Iype P. Joseph
Multicore CPUs (Central Processing Units) and GPUs (Graphics Processing Units) are omnipresent in today’s market-leading smartphones and tablets. With CPUs and GPUs getting more complex, maximizing hardware utilization is becoming problematic. The challenges faced in GPGPU (General Purpose computing using GPU) computing on embedded platforms are different from their desktop counterparts due to their memory […]
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Carolin Wolf
Many computationally intensive applications profit by parallel execution, based on using multiple cores in CPUs, data-parallel GPGPU processing or even several machines like in clusters. However, changing a program to run in parallel requires a high effort and is therefore a time-consuming step during development. During the implementation, it is necessary to consider many steps […]
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Naoki Shibata, Shinya Yamamoto
With an aim to realizing highly accurate position estimation, we propose in this paper a method for efficiently and accurately detecting the 3D positions and poses of traditional fiducial markers with black frames in high-resolution images taken by ordinary web cameras. Our tracking method can be efficiently executed utilizing GPGPU computation, and in order to […]
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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A.Y.Doroshenko, K.A. Zhereb, O.G.Beketov, M.V. Gnynjuk
A flexible and extensible simulation tool architecture, called gpusim, is proposed for heterogeneous grid systems with graphics accelerators. The tool is based on open source Java framework GridSim. Checking for models adequacy and their initial investigation has been performed using known examples of parallel computation problems. The tool allows choosing the most optimal setting parameters […]
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Jonathan Passerat-Palmbach
The race to computing power increases every day in the simulation community. A few years ago, scientists have started to harness the computing power of Graphics Processing Units (GPUs) to parallelize their simulations. As with any parallel architecture, not only the simulation model implementation has to be ported to the new parallel platform, but all […]
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Akihiro Hayashi, Max Grossman, Jisheng Zhao, Jun Shirako, Vivek Sarkar
General purpose computing on GPUs (GPGPU) can enable significant performance and energy improvements for certain classes of applications. However, current GPGPU programming models, such as CUDA and OpenCL, are only accessible by systems experts through low-level C/C++ APIs. In contrast, large numbers of programmers use high-level languages, such as Java, due to their productivity advantages […]
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Guodong Han, Chenggang Zhang, King Tin Lam, Cho-Li Wang
GPU-based many-core accelerators have gained a footing in supercomputing. Their widespread adoption yet hinges on better parallelization and load scheduling techniques to utilize the hybrid system of CPU and GPU cores easily and efficiently. This paper introduces a new userfriendly compiler framework and runtime system, dubbed Japonica, to help Java applications harness the full power […]
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Akihiro Hayashi, Max Grossman, Jisheng Zhao, Jun Shirako, Vivek Sarkar
The initial wave of programming models for general-purpose computing on GPUs, led by CUDA and OpenCL, has provided experts with low-level constructs to obtain significant performance and energy improvements on GPUs. However, these programming models are characterized by a challenging learning curve for non-experts due to their complex and low-level APIs. Looking to the future, […]
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Carolin Wolf, Georg Dotzler, Ronald Veldema, Michael Philippsen
For scientists, it is advantageous to use a high level of abstraction for programming their simulations, so that they can focus on the problem at hand instead of struggling with low-level details. However, current HPC clusters with multiple GPUs per node only offer explicit communication to and from the GPUs, require manual work to keep […]
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Raquel Medina Dominguez
The traditional CPU is able to run only a few complex threads concurrently. By contrast, a GPU (Graphics Processing Unit) allows a concurrent execution of hundreds or thousands of simpler threads. The GPU was originally designed for a computer graphics, but nowadays it is being used for generalpurpose computation using a GPGPU (General Purpose GPU) […]
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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 […]
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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 11.4
  • SDK: AMD APP SDK 2.8
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • 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 5.0.35, AMD APP SDK 2.8

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