high performance computing on graphics processing units: hgpu.org

Graphical Processing Units (GPU) architectures are massively used for resource-intensive computation. Initially dedicated to imaging, vision and graphics, these architectures serve nowadays a wide range of multi-purpose applications. The GPU structure, however, does not suit all applications. This can lead to performance shortage. Among several applications, the aim of this work is to analyze GPU […]

Recent advancements in semi-conductor fabrication has led to a dramatic increase in the size of data sets of advanced imaging sensors. While increased pixel counts leads to greater area coverage and higher resolution, it also results in higher image processing time. If real-time image processing is required, power and size requirements go up as large […]

Graphic Processing Unit (GPU) has involved into a parallel computation for it’s massively multi threaded architecture. Due to its high computational power, GPU has been used to deal with many problems that can be easily parallelized. This paper will present a GPU based spot noise parallel algorithm for 2D vector field visualization. It uses spot […]

Calculating Euclidean distance matrix is a data intensive operation and becomes computationally prohibitive for large datasets. Recent development of Graphics Processing Units (GPUs) has produced superb performance on scientific computing problems using massive parallel processing cores. However, due to the limited size of device memory, many GPU based algorithms have low capability in solving problems […]

Minimum spanning tree is a classical problem in graph theory that plays a key role in a broad domain of applications. This paper proposes a minimum spanning tree algorithm using Prim’s approach on Nvidia GPU under CUDA architecture. By using new developed GPU-based Min-Reduction data parallel primitive in the key step of the algorithm, higher […]

Intersection of inverted lists is a frequently used operation in search engine systems. Efficient CPU and GPU intersection algorithms for large problem size are well studied. We propose an efficient GPU algorithm for high performance intersection of inverted index lists on CUDA platform. This algorithm feeds queries to GPU in batches, thus can take full […]

Driven by the market demand for high-definition 3D graphics, commodity graphics processing units (GPUs) have evolved into highly parallel, multi-threaded, many-core processors, which are ideal for data parallel computing. Many applications have been ported to run on a single GPU with tremendous speedups using general C-style programming languages such as CUDA. However, large applications require […]

Force directed approach is one of the most widely used methods in graph drawing research. However, the running time is increased intolerablely along with the enlargement of the graph size, which restricts the algorithm’s practicability. By the aid of GPU (graphics processing unit) computing platform, we can speed-up the graph layout with low cost, but […]

Solution of large-scale dense nonsymmetric eigenvalue problem is required in many areas of scientific and engineering computing, such as vibration analysis of automobiles and analysis of electronic diffraction patterns. In this study, we focus on the Hessenberg reduction step and consider accelerating it using GPU. Our main strategy is to use the CUBLAS, an optimized […]

Graphics processing units provide a large computational power at a very low price which position them as an ubiquitous accelerator. Efficient primitives that can expand the range of operations performed on the GPU are thus important. Discrete Range Searching(DRS) is one such primitive with direct applications to string processing, document and text retrieval systems, and […]

The evolution of the graphics processing units (GPU) driven by the computer games business brought a graphics hardware as a high performance, programmable and non-expensive chips. Nowadays, the graphic card has a truly programmable architecture which allows to process data with high parallelism and high memory access rate. That is the key motivation fact for […]

The method Finite Difference Time Domain (FDTD) is widely used in electromagnetic simulations. Since this method is a data intensive and computation intensive problem, there are a lot of initiatives to improve the scalability and the performance of the FDTD. Specifically the use of GPU to accelerate the FDTD is in focus, which has a […]