Wei Li, Sara McMains
We present a new approach for computing the voxelized Minkowski sum (excluding any enclosed voids) of two polyhedral objects using programmable Graphics Processing Units (GPUs). We first cull out surface primitives that will not contribute to the final boundary of the Minkowski sum, analyzing and adaptively bounding the rounding errors of the culling algorithm to […]
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Wenbin Li, Sven Simon, Steffen Kiess
Simulation results depend not only on the precision of the floating point arithmetic with respect to the numerical accuracy of the results. They are also sensitive to differences of floating point arithmetic implementations of different hybrid and parallel computing systems such as CPUs, GPUs, dedicated processors like the Cell processor or the GRAPE special-purpose computer […]
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Lancelot Perrotte, Bruno Bodin, Laurent Chodorge
Before an intervention on a nuclear site, it is essential to study different scenarios to identify the less dangerous one for the operator. Therefore, it is mandatory to dispose of an efficient dosimetry simulation code with accurate results. One classical method in radiation protection is the straight-line attenuation method with build-up factors. In the case […]
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Devon Yablonski
This thesis presents an analysis of numerical accuracy issues that are found in many scientific GPU applications due to floating-point computation. Two widely held myths about floating-point on GPUs are that the CPU’s answer is more precise than the GPU version and that computations on the GPU are unavoidably different from the same computations on […]
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Xiaohua Shi, Chuang Li, Xu Wang, Kang Li
We introduced four prototypes of General Purpose GPU solutions by Compute Unified Device Architecture (CUDA) on NVidia GeForce 8800GT and Tesla C870 for a practical Curved Ray Prestack Kirchhoff Time Migration program, which is one of the most widely adopted imaging methods in the seismic data processing industry. We presented how to re-design and re-implement […]
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Michela Taufer, Omar Padron, Philip Saponaro, Sandeep Patel
The advent of general purpose graphics processing units (GPGPU’s) brings about a whole new platform for running numerically intensive applications at high speeds. Their multi-core architectures enable large degrees of parallelism via a massively multi-threaded environment. Molecular dynamics (MD) simulations are particularly well-suited for GPU’s because their computations are easily parallelizable. Significant performance improvements are […]
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Jeremy Jean, Stef Graillat
Recently, parallel computation has become necessary to take full advantage of the gains allowed by Moore’s law. Many scientific and engineering applications exhibit data parallelism but might not make full use of it. Some ubiquitous operations such that the dot product can easily be parallelized and then make good use of available hardware, like multi-core […]
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A. Anderson, W. Goddard, P. Schroder
Quantum Monte Carlo (QMC) is among the most accurate methods for solving the time independent Schrodinger equation. Unfortunately, the method is very expensive and requires a vast array of computing resources in order to obtain results of a reasonable convergence level. On the other hand, the method is not only easily parallelizable across CPU clusters, […]
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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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