high performance computing on graphics processing units: hgpu.org

Many applications in image processing have high inherent parallelism. FPGAs have shown very high performance in spite of their low operational frequency by fully extracting the parallelism. In recent micro processors, it also becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions, though programmers have to use them explicitly to achieve […]

The interactive visualisation of digital terrain datasets deals with their interrelated issues: quality, time and resources. In this paper a GPU-supported rendering technique is introduced, which finds a tradeoff between these issues. For this we use the projective grid method as the foundation. Even though the method is simple and powerful, its most significant problem […]

We present a stereo algorithm that achieves high quality results while maintaining real-time performance. The key idea is simple: we introduce an adaptive aggregation step in a dynamic-programming (DP) stereo framework. The per-pixel matching cost is aggregated in the vertical direction only. Compared to traditional DP, our approach reduces the typical “streaking” artifacts without the […]

Depth from stereo has traditionally been, and continues to be one of the most actively researched topics in computer vision. Recent development in this area has significantly advanced the state of the art in terms of quality. However, in terms of speed, these best stereo algorithms typically take from several seconds to several minutes to […]

A methodology and its associated algorithms are presented for mapping a novel, field-based vehicular mobility model onto graphical processing unit computational platform for simulating mobility in large-scale road networks. Of particular focus is the achievement of real-time execution, on desktop platforms, of vehicular mobility on road networks comprised of millions of nodes and links, and […]

A major challenge in the field of Modeling & Simulation is providing efficient parallel computation for a variety of algorithms. Algorithms that are described easily and computed efficiently for continuous simulation, may be complex to describe and/or efficiently execute in a discrete event context, and vice-versa. Real-world models often employ multiple algorithms that are optimally […]

Lists intersection is an important operation in mod- ern web search engines. Many prior studies have focused on the single-core or multi-core CPU platform or many-core GPU. In this paper, we propose a CPU-GPU cooperative model that can integrate the computing power of CPU and GPU to perform lists intersection more efficiently. In the so-called […]

This paper presents a hybrid algorithm for rendering approximate motion and defocus blur with precise stochastic visibility evaluation. It demonstrates—for the first time, with a full stochastic technique—real-time performance on conventional GPU architectures for complex scenes at 1920×1080 HD resolution. The algorithm operates on dynamic triangle meshes for which per-vertex velocity or corresponding vertices from […]

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 […]