high performance computing on graphics processing units: hgpu.org

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 […]

Graphics processing units (GPU) of today include hundreds of multi-threaded, multicore processors and a complex, high-bandwidth memory architecture, making them a good alternative to speed up general-purpose parallel computation where large data quantities are processed with same functions. Some successful applications of GPU computation have also been introduced in the field of circuit simulation. The […]

The Special Session on GPU Computing and Hybrid Computing aims at providing a forum for scientific researchers and engineers on hot topics related to GPU computing and hybrid computing with special emphasis on applications, performance analysis, programming models and mechanisms for mapping codes. Topics: GPU computing, multi GPU processing, hybrid computing; Programming models, programming frameworks, […]

InPar 2012 is co-located with NVidia’s GPU Technology Conference. This new conference provides a first-tier academic venue for peer-reviewed publications in the emerging fields of parallel computing, encompassing the topics of GPU computing, manycore computing, and heterogeneous computing. InPar has dual focus on “Foundations” — the fundamental advances in parallel computing itself and “Applications” — […]

We present the performance analysis of a port of the LU benchmark from the NAS Parallel Benchmark (NPB) suite to NVIDIA’s Compute Unified Device Architecture (CUDA), and report on the optimisation efforts employed to take advantage of this platform. Execution times are reported for several different GPUs, ranging from low-end consumergrade products to high-end HPC-grade […]