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Posts

May, 13

Fast interpolated cameras by combining a GPU based plane sweep with a max-flow regularisation algorithm

The work presents a method for the high speed calculation of crude depth maps. Performance and applicability are illustrated for view interpolation based on two input video streams, but the algorithm is perfectly amenable to multicamera environments. First a fast plane sweep algorithm generates the crude depth map. Speed results from hardware accelerated transformations and […]
May, 13

Fast short exact repeats finding on GPU

Repeat regions in DNA play very important roles in many vital biological functions. Repeats finding is always deemed as one of the most fundamental problems in genome sequencing and analysis, and exact repeats finding is the first step for many other repeats finding problems. This paper depicts the design and implementation issues of a fast […]
May, 13

GPU-accelerated synthetic aperture radar backprojection in CUDA

Pleasingly parallel algorithms such as filtered back-projection have been documented to enjoy significant speedups when ported to run on a graphics processor instead of a standard CPU. Presented here is a two-dimensional SAR backprojection implementation for a single GPU using the NVIDIA CUDA framework. Given that input range projections may be too large to fit […]
May, 13

Fast Katsevich Algorithm Based on GPU for Helical Cone-Beam Computed Tomography

Katsevich reconstruction algorithm represents a breakthrough for helical cone-beam computed tomography (CT) reconstruction, because it is the first exact cone-beam reconstruction algorithm of filtered backprojection (FBP) type with 1-D shift-invariant filtering. Although FBP-type reconstruction algorithm is effective, 3-D CT reconstruction is time-consuming, and the accelerations of Katsevich algorithm on CPU or cluster have been widely […]
May, 13

Why does PHM matter? – Nvidia’s GPU problems reviewed

The value of Prognostics and Health Management (“PHM”) is best understood by looking at what happens when PHM is not utilized. In the growing field of PHM, the mistakes of others often provide us with valuable real world lessons that should fuel greater development, understanding and use of PHM. The science of PHM should play […]
May, 13

An Efficient Approach for Generating Pencil Filter and Its Implementation on GPU

Traditional pencil drawing methods have their own drawbacks, such as modeling complexity and higher time-consuming. Thus, they are difficult to be suitable for the real-time rendering. In this paper, we present a new pencil texture generating method based on the pencil filter. This approach can conveniently generate the pencil drawing effect by convoluting the input […]
May, 13

CUgrep: A GPU-based high performance multi-string matching system

String matching is one of the oldest and most pervasive problems in computer science. Nowadays applications related to string matching can be found everywhere. Meanwhile, the heterogeneous processing of CPU+GPU has become the popular parallel platform in solving high performance computing applications. This paper proposes a GPU-based multi-string matching algorithm, CUgrep, and uses this algorithm […]
May, 13

Reducing shading on GPUs using quad-fragment merging

Current GPUs perform a significant amount of redundant shading when surfaces are tessellated into small triangles. We address this inefficiency by augmenting the GPU pipeline to gather and merge rasterized fragments from adjacent triangles in a mesh. This approach has minimal impact on output image quality, is amenable to implementation in fixed-function hardware, and, when […]
May, 13

Fast network centrality analysis using GPUs

BACKGROUND: With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU) provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the […]
May, 12

Fast gain-adaptive KLT tracking on the GPU

High-performance feature tracking from video input is a valuable tool in many computer vision techniques and mixed reality applications. This work presents a refined and substantially accelerated approach to KLT feature tracking performed on the GPU. Additionally, a global gain ratio between successive frames is estimated to compensate for changes in the camera exposure. The […]
May, 12

Towards real-time tomography: Fast reconstruction algorithms and GPU implementation

Synchrotron X-ray tomographic microscopy is a powerful technique which allows fast non-destructive, high resolution, quantitative volumetric investigations on diverse samples. Highly brilliant X-rays delivered by third generation synchrotron facilities coupled with modern detector technology permit routinely acquisition of high resolution tomograms in few minutes, making high throughput experiments a reality and bringing real-time tomography closer. […]
May, 12

An Improved Study of Physically Based Fluid Simulation on GPU

A feasible routine for complex fluid simulation is presented, consisting of the pre-processing and runtime stage. The pre-processing stage generates all computation-aided textures, which speeds up the simulating and rendering at run-time stage. We improve the unreasonable processing in most previous methods and give a correct discretized solution of Poisson equation. To improve the computational […]
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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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