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Posts

Jun, 27

7th International Conference on Computer and Electrical Engineering, ICCEE 2014

Submission Deadline: 2014-07-20 Publication: All accepted paper will be published in International Journal of Electrical Energy (IJOEE), which will be indexed by Ulrich’s Periodicals Directory, Google Scholar, EBSCO, Engineering & Technology Digital Library and Electronic Journals Digital Library. Call for Paper: Computer Engineering Algorithm Computer Vision, Graphics and Intelligence Computational and Artificial Intelligence Image Processing […]
Jun, 27

Performance Evaluation of Parallel AES Implementations over CUDA GPU Framework

With a high computational complexity of encryption algorithm, AES, especially for huge real-time data, GPU has recently offered an alternate computational system instead of a traditional CPU (thread), incurring a significant improvement in speeding up the computational intensive parallel data encryption in various aspects – tremendous number of processing cores and non-generic computational processing architecture […]
Jun, 27

Industrial Robot Collision Handling in Harsh Environments

The focus in this thesis is on robot collision handling systems, mainly collision detection and collision avoidance for industrial robots operating in harsh environments (e.g. potentially explosive atmospheres found in the oil and gas sector). Collision detection should prevent the robot from colliding and therefore avoid a potential accident. Collision avoidance builds on the concept […]
Jun, 27

Computation on GPU of Eigenvalues and Eigenvectors of a Large Number of Small Hermitian Matrices

This paper presents an implementation on Graphics Processing Units of QR-Householder algorithm used to find all the eigenvalues and eigenvectors of many small hermitian matrices (double precision) in a very short time to address time constraints for Radar issues.
Jun, 27

Image Noise Removal on Heterogeneous CPU-GPU Configurations

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. […]
Jun, 27

Speeding up a Video Summarization Approach Using GPUs and Multicore CPUs

The recent progress of digital media has stimulated the creation, storage and distribution of data, such as digital videos, generating a large volume of data and requiring efficient technologies to increase the usability of these data. Video summarization methods generate concise summaries of video contents and enable faster browsing, indexing and accessing of large video […]
Jun, 26

On the Characterization of OpenCL Dwarfs on Fixed and Reconfigurable Platforms

The proliferation of heterogeneous computing platforms presents the parallel computing community with new challenges. One such challenge entails evaluating the efficacy of such parallel architectures and identifying the architectural innovations that ultimately benefit applications. To address this challenge, we need benchmarks that capture the execution patterns (i.e., dwarfs or motifs) of applications, both present and […]
Jun, 26

Effect And Analysis of Elastic Fidelity Computing On GPUs

The graphics processing unit (GPU) has become an integral part and plays a very vital role in high-end computing. Though GPU can easily reduce the execution time, it comes at the expense of power and energy consumption. There are various ways and approaches to reduce the power and energy consumption, Elastic Fidelity Computing (EFC) in […]
Jun, 26

Fast LBP Face Detection on low-power SIMD architectures

This paper presents an embedded implementation of a face detection method based on boosted LBP features for Single Instruction Multiple Data (SIMD) architectures. The implementation exploits parallelism and data reuse in the detection algorithm and is integrated into CogniVue’s Gen-1 APEX platform, which uses a SIMD design and is extremely energy efficient. The proposed embedded […]
Jun, 26

Multi-GPU Implementation of a Hybrid Thermal Lattice Boltzmann Solver using the TheLMA Framework

In this contribution, a single-node multi-GPU thermal lattice Boltzmann solver is presented. We implement a simplified version of the hybrid model developed by Lallemand and Luo in 2003, which combines multiple-relaxation-time lattice Boltzmann for the fluid flow with a finite-difference method for temperature. The program is based on the TheLMA framework which was developed for […]
Jun, 26

C and CUDA Implementation for SIRT and SART Reconstruction Algorithms

Tomographic reconstruction techniques deserve studying because of its plenty of application in interdisciplinary fields. With outstanding features of no need to set of uniformly distributed projections for precise reconstruction, easy provide a priori knowledge about the reconstructed object, good image quality, we chose Simultaneous Iterative Reconstruction Technique (SIRT) and Simultaneous Algebraic Reconstruction Technique (SART) as […]
Jun, 25

Customizing Driving Directions with GPUs

Computing driving directions interactively on continental road networks requires preprocessing. This step can be costly, limiting our ability to incorporate new optimization functions, including traffic information or personal preferences. We show how the performance of the state-of-the-art customizable route planning (CRP) framework is boosted by GPUs, even though it has highly irregular structure. Our experimental […]
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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.

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