirtiendo el navegador ALICE en un CVRE completamente funcional, mostrando un buen rendimiento en la manipulación colaborativa de modelos complejos. Para incrementar la eficiencia del cálculo de colisiones, se ha propuesto un algoritmo que trabaja en un espacio conforme R4,1 (5D) que permite detectar colisiones entre puntos, segmentos, triángulos y volúmenes. Este algoritmo se ha implementado en GPU a efectos de obtener una ejecución paralelamás rápida. Los resultadosmuestran reducciones en el tiempo de cálculo de colisiones permitiendo respuesta interactiva. Para la percepción háptica de mallas complejas que modelan objetos rugosos, se han propuesto diferentes algoritmos y estructuras de datos. Las denominadasMesoestructuras Híbridas de Rugosidad (HRM) permiten substituir los detalles geométricos de una cara (rugosidades) por una textura de normales y otra de alturas. La percepción háptica se consigue modulando la fuerza de respuesta entre el dispositivo háptico y la HRM. Los tests realizados para evaluar experimentalmente la eficiencia del cálculo de colisiones y la percepción háptica utilizando HRM respecto a modelar las rugosidades con geometría, mostraron que la técnica propuesta fue acertada, permitiendo percibir detalles 3D correctos a altas tasas de muestreo. Este método anterior es extendido a un procedimiento global para representar rugosidades de objetos. Para hacerlo se propone sustituir el objeto por un modelo simplificado y un atlas de mesostructuras usando texturas de normales y relieves (HyRMA). Este atlas se obtiene de la diferencia en detalle de superficie entre dos mallas del mismo objeto: la original y la simplificada. A partir de un triángulo de la malla simplificada se construye un prisma definido por los vértices del triángulo a lo largo de sus normales, que engloba completamente el relieve de la malla original sobre este triángulo. Las alturas y normales dentro de cada prisma se transforman del espacio de volumen al espacio ortoganal tangente, usando un método novedoso y eficiente que calcula las coordenadas baricéntricas relativas a cada prisma para guardar el mapa de texturas transformado en un atlas ordenado. La percepción háptica se consigue detectando directamente las colisiones entre el dispositivo háptico y el atlas, y modulando la fuerza de respuesta de acuerdo al resultado de la colisión. El procedmiento se ha validado numérica, estadística y perceptualmente en ensayos con usuarios, consiguiendo a tasas interactivas la correcta sensación táctil de los objetos simulados mediante la mesoestructura de las mallas, con alguna pérdida muy puntual de detall