Aug, 5

Roberts edge detection algorithm based on GPU

With the development of the semiconductor technology, the GPU’s floating point computing capacity improves rapidly. How to apply the GPU technology to the non-graphic computing field becomes a highlight in the research of high performance computing. The Roberts edge detection algorithm is a typical image processing algorithms. A fast Roberts edge detection algorithm is presented […]
Aug, 5

GIS Polygon Overlay Processing: New Parallel Algorithm and System Prototype

Polygon overlay is one of the complex operations in computational geometry. It is applied in many fields such as Geographic Information Systems (GIS), computer graphics, VLSI CAD, etc. We have two significant results to report. Our first result is the first output-sensitive CREW PRAM algorithm for simple polygons, which can perform typical set operations including […]
Aug, 5

A Moving Least Squares Based Approach for Contour Visualization of Multi-Dimensional Data

Analysis of high dimensional data is a common task. Often, small multiples are used to visualize 1 or 2 dimensions at a time, such as in a scatterplot matrix. Associating data points between different views can be difficult though, as the points are not fixed. Other times, dimensional reduction techniques are employed to summarize the […]
Aug, 3

Integrating Profiling into MDE Compilers

Scientific computation requires more and more performance in its algorithms. New massively parallel architectures suit well to these algorithms. They are known for offering high performance and power efficiency. Unfortunately, as parallel programming for these architectures requires a complex distribution of tasks and data, developers find difficult to implement their applications effectively. Although approaches based […]
Aug, 3

Multithreading for Visual Effects

Tackle the Challenges of Parallel Programming in the Visual Effects Industry: In Multithreading for Visual Effects, developers from DreamWorks Animation, Pixar, Side Effects, Intel, and AMD share their successes and failures in the messy real-world application area of production software. They provide practical advice on multithreading techniques and visual effects used in popular visual effects […]
Aug, 3

Accelerating 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 […]
Aug, 3

Extending Lyapack for the Solution of Band Lyapunov Equations on Hybrid CPU-GPU Platforms

The solution of large-scale Lyapunov equations is an important tool for the solution of several engineering problems arising in optimal control and model order reduction. In this work we investigate the case when the coefficient matrix of the equations presents a band structure. Exploiting the structure of this matrix we can achive relevant reductions in […]
Aug, 3

Multi-Threaded Automatic Integration Using OpenMP and CUDA

Problems in many areas give rise to computationally expensive integrals that beg the need of efficient techniques to solve them, e.g., in computational finance for the modeling of cash flows; for the computation of Feynman loop integrals in high energy physics; and in stochastic geometry with applications to computer graphics. We demonstrate feasible numerical approaches […]
Aug, 2

Design of an FPGA-Based FDTD Accelerator Using OpenCL

High-performance computing systems with dedicated hardware on FPGAs can achieve power efficient computations compared with CPUs and GPUs. However, the hardware design on FPGAs needs more time than the software design on CPUs and GPUs. We designed an FDTD hardware accelerator using the OpenCL compiler for FPGAs in this paper. Since it is possible to […]
Aug, 2

An Analysis of OpenACC Programming Model: Image Processing Algorithms as a Case Study

Graphics processing units and similar accelerators have been intensively used in general purpose computations for several years. In the last decade, GPU architecture and organization changed dramatically to support an ever-increasing demand for computing power. Along with changes in hardware, novel programming models have been proposed, such as NVIDIA’s Compute Unified Device Architecture (CUDA) and […]
Aug, 2

Extracting Maximal Exact Matches on GPU

The revolution in high-throughput sequencing technologies accelerated the discovery and extraction of various genomic sequences. However, the massive size of the generated datasets raise several computational problems. For example, aligning the sequences or finding the similar regions in them, which is one of the crucial steps in many bioinformatics pipelines, is a time consuming task. […]
Aug, 2

Integrated Arrival and Departure Schedule Optimization Under Uncertainty

In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a post-analysis process of deterministic optimization, and imposing a safety buffer in deterministic optimization, are two ways to learn and alleviate the impact of uncertainty and to avoid […]
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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 13.1
  • SDK: AMD APP SDK 2.9
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 6.0.1, 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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