Mar, 21

High-quality Real-time Stereo using Adaptive Cost Aggregation and Dynamic Programming

We present a stereo algorithm that achieves high quality results while maintaining real-time performance. The key idea is simple: we introduce an adaptive aggregation step in a dynamic-programming (DP) stereo framework. The per-pixel matching cost is aggregated in the vertical direction only. Compared to traditional DP, our approach reduces the typical “streaking” artifacts without the […]
Mar, 21

Stereovision On GPU

Depth from stereo has traditionally been, and continues to be one of the most actively researched topics in computer vision. Recent development in this area has significantly advanced the state of the art in terms of quality. However, in terms of speed, these best stereo algorithms typically take from several seconds to several minutes to […]
Mar, 21

GPU-based Real-Time Execution of Vehicular Mobility Models in Large-Scale Road Network Scenarios

A methodology and its associated algorithms are presented for mapping a novel, field-based vehicular mobility model onto graphical processing unit computational platform for simulating mobility in large-scale road networks. Of particular focus is the achievement of real-time execution, on desktop platforms, of vehicular mobility on road networks comprised of millions of nodes and links, and […]
Mar, 21

An approach for the effective utilization of GP-GPUs in parallel combined simulation

A major challenge in the field of Modeling & Simulation is providing efficient parallel computation for a variety of algorithms. Algorithms that are described easily and computed efficiently for continuous simulation, may be complex to describe and/or efficiently execute in a discrete event context, and vice-versa. Real-world models often employ multiple algorithms that are optimally […]
Mar, 20

Efficient lists intersection by CPU-GPU cooperative computing

Lists intersection is an important operation in mod- ern web search engines. Many prior studies have focused on the single-core or multi-core CPU platform or many-core GPU. In this paper, we propose a CPU-GPU cooperative model that can integrate the computing power of CPU and GPU to perform lists intersection more efficiently. In the so-called […]
Mar, 20

Real-time Stochastic Rasterization on Conventional GPU Architectures

This paper presents a hybrid algorithm for rendering approximate motion and defocus blur with precise stochastic visibility evaluation. It demonstrates—for the first time, with a full stochastic technique—real-time performance on conventional GPU architectures for complex scenes at 1920×1080 HD resolution. The algorithm operates on dynamic triangle meshes for which per-vertex velocity or corresponding vertices from […]
Mar, 20

Efficient planar features matching for robot localization using GPU

Matching image features between an image and a map of landmarks is usually a time consuming process in mobile robot localization or Simultaneous Localisation And Mapping algorithms. The main problem is being able to match features in spite of viewpoint changes. Methods based on interest point descriptors such as SIFT have been implemented on GPUs […]
Mar, 20

GPU-based Dynamic Tubular Grids for Sparse Volume Rendering

Dynamic Tubular Grids (DT-Grids) are designed to encode gridaligned data in level-set simulations. While they are extremely efficient for storing sparse volumetric data, they require logarithmic time for random access. We demonstrate that DT-Grids can be used to efficiently render sparse volumetric data that would otherwise not be able to fit in texture memory. For […]
Mar, 20

GPU Vision: Accelerating Computer Vision algorithms with Graphics Processing Units

We present an introduction to the field of GPU accelerated computer vision by examining several projects that provide the framework for researchers and developers to tap into the computational power of Graphics Processing Units (GPU). Our goal is to identify the tools and areas where GPU acceleration can provide the highest performance increases in computer […]
Mar, 20

A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

The future of high-performance computing is likely to rely on the ability to efficiently exploit huge amounts of parallelism. One way of taking advantage of this parallelism is to formulate problems as “embarrassingly parallel” Monte-Carlo simulations, which allow applications to achieve a linear speedup over multiple computational nodes, without requiring a super-linear increase in inter-node […]
Mar, 20

Mersenne Twister Random Number Generation on FPGA, CPU and GPU

Random number generation is a very important operation in computational science e.g. in Monte Carlo simulations methods. It is also a computationally intensive operation especially for high quality random number generation. In this paper, we present the design and implementation of a parallel implementation of one of the most widely used random number generators, namely […]
Mar, 20

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning

NVIDIA have released a new platform (CUDA) for general purpose computing on their graphical processing units (GPU). This paper evaluates use of this platform for statistical machine learning applications. The transfer rates to and from the GPU are measured, as is the performance of matrix vector operations on the GPU. An implementation of a sparse […]
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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 12.2
  • SDK: 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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