9363
Dongliang Xu, Hongli Zhang, Yujian Fan
Graphics Processing Unit (GPU) has been converted to general purpose parallel processor devices from a single rendering. It performed far better than the CPU in many fields of science. String matching is widely used, especially in information retrieval, intrusion detection, Computational Biology etc. In this paper, we designed and implemented a GPU-based multi-string matching algorithm […]
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Dhanyu Amarasinghe, Ian Parberry
We present a method for simulating the melting and flowing of material in burning objects fast enough to be of use in video games where most of the graphical and computational resources are needed elsewhere. The standard practice of using particle engines or fluid dynamics for melting are far too costly for use in this […]
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Jose Unpingco, Juan Carlos Chaves
Recent trends in hardware development have led to graphics processing units (GPUs) evolving into highly-parallel, multi-core computing platforms suitable for computational science applications. Recently, GPUs such as the NVIDIA Tesla 20-series (with up to 448 cores) have become available to the High Performance Computing Modernization Program (HPCMP) user community. Traditionally, NVIDIA GPUs are programmed using […]
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Alexander M. Merritt, Vishakha Gupta, Abhishek Verma, Ada Gavrilovska, Karsten Schwan
Systems with specialized processors such as those used for accel- erating computations (like NVIDIA’s graphics processors or IBM’s Cell) have proven their utility in terms of higher performance and lower power consumption. They have also been shown to outperform general purpose processors in case of graphics intensive or high performance applications and for enterprise applications […]
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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
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  • 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
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  • 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

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