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Commit 2d558f36 by Őry Máté
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paper: rc4

parent 61902edb
Showing with 361 additions and 332 deletions
miscellaneous/paper/sozopol13/netarch.eps
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miscellaneous/paper/sozopol13/proceedings.tex
...@@ -35,7 +35,8 @@ Scientific Computing} ...@@ -35,7 +35,8 @@ Scientific Computing}
Nowadays more and more general purpose workstations installed in a student laboratory have a built in multi-core CPU and graphics card providing significant computing power. In most cases the utilization of these resources is low, and limited to lecture hours. The concept of utility computing plays an important role in technological development. Nowadays more and more general purpose workstations installed in a student laboratory have a built in multi-core CPU and graphics card providing significant computing power. In most cases the utilization of these resources is low, and limited to lecture hours. The concept of utility computing plays an important role in technological development.
In this paper, we introduce a cloud management system which enables the simultaneous use of both dedicated resources and opportunistic environment. All the free workstations are to a resource pool, and can be used like ordinary cloud resources. Our solution leverages the advantages of HTCondor and OpenNebula systems. In this paper, we introduce a cloud management system which enables the simultaneous use of both dedicated resources and opportunistic environment. All the free workstations are to a resource pool, and can be used like ordinary cloud resources. Our solution leverages the advantages of HTCondor and OpenNebula systems.
Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can dramatically accelerate scientific applications used for various simulations. Our business model harnesses the computing power of GPUs as well, using the needed amount of unused machines. Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose parallel computing platforms that can dramatically accelerate scientific applications used for various simulations. Our business model harnesses the computing power of GPUs as well, using the needed amount of unused machines.
Our pilot infrastructure consists of a high performance cluster and 28 workstations with dual-core CPUs and dedicated graphics cards. Altogether we can use 10,752 CUDA cores through the network. Our pilot infrastructure consists of a high performance cluster of 7 compute nodes with a sum of 76 physical cores and 304 GiB memory,
and 28 workstations with dual-core CPUs and dedicated graphics cards. Altogether we can use 10,752 CUDA cores through the network.
\keywords{Cloud, GPGPU, Grid, HTC, Utility computing} \keywords{Cloud, GPGPU, Grid, HTC, Utility computing}
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