high performance computing on graphics processing units: hgpu.org

In this paper, we research, analyze and develop optimization solutions for the parallel reduction function using graphics processing units (GPUs) that implement the Compute Unified Device Architecture (CUDA), a modern and novel approach for improving the software performance of data processing applications and algorithms. Many of these applications and algorithms make use of the reduction […]

B-Splines are a useful tool in signal processing, and are widely used in the analysis of two and three-dimensional images. B-Splines provide a continuous representation of the signal, image, or volume, which is useful for interpolation, resampling, noise removal, and differentiation – all important steps in many signal processing algorithms. These splines are defined entirely […]

We present a fast and simple adaption of the well-known mean shift technique for image segmentation to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply the equivalent of mean shift segmentation on this image, i.e. sharpen the […]

The realistic and interactive simulation of deformable objects has become a challenge in Computer Graphics. In this paper, we propose a GPU implementation of the resolution of the mechanical equations, using a semi-implicit as well as an implicit integration scheme. At the contrary of the classical FEM approach, forces are directly computed at each node […]

As a consequence of the immense computational power avail-able in GPUs, the usage of these platforms for running data-intensive general purpose programs has been increasing. Since memory and pro-cessor architectures of CPUs and GPUs are substantially different, pro-grams designed for each platform are also very different and often resort to a very distinct set of […]

We present a parallel implementation of the randomized (1 + e)-approximation algorithm for packing and covering linear programs presented by Koufogiannakis and Young [4]. In order to make the algorithm more parallelizable we also implemented a deterministic version of the algorithm, i.e. instead of updating a single random entry at each iteration we updated deterministically […]

Graphics Processing Units (GPUs) have been extensively applied in the High Performance Computing (HPC) community. HPC applications require additional special programming environments to improve the utilization of GPUs, for example, NVIDIA’s CUDA and Khronos group’s OpenCL. This thesis will introduce a preprocessor framework called HPC.NET, which is deployed on the Microsoft .NET platform to meet […]

Real-time optical mapping technology is a technique that can be used in cardiac disease study and treatment technology development to obtain accurate and comprehensive electrical activity over the entire heart. It provides a dense spatial electrophysiology. Each pixel essentially plays the role of a probe on that location of the heart. However, the high throughput […]

Graphics processing units (GPUs) have an SIMD architecture and have been widely used recently as powerful general-purpose co-processors for the CPU. In this paper, we investigate efficient GPU-based data cubing because the most frequent operation in data cube computation is aggregation, which is an expensive operation well suited for SIMD parallel processors. H-tree is a […]

Real-time stereo matching, which is important in many applications like self-driving cars and 3-D scene reconstruction, requires large computation capability and high memory bandwidth. The most time-consuming part of stereomatching algorithms is the aggregation of information (i.e. costs) over local image regions. In this paper, we present a generic representation and suitable implementations for three […]

Massively parallel processing is a type of computing that uses many separate CPUs or GPUs running in parallel to execute a single program. Because most computations are contained in program loops, automatic extraction of parallelism available in loops is extremely important for many-core systems. In this paper, we study speed-up and scalability of parallel code […]

Algorithm-specific, i.e., semantic-specific optimizations have been observed to bring significant performance gains, especially for a diverse set of multi/many-core architectures. However, current programming models and compiler technologies for the state-of-the-art architectures do not exploit well these performance opportunities. In this paper, we propose a pattern-making methodology that enables algorithm-specific optimizations to be encapsulated into "optimization […]