Jae-Hyun Seo, Eun-Sol Ko, Yong-Hyuk Kim
Generally genetic algorithm (GA) has disadvantage of taking a lot of computation time, and it is worth reducing the execution time while keeping good quality and result. Comparative experiments are conducted with one CPU and four GPUs using CUDA (Compute Unified Device Architecture) and generational GA. We implement the fitness functions of the GA which […]
View View   Download Download (PDF)   
Steffen Schmidt
Beamforming algorithms make high demands on the computer hardware and the computation time is an important factor for the assessment of this method. This paper describes techniques for optimizing the implementation of beamforming algorithms in regard to calculation time. The main focus is on using the Graphic Processing Unit for accelerating beamforming. After a brief […]
View View   Download Download (PDF)   
Robert Senser, Tom Altman
DEF-G is a declarative language and framework for the efficient generation of OpenCL GPU applications. Using our proof-of-concept DEF-G implementation, run-time and lines-of-code comparisons are provided for three well-known algorithms (Sobel image filtering, breadth-first search and all-pairs shortest path), each evaluated on three different platforms. The DEF-G declarative language and corresponding OpenCL kernels generated complete […]
View View   Download Download (PDF)   
Meisam Askari, Hossein Ebrahimpour, Azam Asilian Bidgoli, Farahnaz Hosseini
Hough transform is one of the most widely used algorithms in image processing. The major problems of Hough’s transform are its time consuming and its abundant requirement of computational resources. In this paper, we try to solve this problem by paralleling this algorithm and implementing it on GPUs (Graphic Process unit) using CUDA (Compute Unified […]
View View   Download Download (PDF)   
Wasuwee Sodsong, Jingun Hong, Seongwook Chung, Shin-Dug Kim, Bernd Burgstaller
With the emergence of social networks and improvements in computational photography, billions of JPEG images are shared and viewed on a daily basis. Desktops, tablets and smartphones constitute the vast majority of hardware platforms used for displaying JPEG images. Despite the fact that these platforms are heterogeneous multicores, no approach exists yet that is capable […]
View View   Download Download (PDF)   
Ali Ismail Awad
Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront between biometric traits. Recently, the GPU has been considered as a promising parallel processing technology due to its high performance computing, commodity, and availability. Fingerprint authentication is keep growing, and includes the deployment of many image processing and computer vision algorithms. […]
View View   Download Download (PDF)   
Jianfei Zhang, Lei Zhang
Graphics Processing Unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using Compute Unified Device Architecture (CUDA). Sliced Block ELLPACK (SBELL) format […]
View View   Download Download (PDF)   
R.C. Hidalgo, T. Kanzaki, F. Alonso-Marroquin, S. Luding
General-purpose computation on Graphics Processing Units (GPU) on personal computers has recently become an attractive alternative to parallel computing on clusters and supercomputers. We present the GPU-implementation of an accurate molecular dynamics algorithm for a system of spheres. The new hybrid CPU-GPU implementation takes into account all the degrees of freedom, including the quaternion representation […]
View View   Download Download (PDF)   
Kenny May
Tracking sunspots is not an easy task given that multiple sources of data are acquired using a variety of different instruments. With the sources of data and contributors to this repositories quickly growing, it is increasingly important to have an efficient solution to analyze the photographs to record trends and possibly make predictions. CUDA (Compute […]
View View   Download Download (PDF)   
Cyril Fischer
The presented contribution maps the possibilities of exploitation of the massive parallel computational hardware (namely GPU) for solution of the initial value problems of ordinary differential equations. Two cases are discussed: parallel solution of a single ODE and parallel execution of scalar ODE solvers. Whereas the advantages of the special architecture in the case of […]
View View   Download Download (PDF)   
A.M. Adeshina, R. Hashim, N.E.A. Khalid, Siti Z.Z. Abidin
The rapid development in information technology has immensely contributed to the use of modern approaches for visualizing volumetric data. Consequently, medical volume visualization is increasingly attracting attention towards achieving an effective visualization algorithm for medical diagnosis and pre-treatment planning. Previously, research has been addressing implementation of algorithm that can visualize 2-D images into 3-D. Meanwhile, […]
View View   Download Download (PDF)   
John O'Rourke, John Burns
The optimisation of Technical Trading parameters is a computationally intensive exercise. Models comprising a modest number of Technical Indicators require many thousands of simulations to be executed over a sample period of data, with the best performing sets of parameters employed to generate future trading signals. The purpose of this research is to investigate the […]
View View   Download Download (PDF)   
Page 1 of 212

* * *

* * *

Follow us on Twitter

HGPU group

1662 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

337 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: