12540
S. V. Ghorpade, S. M. Kamalapur
Local search metaheuristic algorithms are proven & powerful combinatorial optimization strategies to tackle hard problems like traveling salesman problem. These algorithms explore & evaluate neighbors of a single solution. Time Consuming LSM algorithms can be improved by parallelizing exploration & evaluation of neighbors of a solution. GPU architecture is suitable for algorithms of single program […]
View View   Download Download (PDF)   
Koushik Mandal
General Purpose Computation on Graphics Processing Units (GP-GPU) has been recognized as viable and inexpensive technique in recent trends of parallel computing. Earlier this technology has only been used as commodity processing units in video cards which have been used for generating graphics in High resolution. This technology provides greater computational power with its high […]
View View   Download Download (PDF)   
Gaurav Bhamare, Satish Banait
GPU Computing Have attracted lots of attention due to their large amount of data processing. The algorithm proposed in this paper is use for exact pattern matching on GPU. Among some famous algorithms, the Aho-Corasick Algorithm match multiple pattern simultaneously. a Traditional Aho-Corasick Algorithm matches the pattern by traversing state machine, known as Deterministic finite […]
View View   Download Download (PDF)   
M. Bader, A. Bode, H.-J. Bungartz
Parallel computing has been the enabling technology of high-end machines for many years. Now, it has finally become the ubiquitous key to the efficient use of any kind of multi-processor computer architecture, from smart phones, tablets, embedded systems and cloud computing up to exascale computers. _x000D_ This book presents the proceedings of ParCo2013 – the […]
View View   Download Download (PDF)   
Antonis S. Nikitakis
Human vision is a complex combination of physical, psychological and neurological processes that allow us to interact with our environment. We use vision effortlessly to detect, identify and track objects, to navigate and to create a conceptual map of our surroundings. The goal of computer vision is to design computer systems that are capable of […]
View View   Download Download (PDF)   
Jianting Zhang, Simin You, Le Gruenwald
Fast growing computing power on commodity parallel hardware makes it both an opportunity and a challenge to use modern hardware for large-scale data management. While GPU (Graphics Processing Unit) computing is conceptually an excellent match for spatial data management which is both data and computing intensive, the complexity of multi-dimensional spatial indexing and query processing […]
View View   Download Download (PDF)   
Christopher Lauderdale, Robert Springer, Rishi Khan
As computing systems increase in size and parallelism, it becomes more and more difficult to balance workload during program execution. Heterogeneous systems further complicate the situation, as their different constituent compute resources may consume work at different rates, and may have affinity for different kinds of work. Traditional approaches to load-balancing fail to fully address […]
View View   Download Download (PDF)   
Minsoo Rhu
Recent graphics processing units (GPUs) have emerged as a promising platform for general purpose computing and have been shown to be very efficient in executing parallel applications with regular control and memory access behavior. Current GPU architectures primarily adopt the single-instruction multiple-thread (SIMT) programming model that balances programmability and hardware efficiency. With SIMT, the programmer […]
View View   Download Download (PDF)   
Konstantis Daloukas
The on-chip power delivery network constitutes a vital subsystem of modern nanometer-scale integrated circuits, since it affects in a critical way the performance and correct operation of the devices. As technology scaling enters in the nanometer regime, there is an increasing need for accurate and efficient analysis of the power delivery network. The impact of […]
View View   Download Download (PDF)   
Dariusz Cieslakiewicz
During times of stock market turbulence and crises, monitoring the clustering behaviour of financial instruments allows one to better understand the behaviour of the stock market and the associated systemic risks. In the study undertaken, I apply an effective and performant approach to classify data clusters in order to better understand correlations between stocks. The […]
View View   Download Download (PDF)   
Wo Mei Seen, R. U. Gobithaasan, Kenjiro T. Miura
There is a significant reduction of processing time and speedup of performance in computer graphics with the emergence of Graphic Processing Units (GPUs). GPUs have been developed to surpass Central Processing Unit (CPU) in terms of performance and processing speed. This evolution has opened up a new area in computing and researches where highly parallel […]
View View   Download Download (PDF)   
Ken Chatfield, Karen Simonyan, Andrew Zisserman
We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval – where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art […]
View View   Download Download (PDF)   
Page 1 of 46812345...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

128 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1189 peoples are following HGPU @twitter

* * *

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: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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 © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: