high performance computing on graphics processing units: hgpu.org

Matching image features between an image and a map of landmarks is usually a time consuming process in mobile robot localization or Simultaneous Localisation And Mapping algorithms. The main problem is being able to match features in spite of viewpoint changes. Methods based on interest point descriptors such as SIFT have been implemented on GPUs […]

Dynamic Tubular Grids (DT-Grids) are designed to encode gridaligned data in level-set simulations. While they are extremely efficient for storing sparse volumetric data, they require logarithmic time for random access. We demonstrate that DT-Grids can be used to efficiently render sparse volumetric data that would otherwise not be able to fit in texture memory. For […]

We present an introduction to the field of GPU accelerated computer vision by examining several projects that provide the framework for researchers and developers to tap into the computational power of Graphics Processing Units (GPU). Our goal is to identify the tools and areas where GPU acceleration can provide the highest performance increases in computer […]

The future of high-performance computing is likely to rely on the ability to efficiently exploit huge amounts of parallelism. One way of taking advantage of this parallelism is to formulate problems as “embarrassingly parallel” Monte-Carlo simulations, which allow applications to achieve a linear speedup over multiple computational nodes, without requiring a super-linear increase in inter-node […]

Random number generation is a very important operation in computational science e.g. in Monte Carlo simulations methods. It is also a computationally intensive operation especially for high quality random number generation. In this paper, we present the design and implementation of a parallel implementation of one of the most widely used random number generators, namely […]

NVIDIA have released a new platform (CUDA) for general purpose computing on their graphical processing units (GPU). This paper evaluates use of this platform for statistical machine learning applications. The transfer rates to and from the GPU are measured, as is the performance of matrix vector operations on the GPU. An implementation of a sparse […]

This paper presents an approach that allows for performing regression on large data sets in reasonable time. The main component of the approach consists in speeding up the slowest operation of the used algorithm by running it on the Graphics Processing Unit (GPU) of the video card, instead of the processor (CPU). The experiments show […]

Desktop software developers interest in graphics hardware is increasing as a result of modern graphics cards’ capabilities to act as compute devices that augment the main processor. This capability means parallel computing is no longer a dedicated task for the CPU. A trend toward heterogeneous computing combines the main processor and graphics processing unit (GPU). […]

We propose a parallel Mean Shift (MS) tracking algorithm on Graphics Processing Unit (GPU) using Compute Unified Device Architecture (CUDA). Traditional MS algorithm uses a large number of color histogram, say typically 16x16x16, which makes parallel implementation infeasible. We thus employ K-Means clustering to partition the object color space that enables us to represent color […]

The shader in the GPU are widely used to model coastal terrain, but the created terrain are of great similarity and unable to embody the differences of coastal features. To overcome the above disadvantage, we present a new modeling method for created terrain based on sketch map. Through specifying the coastal features type, the proposed […]

This study is devoted to exploring possible applications of GPU technology for acceleration of the database access. We use the n-gram based approximate text search engine as a test bed for GPU based acceleration algorithms. Two solutions – hybrid CPU/GPU and pure GPU algorithms for query processing are studied and compared with the baseline CPU […]

Interactive isosurface extraction has recently become possible through successful efforts to map algorithms such as Marching Cubes (MC) and Marching Tetrahedra (MT) to modern Graphics Processing Unit (GPU) architectures. Other isosurfacing algorithms, however, are not so easily portable to GPUs, either because they involve more complex operations or because they are not based on discrete […]