Hans-Friedrich Pabst, Jan P. Springer, Andre Schollmeyer, Robert Lenhardt, Christian Lessig, Bernd Froehlich
We propose a conceptual extension of the standard triangle-based graphics pipeline by an additional intersection stage. The corresponding intersection program performs ray-object intersection tests for each fragment of an object’s bounding volume. The resulting hit fragments are transferred to the fragment shading stage for computing the illumination and performing further fragment operations. Our approach combines […]
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Dominik Goddeke
The main contribution of this thesis is to demonstrate that graphics processors (GPUs) as representatives of emerging many-core architectures are very well-suited for the fast and accurate solution of large sparse linear systems of equations, using parallel multigrid methods on heterogeneous compute clusters. Such systems arise for instance in the discretisation of (elliptic) partial differential […]
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Owen Harrison, John Waldron
Graphics Processing Units (GPUs) present large potential performance gains within stream processing applications over the standard CPU. These performance gains are best realised when high computational intensity is required across large amounts of mostly independent input elements. The GPU’s success in general purpose stream processing has been demonstrated in many diverse fields, though attempts to […]
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Andrew Thall
Double-float (df64) and quad-float (qf128) numeric types can be implemented on current GPU hardware and used efficiently and effectively for extended-precision computational arithmetic. Using unevaluated sums of paired or quadrupled f32 single-precision values, these numeric types provide approximately 48 and 96 bits of mantissa respectively at single-precision exponent ranges for computer graphics, numerical, and general-purpose […]
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Oliver Fluck, Shmuel Aharon, Daniel Cremers, Mikael Rousson
Due to the immense computational power of today’s graphics processors (GPU), general purpose computation on GPUs has become a vivid research area. The performance of algorithms running on GPUs highly depends on how well they can be arranged to fit and exploit the processors single instruction multiple data (SIMD) architecture. Many tasks that are considered […]
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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

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