May, 24

A Framework for 3D Model-Based Visual Tracking Using a GPU-Accelerated Particle Filter

A novel framework for acceleration of particle filtering approaches to 3D model-based, markerless visual tracking in monocular video is described. Specifically, we present a methodology for partitioning and mapping the computationally expensive weight-update stage of a particle filter to a graphics processing unit (GPU) to achieve particle- and pixel-level parallelism. Nvidia CUDA and Direct3D are […]
May, 24

A new representation of intensity atlas for GPU-accelerated instance generation

Fast instance generation is a key requirement in atlas-based registration and other problems that need a large number of atlas instances. This paper describes a new method to represent and construct intensity atlases. Both geometry and intensity information are represented using B-spline deformation lattices; intensities are approximated using the multi-level B-spline approximation algorithm during model […]
May, 24

SSE Vectorized and GPU Implementations of Arakawa’s Formula for Numerical Integration of Equations of Fluid Motion

The numerical method presented by Arakawa in 1966[3] implements a finite difference scheme of the Jacobian for the solution of the equation of motion for two-dimensional incompressible flows, which diminishes nonlinear computational instability and permits long-term numerical integrations. This paper presents an efficient implementation of Arakawa’s formula using vectorized Streaming SIMD Extension (SSE) and Advanced […]
May, 24

Parallel Computing Model of Multiple Dimensions Data Streams Canonical Correlation Analysis with GPU

With view to satisfying the requirement of real-time under the circumstance of resource-constraints, specific and practical architecture for high-dimensional data streams are proposed, meanwhile, based on CUDA (Compute Unified Device Architecture), canonical correlation analysis between two multiple dimensions data streams using data cube pattern and dimensionality-reduction technique is carried out in this framework. The theoretical […]
May, 24

GPU Rendering of the Thin Film on Paints with Full Spectrum

Spectrum-based rendering uses spectral distributions instead of just three RGB colors for representation of light sources and surface properties in rendering equation. Since, spectrum has a value at every visible wavelength, the spectrum-based rendering gives much accurate color computation compared to RGB-based rendering and it give us opportunity to simulate wavelength dependent phenomena and effects […]
May, 24

GPU-based DVB-S2 LDPC decoder with high throughput and fast error floor detection

A new strategy is proposed for implementing computationally intensive high-throughput decoders based on the long length irregular LDPC codes adopted in the DVB-S2 standard. It is supported on manycore graphics processing unit (GPU) architectures, for performing parallel multi-threaded decoding of multiple codewords with reduced accesses to global memory. This novel approach is flexible and scalable, […]
May, 24

GPU-based tolerance volumes for mesh processing

In an increasing number of applications triangle meshes represent a flexible and efficient alternative to traditional NURBS-based surface representations. Especially in engineering applications it is crucial to guarantee that a prescribed approximation tolerance to a given reference geometry is respected for any combination of geometric algorithms that are applied when processing a triangle mesh. We […]
May, 24

Accelerating Two Algorithms for Large-Scale Compound Selection on GPUs

Compound selection procedures based on molecular similarity and diversity are widely used in drug discovery. Current algorithms are often time consuming when applied to very large compound sets. This paper describes the acceleration of two selection algorithms (the leader and the spread algorithms) on graphical processing units (GPUs). We first parallelized the molecular similarity calculation […]
May, 24

Using a GPU-CPU architecture to speed up a GA-based real-time system for trading the stock market

The use of mechanical trading systems allows managing a huge amount of data related to the factors affecting investment performance (macroeconomic variables, company information, industrial indicators, market variables, etc.) while avoiding the psychological reactions of traders when they invest in financial markets. When trading is executed in an intra-daily frequency instead a daily frequency, mechanical […]
May, 24

A Modeling Approach based on UML/MARTE for GPU Architecture

Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many efforts have been done to describe abstractions of applications in relation to their target architectures. Thus, when […]
May, 23

A GPU acceleration for FFT-based fast solvers for the integral equation

This paper presents the advantages that GPUs can bring when used in cooperation with the CPU in solving linear systems arising from electromagnetic applications. In particular we show how FFT-based methods for the integral equation can be speed up with very low effort. Two different applications are shown to prove the gain in computational times […]
May, 23

Real-Time Optical Flow Calculations on FPGA and GPU Architectures: A Comparison Study

FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing a variety of computations. Applications successfully implemented on FPGAs have typically contained high levels of parallelism and have often used simple statically-scheduled control and modest arithmetic. Recently introduced computing devices such as coarse grain reconfigurable arrays, multi-core processors, and graphical processing […]
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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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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