Métodos de acceso métrico-espaciales

Tradicionalmente, los datos que contienen las bases de datos están estructurados en tuplas y son comparables a través de operadores relacionales. Para acelerar este tipo de consultas existen índices eficientes, tales como B+-Tree. Sin embargo, cada vez es más importante el almacenamiento de objetos no estructurados, que no se pueden comparar por igualdad, para los cuales dichos índices no son aplicables. Algunos ejemplos son: imágenes (rostros, radiografías, pinturas, marcas, paisajes, etc.), texto plano y semiestructurado (documentos, archivos XML, etc.), sonidos (música, voz, etc.) y objetos espaciales (ciudades, rutas, puntos de interés, etc.). Ante esta situación, han surgido otras formas de consultas, siendo algunas de las más importantes las espaciales y las por similitud. Un aspecto no estudiado aún, es la combinación de estos dos tipos de búsqueda, e.g. "encontrar objetos similares a uno dado, ubicados dentro de un área". Estos tipos de consultas son importantes en especial en los Sistemas de Información Geográfica y aún no existen métodos de acceso que los soporten. En este proyecto estudiamos distintos aspectos referidos al procesamiento de consultas métricoespaciales, las funciones de distancia a utilizar, y el uso de paralelismo en GPU para hacer más eficiente el procesamiento de las mismas.Eje: Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Métodos de acceso métrico-espaciales

Tradicionalmente, los datos que contienen las bases de datos están estructurados en tuplas y son comparables a través de operadores relacionales. Para acelerar este tipo de consultas existen índices eficientes, tales como B+-Tree. Sin embargo, cada vez es más importante el almacenamiento de objetos no estructurados, que no se pueden comparar por igualdad, para los cuales dichos índices no son aplicables. Algunos ejemplos son: imágenes (rostros, radiografías, pinturas, marcas, paisajes, etc.), texto plano y semiestructurado (documentos, archivos XML, etc.), sonidos (música, voz, etc.) y objetos espaciales (ciudades, rutas, puntos de interés, etc.). Ante esta situación, han surgido otras formas de consultas, siendo algunas de las más importantes las espaciales y las por similitud. Un aspecto no estudiado aún, es la combinación de estos dos tipos de búsqueda, e.g. "encontrar objetos similares a uno dado, ubicados dentro de un área". Estos tipos de consultas son importantes en especial en los Sistemas de Información Geográfica y aún no existen métodos de acceso que los soporten. En este proyecto estudiamos distintos aspectos referidos al procesamiento de consultas métricoespaciales, las funciones de distancia a utilizar, y el uso de paralelismo en GPU para hacer más eficiente el procesamiento de las mismas.Eje: Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Runtime Adaptive Hybrid Query Engine based on FPGAs

This paper presents the fully integrated hardware-accelerated query engine for large-scale datasets in the context of Semantic Web databases. As queries are typically unknown at design time, a static approach is not feasible and not flexible to cover a wide range of queries at system runtime. Therefore, we introduce a runtime reconfigurable accelerator based on a Field Programmable Gate Array (FPGA), which transparently incorporates with the freely available Semantic Web database LUPOSDATE. At system runtime, the proposed approach dynamically generates an optimized hardware accelerator in terms of an FPGA configuration for each individual query and transparently retrieves the query result to be displayed to the user. During hardware-accelerated execution the host supplies triple data to the FPGA and retrieves the results from the FPGA via PCIe interface. The benefits and limitations are evaluated on large-scale synthetic datasets with up to 260 million triples as well as the widely known Billion Triples Challenge

Image Processing with CUDA

This thesis puts to the test the power of parallel computing on the GPU against the massive computations needed in image processing of large images. The GPU has long been used to accelerate 3D applications. With the advent of high level programmable interfaces, programming to the GPU is simplied and is being used to accelerate a wider class of applications. More specically, this thesis focuses on CUDA as its parallel programming platform. This thesis explores on the possible performance gains that can be achieved by using CUDA on image processing. Two well known algorithms for image blurring and edge detection is used in the experiment. Benchmarks are done between the parallel implementation and the sequential implementation

Bin-Hash Indexing: A Parallel Method for Fast Query Processing

This paper presents a new parallel indexing data structure for answering queries. The index, called Bin-Hash, offers extremely high levels of concurrency, and is therefore well-suited for the emerging commodity of parallel processors, such as multi-cores, cell processors, and general purpose graphics processing units (GPU). The Bin-Hash approach first bins the base data, and then partitions and separately stores the values in each bin as a perfect spatial hash table. To answer a query, we first determine whether or not a record satisfies the query conditions based on the bin boundaries. For the bins with records that can not be resolved, we examine the spatial hash tables. The procedures for examining the bin numbers and the spatial hash tables offer the maximum possible level of concurrency; all records are able to be evaluated by our procedure independently in parallel. Additionally, our Bin-Hash procedures access much smaller amounts of data than similar parallel methods, such as the projection index. This smaller data footprint is critical for certain parallel processors, like GPUs, where memory resources are limited. To demonstrate the effectiveness of Bin-Hash, we implement it on a GPU using the data-parallel programming language CUDA. The concurrency offered by the Bin-Hash index allows us to fully utilize the GPU's massive parallelism in our work; over 12,000 records can be simultaneously evaluated at any one time. We show that our new query processing method is an order of magnitude faster than current state-of-the-art CPU-based indexing technologies. Additionally, we compare our performance to existing GPU-based projection index strategies

Manycore processing of repeated range queries over massive moving objects observations

The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extents of queries and objects are continuously modified over time. To tackle this problem and significantly accelerate query processing we devise a hybrid CPU/GPU pipeline that compresses data output and save query processing work. The devised system relies on an ad-hoc spatial index leading to a problem decomposition that results in a set of independent data-parallel tasks. The index is based on a point-region quadtree space decomposition and allows to tackle effectively a broad range of spatial object distributions, even those very skewed. Also, to deal with the architectural peculiarities and limitations of the GPUs, we adopt non-trivial GPU data structures that avoid the need of locked memory accesses and favour coalesced memory accesses, thus enhancing the overall memory throughput. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated range queries over massive sets of continuously moving objects, characterized by highly skewed spatial distributions. In comparison with state-of-the-art CPU-based implementations, our method highlights significant speedups in the order of 14x-20x, depending on the datasets, even when considering very cheap GPUs

GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons

Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-in-polygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches

GPU-friendly marching cubes.

Xie, Yongming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 77-85).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Isosurfaces --- p.1Chapter 1.2 --- Graphics Processing Unit --- p.2Chapter 1.3 --- Objective --- p.3Chapter 1.4 --- Contribution --- p.3Chapter 1.5 --- Thesis Organization --- p.4Chapter 2 --- Marching Cubes --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- Marching Cubes Algorithm --- p.7Chapter 2.3 --- Triangulated Cube Configuration Table --- p.12Chapter 2.4 --- Summary --- p.16Chapter 3 --- Graphics Processing Unit --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- History of Graphics Processing Unit --- p.19Chapter 3.2.1 --- First Generation GPU --- p.20Chapter 3.2.2 --- Second Generation GPU --- p.20Chapter 3.2.3 --- Third Generation GPU --- p.20Chapter 3.2.4 --- Fourth Generation GPU --- p.21Chapter 3.3 --- The Graphics Pipelining --- p.21Chapter 3.3.1 --- Standard Graphics Pipeline --- p.21Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.23Chapter 3.3.3 --- Vertex Processors --- p.25Chapter 3.3.4 --- Fragment Processors --- p.26Chapter 3.3.5 --- Frame Buffer Operations --- p.28Chapter 3.4 --- GPU CPU Analogy --- p.31Chapter 3.4.1 --- Memory Architecture --- p.31Chapter 3.4.2 --- Processing Model --- p.32Chapter 3.4.3 --- Limitation of GPU --- p.33Chapter 3.4.4 --- Input and Output --- p.34Chapter 3.4.5 --- Data Readback --- p.34Chapter 3.4.6 --- FramebufFer --- p.34Chapter 3.5 --- Summary --- p.35Chapter 4 --- Volume Rendering --- p.37Chapter 4.1 --- Introduction --- p.37Chapter 4.2 --- History of Volume Rendering --- p.38Chapter 4.3 --- Hardware Accelerated Volume Rendering --- p.40Chapter 4.3.1 --- Hardware Acceleration Volume Rendering Methods --- p.41Chapter 4.3.2 --- Proxy Geometry --- p.42Chapter 4.3.3 --- Object-Aligned Slicing --- p.43Chapter 4.3.4 --- View-Aligned Slicing --- p.45Chapter 4.4 --- Summary --- p.48Chapter 5 --- GPU-Friendly Marching Cubes --- p.49Chapter 5.1 --- Introduction --- p.49Chapter 5.2 --- Previous Work --- p.50Chapter 5.3 --- Traditional Method --- p.52Chapter 5.3.1 --- Scalar Volume Data --- p.53Chapter 5.3.2 --- Isosurface Extraction --- p.53Chapter 5.3.3 --- Flow Chart --- p.54Chapter 5.3.4 --- Transparent Isosurfaces --- p.56Chapter 5.4 --- Our Method --- p.56Chapter 5.4.1 --- Cell Selection --- p.59Chapter 5.4.2 --- Vertex Labeling --- p.61Chapter 5.4.3 --- Cell Indexing --- p.62Chapter 5.4.4 --- Interpolation --- p.65Chapter 5.5 --- Rendering Translucent Isosurfaces --- p.67Chapter 5.6 --- Implementation and Results --- p.69Chapter 5.7 --- Summary --- p.74Chapter 6 --- Conclusion --- p.76Bibliography --- p.7