high performance computing on graphics processing units: hgpu.org

We demonstrate real time off-axis Quantitative Phase Imaging (QPI) using a phase reconstruction algorithm based on NVIDIA’s CUDA programming model. The phase unwrapping component is based on Goldstein’s algorithm. By mapping the process of extracting phase information and unwrapping to GPU, we are able to speed up the whole procedure by more than 18.8x with […]

Extracting performance improvements from modest and cost-effective computing resources is one of the key challenges in the IT sector. CPU clock speeds have reached a plateau in recent years, with no significant clock speed improvements forthcoming. However, we see an increasing number of computational cores available on the desktop, via the CPU and, more recently, […]

Optical flow estimation (the estimation of the apparent motion of objects in an image sequence) is used in many applications like video compression, object detection and tracking, robot navigation, and so on. This project was focussed on one specific optical flow estimation algorithm, which uses directional filters and an AM-FM demodulation algorithm for the estimation […]

This paper describes an optical flow estimation algorithm using directional filters and an AM-FM demodulation algorithm, and its efficient implementation on a NVIDIA GPU using CUDA. The resulting implementation is several thousand times faster than the corresponding MATLAB code, which makes the described scheme suitable for real-time applications. This paper also describes a new multiresolution […]

The implementation via CUDA of a hybrid dense dynamic programming method for knapsack problems on amulti-GPU architecture is considered. Tests are carried out on a Bull cluster with Tesla S1070 computing systems. A first series of computational results shows substantial speedup. The speedup factor is close to 28 with two GPUs.

A number of issues related to floating point accuracy and compliance are a frequent source of confusion on both CPUs and GPUs. The purpose of this white paper is to discuss the most common issues related to NVIDIA GPUs and to supplement the documentation in the CUDA C Programming Guide.

This paper proposes and describes a methodology developed to port complex scientific applications originally written in FORTRAN to nVidia CUDA. The significance of this lies in the fact that, despite the performance improvement and programmer-friendliness provided by CUDA, it presently lacks support for FORTRAN. The methodology described in this paper addresses this problem using a […]

Finding interesting celestial objects among tens of thousands or even millions of recorded raw data is not an easy task to implement. In this paper we speed up this process with high level nvidia cuda C++ template library called Thrust, which makes our database with R interface much more evaluatedcient.

In this paper, we propose a scheme to accelerate the Probabilistic Latent Semantic Indexing (PLSI), which is an automated document indexing method based on a statistical latent semantic model, exploiting the high parallelism of Graphics Processing Unit (GPU). Our proposal is composed of three techniques: the first one is to accelerate the Expectation-Maximization (EM) computation […]

This paper proposes a new sparse matrix storage format which allows an efficient implementation of a sparse matrix vector product on a Fermi Graphics Processing Unit (GPU). Unlike previous formats it has both low memory footprint and good throughput. The new format, which we call Sliced ELLR-T has been designed specifically for accelerating the iterative […]

Molecular dynamic (MD) simulation is proven to be an important tool to study the structure as well as the physical properties at atomic level in materials science. However, it requires a huge computing time and hence limits the ability to treat a large scale simulation. In this paper we present a solution to speed up […]

Monte Carlo simulation is ideally suited for solving Boltzmann neutron transport equation in inhomogeneous media. However, routine applications require the computation time to be reduced to hours and even minutes in a desktop system. The interest in adopting GPUs for Monte Carlo acceleration is rapidly mounting, fueled partially by the parallelism afforded by the latest […]