high performance computing on graphics processing units: hgpu.org

Binarization is widely used technique in many of the image processing applications. Fast algorithms are needed for fast and efficient image processing systems. Many algorithms of image processing and pattern recognition have recently been implemented on Graphic Processing Unit (GPU) for faster computational times. GPUs are most prominent hardware in utilizing parallelism and pipelining than […]

High speed feature detection is a requirement for many real-time multimedia and computer vision applications. In previous work, the Harris and KLT algorithms were redesigned to increase the performance by reducing the algorithmic complexity, resulting in the Low Complexity Corner Detector algorithm. To attain further speedup, this paper proposes the implementation of this low complexity […]

Neighborhood denoising filters are powerful techniques in image processing and can effectively enhance the image quality in CT reconstructions. In this study, by taking the bilateral filter and the non-local mean filter as two examples, we discuss their implementations and perform fine-tuning on the targeted GPU architecture. Experimental results show that the straightforward GPU-based neighborhood […]

General purpose computing on GPUs provides a way for certain applications to benefit from a commonly available massively parallel architecture. As such deployment becomes more widespread, multiple GPU applications will have to execute on the same hardware in systems that have only one GPU. The aggregate loads of the GPU and CPU impact the performance […]

Data sets of immense size are regularly generated on large scale computing resources. Even among more traditional methods for acquisition of volume data, such as MRI and CT scanners, data which is too large to be effectively visualized on standard workstations is now commonplace. One solution to this problem is to employ a ‘visualization cluster,’ […]

Modern accelerators and multi-core architectures offer significant computing power at a very modest cost. With this trend, an important research issue at the software end is how to make the best use of these computing devices, and how to enable high performance without the users having to put too much effort into learning the architecture […]

We developed a ray casting-based rendering system for the visualization of geological subsurface models consisting of multiple highly detailed height fields. Based on a shared out-of-core data management system, we virtualize the access to the height fields, allowing us to treat the individual surfaces at different local levels of detail. The visualization of an entire […]

Image processing and pattern recognition algorithms take more time for execution on a single core processor. Graphics Processing Unit (GPU) is more popular now-a-days due to their speed, programmability, low cost and more inbuilt execution cores in it. Most of the researchers started work to use GPUs as a processing unit with a single core […]

The aim of this work contribution is the neuromorphic low-power GPU implementation of the processing stages for robust and multichannel optical flow estimation that permits highly parallel real-time filtering.

PATUS is a code generation and auto-tuning framework for stencil computations targeted at modern multi- and many-core processors, such as multicore CPUs and graphics processing units. Its ultimate goals are to provide a means towards productivity and performance on current and future multi- and many-core platforms. The framework generates the code for a compute kernel […]

This paper presents the architecture of an heterogeneous cluster where each node has one or more Graphical Unit Processors (GPUs). The motivation of the work is the fact that this technology presents very impressive results in High Performance Computing at a very low cost and very small energy consumption so. Although this might not be […]

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 in a hybrid CPU-GPU computing environment. Considering that the Hessenberg reduction algorithm […]