Posts

Apr, 18

Comparative Study of Frequent Itemset Mining Techniques on Graphics Processor

Frequent itemset mining (FIM) is a core area for many data mining applications as association rules computation, clustering and correlations, which has been comprehensively studied over the last decades. Furthermore, databases are becoming gradually larger, thus requiring a higher computing power to mine them in reasonable time. At the same time, the improvements in high […]
Apr, 18

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Recent years have witnessed a phenomenal growth in the computational capabilities and applications of GPUs. However, this trend has also led to dramatic increase in their power consumption. This paper surveys research works on analyzing and improving energy efficiency of GPUs. It also provides a classification of these techniques on the basis of their main […]
Apr, 17

Challenges and Advances in Large-scale DFT Calculations on GPUs, webinar

Recent advances in reformulating electronic structure algorithms for stream processors such as graphical processing units have made DFT calculations on systems comprising up to O(10 to the 3) atoms feasible. Simulations on such systems that previously required half a week on traditional processors can now be completed in only half an hour. Join Professor Heather […]
Apr, 17

Energy-Efficient FPGA Implementation for Binomial Option Pricing Using OpenCL

Energy efficiency of financial computations is a performance criterion that can no longer be dismissed, and is as crucial as raw acceleration and accuracy of the solution. In order to reduce the energy consumption of financial accelerators, FPGAs offer a good compromise with low power consumption and high parallelism. However, designing and prototyping an application […]
Apr, 17

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes

The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient […]
Apr, 17

Importance Sampling of Realistic Light Sources

Realistic images can be rendered by simulating light transport with Monte Carlo methods. The possibility to use realistic light sources for synthesizing images greatly contributes to their physical realism. Among existing models, the ones based on environment maps and light fields are attractive due to their ability to capture faithfully the far-field and near-field effects […]
Apr, 17

Feasibility Analysis of Bilateral Filtering by General Purpose Graphical Processing Unit Computing

Digital Image Processing is an evergreen area of research in the signal processing domain. Denoising of digital images is one of the most fundamental operations that is performed in the pre-processing stage of almost all image processing operations. This important feature makes denoising as one of the lucrative research areas within the broad area of […]
Apr, 17

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association […]
Apr, 16

CISE 2014 – Asian Conference on Computer and Information Science and Engineering, CISE 2014

The Asian Conference on Computer and Information Science and Engineering will incorporate all topics within the field of computer and information science. This inaugural event promises to attract experts within the field of computer and information science and engineering, and allow for professors, researchers and university students to collaborate on this ever-growing field.
Apr, 16

Performance-aware component composition for GPU-based systems

This thesis addresses issues associated with efficiently programming modern heterogeneous GPU-based systems, containing multicore CPUs and one or more programmable Graphics Processing Units (GPUs). We use ideas from component-based programming to address programming, performance and portability issues of these heterogeneous systems. Specifically, we present three approaches that all use the idea of having multiple implementations […]
Apr, 16

On optimization techniques for the matrix multiplication on hybrid CPU+GPU platforms

The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms is analyzed. Basic models of the execution time of the hybrid routine and information obtained during its installation are used to optimize the execution time with a balanced assignation of the computation to the computing components in the heterogeneous system. Satisfactory […]
Apr, 16

Dynamic Instrumentation and Optimization for GPU Applications

Parallel architectures like GPUs are a tantalizing compute fabric for performance-hungry developers. While GPUs enable order-of-magnitude performance increases in many data-parallel application domains, writing efficient codes that can actually manifest those increases is a non-trivial endeavor, typically requiring developers to exercise specialized architectural features exposed directly in the programming model. Achieving good performance on GPUs […]
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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 11.4
  • SDK: AMD APP SDK 2.8
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 5.0.35, AMD APP SDK 2.8

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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