11252
Diming Zhang, Yuanjiang Li
Laboratory experience is an essential factor for engineering and science education. Virtual laboratories are widely used by universities and research institutions in various kinds of academic sectors. However, general virtual laboratories always have some weakness for computer graphics which its experiment needs to be done in high performance computers. In the assessment of a graduate […]
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Knut Skogstrand Gjerden
Most relatively modern desktop or even laptop computers contain a graphics card useful for more than showing colors on a screen. In this paper, we make a case for why you should learn enough about GPU (graphics processing unit) computing to use as an accelerator or even replacement to your CPU code. We include an […]
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Dan Connors
Computer Vision (CV) is a rapidly growing field, intent on enabling computers to process, analyze, and understand the information of images to produce structured information and/or make decisions. In recent years, interest in computer vision has grown in part as a result of both cheaper and more capable cameras, but also largely because of affordable […]
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David Bunde, Karen L. Karavanic, Jens Mache
How can parallel computing topics be incorporated into core courses that are taken by the majority of undergraduate students? This paper reports our experiences adding GPU computing with CUDA into the core undergraduate computer organization course at two different colleges. We have found that even though programming in CUDA is not necessarily easy, programmer control […]
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Alexander Breuer, Michael Bader
We present a software package that supports teaching different parallel programming models in a computational science and engineering context. It implements a Finite Volume solver for the shallow water equations, with application to tsunami simulation in mind. The numerical model is kept simple, using patches of Cartesian grids as computational domain, which can be connected […]
Jan Novak, Anton Kaplanyan, Gabor Liktor, Carsten Dachsbacher
Convolution of two functions is an important mathematical operation that found heavy application in signal processing. In computer graphics and image processing we usually work with discrete functions (e.g. an image) and apply a discrete form of the convolution to remove high frequency noise, sharpen details, detect edges, or otherwise modulate the frequency domain of […]
Chris Lupo, Zoe J. Wood, Christine Victorino
Massively parallel Graphics Processing Unit (GPU) hardware has become increasingly powerful, available and affordable. Software tools have also advanced to the point that programmers can write general purpose parallel programs that take advantage of the large number of compute cores available in the hardware. With literally hundreds of compute cores available on a single device, […]
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Peter E. Strazdins
We detail the design and experiences in delivering a specialty multicore computing course whose materials are openly available. The course ambitiously covers three multicore programming paradigms: shared memory (OpenMP), device (CUDA) and message passing (RCCE), and involves significant practical work on their respective platforms: an UltraSPARC T2, Fermi GPU and the Single-Chip Cloud Computer. Specialized […]
Michael Steffen, Phillip Jones, Joseph Zambreno
Since its introduction over two decades ago, graphics hardware has continued to evolve to improve rendering performance and increase programmability. While most undergraduate courses in computer graphics focus on rendering algorithms and programming APIs, we have recently created an undergraduate senior elective course that focuses on graphics processing and architecture, with a strong emphasis on […]
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Erik Wynters
This paper is an introduction to general-purpose computing on graphics processing units. This involves taking advantage of the parallel processing power of modern graphics cards to do general purpose computation. The CUDA architecture used for general purpose computations on NVIDIA graphics cards is described, and important features affecting the run times of CUDA programs are […]
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Mark Richards, Scott Lathrop
Many new users of TeraGrid or other HPC resources are scientists or other domain experts by training and are not necessarily familiar with core principles, practices, and resources within the HPC community. As a result, they often make inefficient use of their own time and effort and of the computing resources as well. In 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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  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
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  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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