Henk Mulder
With the emergence of general purpose GPU (GPGPU) programming, concurrent data processing of large arrays of data has gained a significant boost in performance. However, due to the memory architecture between the host and GPU device and other limitations in the instructions available on GPUs, the implementation of dynamic data structures, like linked list and […]
Saurabh Jha, Tejaswi Agarwal, B. Rajesh Kanna
To define and identify a region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be described in terms of its boundary characteristics. To address the generic issues of contour tracking, the yConvex Hypergraph (yCHG) model was proposed by Kanna et al [1]. In this work, we propose a parallel approach […]
View View   Download Download (PDF)   
Erik Kral, Petr Capek, Lubomir Vasek
This research paper explores an OpenCL-based algorithm to aid heat load modelling for district heating plants. Previous studies have proven that heat loads mostly depend on the external temperatures (temperature dependency component) and the time of the day (time dependency component). In this research we have used the sum of two truncated exponential functions to […]
View View   Download Download (PDF)   
Chouchene Marwa, Sayadi Fatma, Tourki Rached
Largely driven by the gaming industry, research and development of hardware tools for the generation of images, such as graphics cards (or GPU, Graphics Processing Units), experienced a tremendous growth in recent years. The increased power and flexibility and the low price of these GPUs have resulted in unexpected use in areas other than graphics. […]
View View   Download Download (PDF)   
Nermine N. Sophoclis, M. Abdeen, El-Sayed M. El-Horbaty
Preprocessing of data is a vital aspect in information retrieval. Stemming is a major preprocessing task. The goal of stemming is to reduce the inflectional and some of the derivational forms of a word to its base form. Dealing with the massive amounts of data on the web, preprocessing generally consumes a major portion of […]
View View   Download Download (PDF)   
Peter Fodrek, Ludovit Farkas, Tomas Murgas
This paper will report our evaluation to use openCL as a platform for hard realtime scheduling. Specifically, we have evaluated which types of tasks are faster on GPGPU than on CPU. We have investigated computational tasks, memory intensive tasks (especially tasks using low latency GDDR memory) and disk intensive tasks. This study is the first […]
View View   Download Download (PDF)   
Dibyam Pradhan, Naveen M., Sai Hareesh A., Pallav Kumar Baruah, Venkatachalam Chandrasekaran
Image inpainting refers to the process of reconstructing the original image from a damaged one in a visually plausible way. We propose a new gradientbased algorithm for exemplar-based inpainting by making use of L1 norm. We implement the most time consuming step of the algorithm on the GPU and compare the serial execution timings against […]
View View   Download Download (PDF)   
Julian Becerra-Sagredo, Carlos Malaga, Francisco Mandujano
Multigrid algorithms are among the fastest iterative methods known today for solving large linear and some non-linear systems of equations. Greatly optimized for serial operation, they still have a great potential for parallelism not fully realized. In this work, we present a novel multigrid algorithm designed to work entirely inside many-core architectures like the graphics […]
View View   Download Download (PDF)   

* * *

* * *

Follow us on Twitter

HGPU group

1660 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 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: