Michelle Perry
This dissertation studies a graphical processing unit (GPU) construction of Bayesian neural networks (BNNs) using large training data sets. The goal is to create a program for the mapping of phenomenological Minimal Supersymmetric Standard Model (pMSSM) parameters to their predictions. This would allow for a more robust method of studying the Minimal Supersymmetric Standard Model, […]
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Yoshihito Sano, Yoshiaki Kadono, Naoki Fukuta
To realize a simulation which can handle hundreds of thousands of negotiating agents keeping their detailed behaviors, massive amount of computational power is required. Also having good programmability of agents’ codes to realize complex behaviors is essential to realize it. On deploying such negotiating agents on an agent simulation, it is important to be able […]
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Nicolo' Savioli
The aim of my thesis is to parallelize the Weighting Histogram Analysis Method (WHAM), which is a popular algorithm used to calculate the Free Energy of a molecular system in Molecular Dynamics simulations. WHAM works in post processing in cooperation with another algorithm called Umbrella Sampling. Umbrella Sampling has the purpose to add a biasing […]
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Herve Paulino, Eduardo Marques
Heterogeneity is omnipresent in today’s commodity computational systems, which comprise at least one multi-core Central Processing Unit (CPU) and one Graphics Processing Unit (GPU). Nonetheless, all this computing power is not being exploited in mainstream computing, as the programming of these systems entails many details of the underlying architecture and of its distinct execution models. […]
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Philippe Helluy, Jonathan Jung
In this paper, we propose a new very simple numerical method for solving liquid-gas compressible flows. Such flows are difficult to simulate because classic conservative finite volume schemes generate pressure oscillations at the liquid-gas interface. We extend to several dimensions the random choice scheme that we have constructed in [2]. The extension is performed through […]
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Tommy MacWilliam, Cris Cecka
We present CrowdCL, an open-source framework for the rapid development of volunteer computing and OpenCL applications on the web. Drawing inspiration from existing GPU libraries like PyCUDA, CrowdCL provides an abstraction layer for WebCL aimed at reducing boilerplate and improving code readability. CrowdCL also provides developers with a framework to easily run computations in the […]
Bilal Jan, Bartolomeo Montrucchio, Carlo Ragusa, Fiaz Gul Khan, Omar Khan
This paper presents a comparative analysis of the three widely used parallel sorting algorithms: OddEven sort, Rank sort and Bitonic sort in terms of sorting rate, sorting time and speed-up on CPU and different GPU architectures. Alongside we have implemented novel parallel algorithm: min-max butterfly network, for finding minimum and maximum in large data sets. […]
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Per-Erik Mahl, Tim Liberg
Efficient model checking is important in order to make this type of software verification useful for systems that are complex in their structure. If a system is too large or complex then model checking does not simply scale, i.e., it could take too much time to verify the system. This is one strong argument for […]
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Han Dong, Dibyajyoti Ghosh, Fahad Zafar, Shujia Zhou
Current multi- and many-core computing typically involves multi-core Central Processing Units (CPU) and many-core Graphical Processing Units (GPU) whose architectures are distinctly different. To keep longevity of application codes, it is highly desirable to have a programming paradigm to support these current and future architectures. Open Computing Language (OpenCL) is created to address this problem. […]
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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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