May, 4

A 57mW embedded mixed-mode neuro-fuzzy accelerator for intelligent multi-core processor

Artificial intelligence (Al) functions are becoming important in smartphones, portable game consoles, and robots for such intelligent applications as object detection, recognition, and human-computer interfaces (HCI). Most of these functions are realized in software with neural networks (NN) and fuzzy systems (FS), but due to power and speed limitations, a hardware solution is needed. For […]
May, 4

Accelerated polyhedral visual hulls using OpenCL

We present a method for reconstruction of the visual hull (VH) of an object in real-time from multiple video streams. A state of the art polyhedral reconstruction algorithm is accelerated by implementing it for parallel execution on a multi-core graphics processor (GPU). The time taken to reconstruct the VH is measured for both the accelerated […]
May, 4

Accelerating floating-point fitness functions in evolutionary algorithms: a FPGA-CPU-GPU performance comparison

Many large combinatorial optimization problems tackled with evolutionary algorithms often require very high computational times, usually due to the fitness evaluation. This fact forces programmers to use clusters of computers, a computational solution very useful for running applications of intensive calculus but having a high acquisition price and operation cost, mainly due to the Central […]
May, 4

Parallel AES algorithm for fast Data Encryption on GPU

With the improvement of cryptanalysis, More and more applications are starting to use Advanced Encryption Standard (AES) instead of Data Encryption Standard (DES) to protect their information security. However, current implementations of AES algorithm suffer from huge CPU resource consumption and low throughput. In this paper, we studied the technologies of GPU parallel computing and […]
May, 4

High performance predictable histogramming on GPUs: exploring and evaluating algorithm trade-offs

Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image processing, due to their massive parallel processing power. Many image processing applications use the histogramming algorithm, which fills a set of bins according to the frequency of occurrence of pixel values taken from an input image. Histogramming has been mapped on […]
May, 4

Efficient Parallel Intra-prediction Mode Selection Scheme for 4×4 Blocks in H.264

An intra-prediction mode with 4×4 block and 16×16 block sizes for luma component and 8×8 block size for chroma component is used in H.264 to improve the rate-distortion performance. However, the computational complexity of H.264 encoder is drastically increased due to the various intraprediction modes. Recently efficient hardware architectures were proposed for the fast execution […]
May, 4

Graphics hardware & GPU computing: past, present, and future

Modern GPUs have emerged as the world’s most successful parallel architecture. GPUs provide a level of massively parallel computation that was once the preserve of supercomputers like the MasPar and Connection Machine. For example, NVIDIA’s GeForce GTX 280 is a fully programmable, massively multithreaded chip with up to 240 cores, 30,720 threads and capable of […]
May, 3

Large data real-time classification with Non-negative Matrix Factorization and Self-Organizing Maps on GPU

This article brings an interesting comparison of two different methods, which were implemented on GPU and help us to detect system intrusions. Generally, both of them can be widely used in the area of information retrieval. The modern trends of parallel computation have a significant influence on performance of implemented methods (Non-negative Matrix Factorization (NMF) […]
May, 3

Interactive Two-sided Refraction for Dynamic Object on GPU

This paper introduces an approach to interactively compute two-sided refraction on GPU and gains comparable realistic effects with ray tracing, while most current interactive techniques are restricted to single refraction. Our approach is also efficient for total internal reflection (TIR). When processing rigid movable object, we render the object’s normal to a cube map first […]
May, 3

Real-Time Animating and Rendering of Large Scale Grass Scenery on GPU

A method for animating and rendering large grass scenery in real-time is presented. The grass scenery consists of geometry-based grass blades and image-based quads covered with clumps of grass texture. Geometry-based grass blades are rendered for animating interactively for grass scenery close to camera. Imaged-based quads covered with clumps of grass texture are rendered for […]
May, 3

Real-time ocean wave motion simulation based on statistic model and GPU programming

This paper will discuss the method of simulating ocean waves basing on statistic model and GPU programming. In the method, the ocean surface is viewed as a height field, which is obtained by Phillips spectrum and 2D FFT. In order to realize the realistic scene, we take full advantage of the extended functions of GPU […]
May, 3

GPU-based surface oriented interslice directional interpolation for volume visualization

Laser scanning confocal endomicroscopy (LSCEM) is emerging as an in vivo 3D cellular imaging technology. However, the image datasets acquired from LSCEM are generally with low z-depth resolution, which causes artifacts in volume rendering. Intermediate data between consecutive images needs to be generated. One common solution uses trilinear interpolation which more or less reduces the […]
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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

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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