4308

Posts

Jun, 2

Acceleration of LOD-FDTD Method Using Fundamental Scheme on Graphics Processor Units

This letter presents the acceleration of locally one-dimensional finite-difference time-domain (LOD-FDTD) method using fundamental scheme on graphics processor units (GPUs). Compared to the conventional scheme, the fundamental LOD-FDTD (denoted as FLOD-FDTD) scheme has its right-hand sides cast in the simplest form without involving matrix operators. This leads to a substantial reduction in floating-point operations as […]
Jun, 2

Swarm’s flight: Accelerating the particles using C-CUDA

With the development of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform, several areas of knowledge are being benefited with the reduction of the computing time. Our goal is to show how optimization algorithms inspired by Swarm Intelligence can take profit from this technology. In this paper, we provide an implementation […]
Jun, 2

Spherical harmonic transform on heterogeneous architectures using hybrid programming

Spherical Harmonic Transforms (SHT) are at the heart of many scientific and practical ap- plications ranging from climate modeling to cosmological observations. In many of these areas a new wave of exciting, cutting-edge science goals have been recently proposed calling for simulations and analyses of actual experimental or observational data at very high resolutions, accompanied […]
Jun, 1

GPS forward model computing study on CPU/GPU co-processing parallel system using CUDA

Profiles of refraction and bending angle, which computed through the forward model for GPSRO (Global Positioning System radio occultation), are extremely important for GPS radio occultation data assimilation to the forecast system of NWP (Numerical Weather Prediction). The daily processing of GPS RO data in assimilation system costs amount of time, thus there is an […]
Jun, 1

GPUMLib: A new Library to combine Machine Learning algorithms with Graphics Processing Units

The Graphics Processing Unit (GPU) is a highly parallel, many-core device with enormous computational power, especially well-suited to address Machine Learning (ML) problems that can be expressed as data-parallel computations. As problems become increasingly demanding, parallel implementations of ML algorithms become critical for developing hybrid intelligent real-world applications. The relative low cost of GPUs combined […]
Jun, 1

A GPU/CUDA implementation of the collection-diffusion model to compute SER of large area and complex circuits

This work reports the CUDA implementation of the collection-diffusion model to compute the soft-error rate (SER) of large area and/or complex circuits on graphics processing units (GPU). We detail the time parallelization introduced in the algorithm to accelerate by one order of magnitude the SER calculation. Code performances are evaluated on a NVIDIA Tesla C1060 […]
Jun, 1

CUDA Accelerated LTL Model Checking

Recent technological developments made available various many-core hardware platforms. For example, a SIMD-like hardware architecture became easily accessible for many users who have their computers equipped with modern NVIDIA GPU cards with CUDA technology. In this paper we redesign the maximal accepting predecessors algorithm for LTL model checking in terms of matrix-vector product in order […]
Jun, 1

Implementations of Parallel Computation of Euclidean Distance Map in Multicore Processors and GPUs

Given a 2-D binary image of size nxn, Euclidean Distance Map (EDM) is a 2-D array of the same size such that each element is storing the Euclidean distance to the nearest black pixel. It is known that a sequential algorithm can compute the EDM in O(n2) and thus this algorithm is optimal. Also, work-time […]
Jun, 1

The Parallel Processing Based on CUDA for Convolution Filter FDK Reconstruction of CT

Computed tomography (CT) technology has been used in many fields. But the slow speed of CT image reconstruction is unbearable in some situation. The parallel processing based on graphic processing unit (GPU) is a great option to accelerate the speed of CT image reconstruction. But the general purpose GPU program model is difficult to use […]
Jun, 1

Parallel particle filter algorithm in face tracking

This paper proposed a parallel particle filter algorithm with the help of GPU (Graphic Processing Unit) in face tracking. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of […]
Jun, 1

Robust Low Complexity Feature Tracking using CUDA

In this paper, we propose a real-time video processing implementation of a Robust Low Complexity Feature Tracking (RLCT) algorithm on GPU (Graphics Processing Unit) using the CUDA (Compute Unified Device Architecture) paradigm. The RLCT outperforms state-of-the-art implementations of pyramidal KLT (Kanade-Lucas-Tomasi) on GPU by removing the overhead of the image pyramid construction, by predicting the […]
Jun, 1

Texture-Based Visualization of Unsteady 3D Flow by Real-Time Advection and Volumetric Illumination

This paper presents an interactive technique for the dense texture-based visualization of unsteady 3D flow, taking into account issues of computational efficiency and visual perception. High efficiency is achieved by a 3D graphics processing unit (GPU)-based texture advection mechanism that implements logical 3D grid structures by physical memory in the form of 2D textures. This […]
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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.

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Node 1
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  • 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
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  • 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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