Posts

Apr, 7

A New Digital Repository for Hyperspectral Imagery with Unmixing-Based Retrieval Functionality Implemented on GPUs

Over the last few years, hyperspectral image data have been collected for a large number of locations over the world, using a variety of instruments for Earth observation. In addition, several new hyperspectral missions will become operational in the near future. Despite the increasing availability and large volume of hyperspectral data in many applications, there […]
Apr, 7

State of the Art Report on Real-time Rendering with Hardware Tessellation

For a long time, GPUs have primarily been optimized to render more and more triangles with increasingly flexible shading. However, scene data itself has typically been generated on the CPU and then uploaded to GPU memory. Therefore, widely used techniques that generate geometry at render time on demand for the rendering of smooth and displaced […]
Apr, 7

Detection of a faint fast-moving near-Earth asteroid using synthetic tracking technique

We report a detection of a faint near-Earth asteroid (NEA), which was done using our synthetic tracking technique and the CHIMERA instrument on the Palomar 200-inch telescope. This asteroid, with apparent magnitude of 23, was moving at 5.97 degrees per day and was detected at a signal-to-noise ratio (SNR) of 15 using 30 sec of […]
Apr, 7

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

In this report, we analyze the computational and performance aspects of current state-of-the-art object recognition and optical flow algorithms. First, we identify important algorithms for object recognition and optical flow, then we perform a pattern decomposition to identify key computations. We include profiles of the runtime and energy efficiency (GFLOPS/W) for our implementation of these […]
Apr, 7

GPU-Accelerated Face Detection Algorithm

This work is an overview of a preliminary experience in developing high-performance face detection accelerated by GPU co-processors. The objective is to illustrate the advantages and difficulties encountered while utilizing the GPU technology to perform face detection. Moreover the introduced implementation is a much faster than currently existing techniques. Previous techniques for speeding up face […]
Apr, 7

A Study on Efficient Application Mapping on Parallel Computing Accelerators

Since the invention of electronic computers, their performance has been constantly advanced. The recent progress of micro processors in performance has been mainly achieved by increasing the number of cores on a device, instead of increasing working frequency. In addition, because of increasing of density of semiconductors, not only computational performance but also density of […]
Apr, 7

Parallel processing for SAR image generation in CUDA – GPGPU platform

High resolution imagery from synthetic aperture radar (SAR) video data requires numerical computations of the order of gigaflops (GFLOP). The computational burden increases with the image size and the amount of input raw video signals. General purpose graphic processor units (GPGPU) can play a pivotal role in parallel processing the raw video data to generate […]
Apr, 7

Acceleration of a Full-scale Industrial CFD Application with OP2

Hydra is a full-scale industrial CFD application used for the design of turbomachinery at Rolls Royce plc. It consists of over 300 parallel loops with a code base exceeding 50K lines and is capable of performing complex simulations over highly detailed unstructured mesh geometries. Unlike simpler structured-mesh applications, which feature high speed-ups when accelerated by […]
Apr, 7

Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-sigma formats on NVIDIA GPUs

Numerical methods in sparse linear algebra typically rely on a fast and efficient matrix vector product, as this usually is the backbone of iterative algorithms for solving eigenvalue problems or linear systems. Against the background of a large diversity in the characteristics of high performance computer architectures, it is a challenge to derive a cross-platform […]
Apr, 6

Optimizing Krylov Subspace Solvers on Graphics Processing Units

Krylov subspace solvers are often the method of choice when solving sparse linear systems iteratively. At the same time, hardware accelerators such as graphics processing units (GPUs) continue to offer significant floating point performance gains for matrix and vector computations through easy-to-use libraries of computational kernels. However, as these libraries are usually composed of a […]
Apr, 6

A Low-Power Hybrid CPU-GPU Sort

This thesis analyses the energy efficiency of a low-power CPU-GPU hybrid architecture. We evaluate the NVIDIA Ion architecture, which couples an Intel Atom low power processor with an integrated GPU that has an order of magnitude fewer processors compared to traditional discrete GPUs. We attempt to create a system that balances computation and I/O capabilities […]
Apr, 6

High Performance Computing for Large Graphs of Internet Applications using GPU

The high speed CPU based routers currently in use could not handle the massive data required for real-time multimedia communication. Graphics processing units (GPUs) offer an appreciable alternative due to high computation power which results from their parallel execution units. This paper presents the implementation of the Dijkstra’s link state IP routing algorithm using GPU. […]
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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: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 11.4
  • SDK: AMD APP SDK 2.8
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8

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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