Ramiro Marco Figuera
In this Master Thesis an analysis of illumination conditions at the lunar south pole using parallel computing techniques is presented. Due to the small inclination (1.54o) of the lunar rotational axis with respect to the ecliptic plane and the topography of the lunar south pole, which allows long illumination periods, the study of illumination conditions […]
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Prajakta Tapkir, Saurabh Thakur, C. Bhattacharya
High resolution imagery from synthetic aperture radar (SAR) video data requires numerical computations of the order of gigaflops (GFLOP). The computational burden increases with the image size and the amount of input raw video signals. General purpose graphic processor units (GPGPU) can play a pivotal role in parallel processing the raw video data to generate […]
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Konstantinos G. Derpanis, Mikhail Sizintsev, Kevin J. Cannons, Richard P. Wildes
This paper provides a unified framework for the interrelated topics of action spotting, the spatiotemporal detection and localization of human actions in video, and action recognition, the classification of a given video into one of several predefined categories. A novel compact local descriptor of video dynamics in the context of action spotting and recognition is […]
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Mattias Larsson
This thesis reviews if OpenCL is a suitable and cost effective platform for algorithm development in health care systems. Aspects such as maintainability, performance, portability and integration with high-level languages (in this case Python) are analyzed. The review is done by implementing one part of a dose calculation algorithm that is complex enough to provide […]
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Wei Zhang
Scheduling on heterogeneous parallel and distributed computing environment has been studied for decades. Based on different assumptions, researchers have proposed several algorithms and heuristics aiming to improve the performance of parallel applications. Most of these works focus on clusters of CPUs or grid-based environments where heterogeneity is created by processors and networks of varying speeds. […]
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Richard Membarth, Jan-Hugo Lupp, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert
For medical imaging applications, a timely execution of tasks is essential. Hence, running multiple applications on the same system, scheduling with the capability of task preemption and prioritization becomes mandatory. Using GPUs as accelerators in this domain, imposes new challenges since GPU’s common FIFO scheduling does not support task prioritization and preemption. As a remedy, […]
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Harshit Kharbanda, Roy H. Campbell
Neural networks allow the implementation of complicated applications such as stock market predictions on low-end PCs. However, the training of neural networks can take many hours on a PC. In this paper we propose a technique for training complicated neural networks on a commodity GPU (available in a low-end PC) that completes 6 times faster […]
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Julian Ortega, Helmuth Trefftz, Christian Trefftz
The AES block cipher cryptographic algorithm is widely used and it is resource intensive. An existing sequential open source implementation of the algorithm was parallelized on multi-core microprocessors and GPUs. Performance results are presented.
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Akio Doi, Hiroki Takahashi, Taro Mawatari, Sachio Mega
In this paper, we present the development of a highspeed volume rendering system that combines 3D texture compression and parallel programming techniques for rendering multiple high-resolution 3D images obtained with medical or industrial CT. The 3D texture compression algorithm (DXT5) provides extremely high efficiency since it reduces the memory consumption to 1/4 of the original […]
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Satish Chikkagoudar, Kai Wang, Mingyao Li
BACKGROUND: Gene-gene interaction analysis in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no […]

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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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