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Stochastic simulation; Molecular Dynamics; Atmospheric and climate modeling; Weather forecast investigation; Grid/Cloud Hybrid Virtualization; *
“GPGPU stands for General-Purpose computation on Graphics Processing Units, also known as GPU Computing. Graphics Processing Units (GPUs) are high-performance many-core processors capable of very high computation and data throughput.">
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<meta property="og:description" content="On September 28, 2009, a workstation Genesis GE-i940 Tesl, based on both GPGPU* and nVidia/CUDA** Technologies has been installed at DSA/LabMNCP.
It is a testbed for developing advanced simulation in the following research field:
Stochastic simulation; Molecular Dynamics; Atmospheric and climate modeling; Weather forecast investigation; Grid/Cloud Hybrid Virtualization; *
“GPGPU stands for General-Purpose computation on Graphics Processing Units, also known as GPU Computing. Graphics Processing Units (GPUs) are high-performance many-core processors capable of very high computation and data throughput."><meta property="og:image" content="/img/logohpsclab.png">
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<title>Genesis GE-i940 Tesla</title>
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<h1>Genesis GE-i940 Tesla</h1>
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<p>On September 28, 2009, a workstation Genesis GE-i940 Tesl, based on both GPGPU* and nVidia/CUDA** Technologies has been installed at DSA/LabMNCP.</p>
<p>It is a testbed for developing advanced simulation in the following research field:</p>
<ul>
<li><span style="color: blue;">Stochastic simulation;</li>
<li><span style="color: blue;">Molecular Dynamics;</li>
<li><span style="color: blue;">Atmospheric and climate modeling;</li>
<li><span style="color: blue;">Weather forecast investigation;</li>
<li><span style="color: blue;">Grid/Cloud Hybrid Virtualization;</li>
</ul>
<p><span style="color: blue;">*</span><br />
“GPGPU stands for General-Purpose computation on Graphics Processing Units, also known as GPU Computing. Graphics Processing Units (GPUs) are high-performance many-core processors capable of very high computation and data throughput. See more <a href="https://www.ibiblio.org/" target="_blank">here</a>.”</p>
<p><span style="color: blue;">**</span><br />
“NVIDIA® CUDA™ is a general purpose parallel computing architecture that leverages the parallel compute engine in NVIDIA graphics processing units (GPUs) to solve many complex computational problems in a fraction of the time required on a CPU. See more <a href="https://developer.nvidia.com/about-cuda" target="_blank">here</a>. “</p>
<table>
<thead>
<tr>
<th><span style="font-size: 18px; color: blue;">Hardware</span></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<td></td>
<td><img src="/img/ge.jpg" alt="ge-image" /></td>
</tr>
<tr>
<td>Mainboard</td>
<td>Asus x58/ICH10R 3 PCI-Express x16, 6 SAT, 2 SAS, 3+6 USB</td>
</tr>
<tr>
<td>CPU</td>
<td>i7-940 2,93 133 GHz fsb, Quad Core 8 Mb cache</td>
</tr>
<tr>
<td>RAM</td>
<td>6 x 2Gb DRR 3 1333 DIM</td>
</tr>
<tr>
<td>Hard Disk</td>
<td>2 x 500 Gb SATA 16Mb cache 7.200 RPM</td>
</tr>
<tr>
<td>GPU</td>
<td>1 Quadro FX5800 4Gb RAM</td>
</tr>
<tr>
<td></td>
<td>2 x Tesla C1060 4 Gb RAM</td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th><span style="font-size: 18px; color: blue;">Software</span></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<td>OS:</td>
<td><a href="https://www.centos.org/" target="_blank">GNU/Linux CentOs 5.3 64 Bit</a></td>
</tr>
<tr>
<td>Driver:</td>
<td><a href="https://www.nvidia.com/object/thankyou_linux.html?url=/compute/cuda/2_1/drivers/NVIDIA-Linux-x86_64-180.22-pkg2.run" target="_blank">nVidia Cuda 180.22 Linux 64bit</a></td>
</tr>
<tr>
<td>VMware:</td>
<td><a href="https://www.vmware.com/" target="_blank">VMware-server-2.0.2</a></td>
</tr>
</tbody>
</table>
<table>
<thead>
<tr>
<th><span style="font-size: 18px; color: blue;">OUTPUT of First Test:</span></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<td></td>
<td>Serial simulation(ms)</td>
<td>GPU(ms)</td>
</tr>
<tr>
<td>execution time for malloc</td>
<td>0.02</td>
<td>175.21 ms</td>
</tr>
<tr>
<td>execution time for RndGnr</td>
<td>51430.92</td>
<td>2283.19</td>
</tr>
<tr>
<td>execution time for init</td>
<td>275.48</td>
<td>0.31</td>
</tr>
<tr>
<td>execution time for computing</td>
<td>391391.12</td>
<td>329.19 ms</td>
</tr>
<tr>
<td>execution time for I/O</td>
<td>56822.77</td>
<td>64740.54 ms</td>
</tr>
<tr>
<td>execution time for GPU/CPU</td>
<td></td>
<td>198.43 ms</td>
</tr>
</tbody>
</table>
<p><span style="font-size: 18px; color: blue;">Output using GPU,</span>
<pre>
device 0 : Quadro FX 5800
device 1 : Tesla C1060
device 2 : Tesla C1060</p>
<p>Selected device: 2 <<<<<<<<<<<<<<<<<<</p>
<p>device 2 : Tesla C1060
major/minor : 1.3 compute capability
Total global mem : -262144 bytes
Shared block mem : 16384 bytes
RegsPerBlock : 16384
WarpSize : 32
MaxThreadsPerBlock : 512
TotalConstMem : 65536 bytes
ClockRate : 1296000 (kHz)
deviceOverlap : 1
deviceOverlap : 1
MultiProcessorCount: 30</p>
<hr />
<p>Using 1048576 particles
100 time steps</p>
<hr />
<p></pre></p>
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