Aug, 23

Exploiting Parallel Processing Power of GPU for High Speed Frequent Pattern Mining

Frequent pattern mining is one of the widely used data mining techniques for discovering trends or patterns from databases. As data is growing in exponential pace, data mining activities need more powerful computing. Fortunately modern GPUs (Graphics Processing Units) have specialized electronic circuits and support parallel processing. GPUs are capable of processing huge amount of […]
Aug, 21

GPGPU Programming for Games and Science

An In-Depth, Practical Guide to GPGPU Programming Using Direct3D 11. GPGPU Programming for Games and Science demonstrates how to achieve the following requirements to tackle practical problems in computer science and software engineering: Robustness, Accuracy, Speed, Quality source code that is easily maintained, reusable, and readable. The book primarily addresses programming on a graphics processing […]
Aug, 21

GPU accelerated rendering of vector based maps on iOS

Digital maps can be represented as either raster (bitmap images) or vector data. Vector maps are often preferable as they can be stored more efficiently and rendered irrespective of screen resolution. Vector map rendering on demand can be a computationally intensive task and has to be implemented in an efficient manner to ensure good performance […]
Aug, 21

A Computational Realization of a Semi-Lagrangian Method for Solving the Advection Equation

A parallel implementation of a method of the semi-Lagrangian type for the advection equation on a hybrid architecture com-putation system is discussed. The difference scheme with variable stencil is constructed on the base of an integral equality between the neighboring time levels. The proposed approach allows one to avoid the Courant-Friedrichs-Lewy restriction on the relation […]
Aug, 21

Volumetric Rendering Techniques for Scientific Visualization

Direct volume rendering is widely used in many applications where the inside of a transparent or a partially transparent material should be visualized. We have explored several aspects of the problem. First, we proposed a view-dependent selective refinement scheme in order to reduce the high computational requirements without affecting the image quality significantly. Then, we […]
Aug, 21

Error Resilience Evaluation on GPGPU Applications

While graphics processing units (GPUs) have gained wide adoption as accelerators for general-purpose applications (GPGPU), the end-to-end reliability implications of their use have not been quantified. Fault injection is a widely used method for evaluating the reliability of applications. However, building a fault injector for GPGPU applications is challenging due to their massive parallelism, which […]
Aug, 19

GPU Accelerated Range Trees with Applications

Range searching is a primal problem in computational geometry with applications to database systems, mobile computing, geographical information systems, and the like. Defined simply, the problem is to preprocess a given a set of points in a d-dimensional space so that the points that lie inside an orthogonal query rectangle can be efficiently reported. Many […]
Aug, 19

Parallel Graph Mining with GPUs

Frequent graph mining is an important though computationally hard problem because it requires enumerating possibly an exponential number of candidate subgraph patterns, and checking their presence in a database of graphs. In this paper, we propose a novel approach for parallel graph mining on GPUs, which have emerged as a relatively cheap but powerful architecture […]
Aug, 19

Parallel Outlier Detection on Uncertain Data for GPUs

Outlier detection, also known as anomaly detection, is a common data mining task in identifying data points that are outside expected patterns in a given dataset. It has useful applications such as network intrusion, system faults, and fraudulent activity. In addition, real world data are uncertain in nature and they may be represented as uncertain […]
Aug, 19

Practical Symbolic Race Checking of GPU Programs

Even the careful GPU programmer can inadvertently introduce data races while writing and optimizing code. Currently available GPU race checking methods fall short either in terms of their formal guarantees, ease of use, or practicality. Existing symbolic methods: (1) do not fully support existing CUDA kernels; (2) may require user-specified assertions or invariants; (3) often […]
Aug, 19

An Efficient Cell List Implementation for Monte Carlo Simulation on GPUs

Maximizing the performance potential of the modern day GPU architecture requires judicious utilization of available parallel resources. Although dramatic reductions can often be obtained through straightforward mappings, further performance improvements often require algorithmic redesigns to more closely exploit the target architecture. In this paper, we focus on efficient molecular simulations for the GPU and propose […]
Aug, 19

Accelerated composite distribution function methods for computational fluid dynamics using GPU

The Kinetic Theory of Gases has long been established as a useful tool for the solution of modern Computational Fluid Dynamics (CFD) problems. Together with the Finite Volume Method, such approaches have been popular in CFD for over 30 years, with techniques such as the Equilibrium Flux Method (EFM) or Kinetic Flux Vector Splitting (KFVS), […]
Page 3 of 74812345...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

142 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1223 peoples are following HGPU @twitter

Featured events

* * *

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: