high performance computing on graphics processing units: hgpu.org

This paper presented a new method for scene images classification via Partially Connected Neural Network. The neural network has a mesh structure in which each neuron maintain a fixed number of connections with other neurons. In training, the evolutionary computation method was used to optimize the connection target neurons and its connection weights. The model […]

A variety of research exists for the processing of continuous queries in large, mobile environments. Each method tries, in its own way, to address the computational bottleneck of constantly processing so many queries. In this paper, we introduce an efficient and scalable system for monitoring continuous queries by leveraging the parallel processing capability of the […]

Traditional texturing using a set of two dimensional image maps is an established and widespread practice. However, it is difficult to parameterize a model in texture space, particularly with representations such as implicit surfaces, subdivision surfaces, and very dense or detailed polygonal meshes. Based on an adaptive octree textures definition, this paper proposes a direct […]

This paper investigates the benefits derived by adopting the use of Graphics Processing Unit (GPU) parallel programming in the field of biomedical signal processing. The differences in execution time when computing the Correlation Dimension (CD) of multivariate neurophysiological recordings and the Skin Conductance Level (SCL) are reported by comparing several common programming environments. Moreover, as […]

With recent technological advances, graphics processing units (GPUs) are providing increasingly higher performance with improvement programmability. This paper investigates NVIDIApsilas CUDA technology that enables data mining algorithm be parallelized effectively on GPU. The proposed algorithm exploits the computational power and the memory hierarchy of GPUs, using the shared memory to store frequently accessed data. Experimental […]

Local search (LS) algorithms are among the most powerful techniques for solving computationally hard problems in combinatorial optimization. These algorithms could be viewed as "walks through neighborhoods" where the walks are performed by iterative procedures that allow to move from a solution to another one in the solution space. In these heuristics, designing operators to […]

This paper presents a simple approach for adding more details to non-photorealistic image without increasing the complexity of the model. Bump mapping technique is introduced to create the details that can vary automatically with the change of light and view direction, but this technique originally aims to make a rendered surface look more realistic, so […]

Graphics processing units (GPUs) for years have been dedicated mostly to real time rendering. Recently leading GPU manufactures have extended their research area and decided to support also graphics computing. In this paper, we describe an impact of new GPU features on development process of an efficient finite difference time domain (FDTD) implementation.

This Paper proposes an interactive point clouds fairing algorithm running on many-core system. The algorithm is composed of four steps. Firstly, a k nearest neighbor searching method was designed which could fully utilize the computing ability of GPU. Secondly, a parallel Gaussian weighted normal estimation was put forward. Thirdly, a weighted fairing method was proposed […]

We present the I3DC system prototype, which constructs the cube for tens of millions of data items within milliseconds, and provides high-quality cube visualization as well as highly-interactive OLAP operations. Our approach is based on a novel blending-as-aggregation (BAA) algorithm that maps distributive OLAP aggregations to the intrinsic rendering mechanisms of the GPU. Our system […]

The computational power of modern graphics processing units (GPUs) has become an interesting alternative in high performance computing. The specialized hardware of GPUs delivers a high degree of parallelism and performance. Various applications in scientific computing have been implemented such that computationally intensive parts are executed on GPUs. In this article, we present a GPU […]

The purpose of this paper is to implement association rule mining algorithm using Nvidia CUDA framework for general purpose computing on GPU. The major objective is to perform performance comparison of association rule mining algorithm using C based implementation on Intel Quad Core/Core2 Duo CPU with CUDA based implementation on Nvidia G80 and GTX 200 […]