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Posts

Aug, 9

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA CUDA

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP’s problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages […]
Aug, 9

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and GPU accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing GPU clusters is difficult for novice programmers, and […]
Aug, 9

Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks

In semantic scene segmentation, every pixel of an image is assigned a category label. This task can be made easier by incorporating depth information, which structured light sensors provide. Depth, however, has very different properties from RGB image channels. In this paper, we present a novel method to provide depth information to convolutional neural networks. […]
Aug, 9

Parallel Distributed Breadth First Search on the Kepler Architecture

We present the results obtained by using an evolution of our CUDA-based solution for the exploration, via a Breadth First Search, of large graphs. This latest version exploits at its best the features of the Kepler architecture and relies on a 2D decomposition of the adjacency matrix to reduce the number of communications among the […]
Aug, 9

GPU Parallel Implementation of the Approximate K-SVD Algorithm Using OpenCL

Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. We investigate a parallel version of the approximate K-SVD algorithm, where multiple atoms are updated simultaneously, and implement it using OpenCL, for execution on graphics processing units (GPU). […]
Aug, 7

Optimizing memory management on heterogeneous systems using polyhedral, compile-time techniques

The target of this thesis is to optimize memory management on heterogeneous systems. Our approach involves performing memory access pattern analysis on kernels in order to produce an accurate estimation of the memory usage. This information is produced in the form of array ranges describing which elements are accessed as well as whether they are […]
Aug, 7

On the Fly Porn Video Blocking Using Distributed Multi-GPU and Data Mining Approach

Preventing users from accessing adult videos and at the same time allowing them to access good educational videos and other materials through campus wide network is a big challenge for organizations. Major existing web filtering systems are textual content or link analysis based. As a result, potential users cannot access qualitative and informative video content […]
Aug, 7

Dense Arithmetic over Finite Fields with the CUMODP Library

CUMODP is a CUDA library for exact computations with dense polynomials over finite fields. A variety of operations like multiplication, division, computation of subresultants, multi-point evaluation, interpolation and many others are provided. These routines are primarily designed to offer GPU support to polynomial system solvers and a bivariate system solver is part of the library. […]
Aug, 7

Multi-Agent Systems and General-Purpose Computing on Graphics Processing Units: A Survey

In some application domains, using a Multi-Agent Systems (MAS) modeling approach may require to handle a large number of agents (crowds, traffic, animal societies, ecosystems, etc.). Today, as this number is constantly growing, the computational resources which are needed cannot be fulfilled by the CPU of single Personal Computers (PC) any more. Considering this issue, […]
Aug, 7

Cell Charge Approximation for Accelerating Molecular Simulation on CUDA-Enabled GPU

Methods for Molecular Dynamics(MD) simulations are investigated. MD simulation is the widely used computer simulation approach to study the properties of molecular system. Force calculation in MD is computationally intensive. Parallel programming techniques can be applied to improve those calculations. The major aim of this paper is to speed up the MD simulation calculations by/using […]
Aug, 5

FPGA Acceleration of Multifunction Printer Image Processing using OpenCL

OpenCL adoption in the High Performance Computing, entertainment and scientific computing markets continues to grow. The flexibility and portability of OpenCL make it an excellent platform upon which to develop image processing applications. However, OpenCL has not yet been applied to the hardcopy printer and Multi-Function Printer, MFP, markets. The printer/MFP markets traditionally use full […]
Aug, 5

The Reduction Problem in CUDA and Its Simulation with P Systems

We introduce P systems with dynamic communication graphs which simulate the functioning of the CUDA architecture when solving the parallel reduction problem.
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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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