GPU servers and parallel hardware at RGNC

Contents




1. Servers

We currently hold three GPU computing servers, named after the three highest peaks in Spain.

Note: We are still working on the configuration of the servers, so more libraries are going to be installed soon...


Teide:

  • Even gaming
  • CPU: 8 cores (2-socket 4-core) Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz
  • Main memory: 32 GBytes DDRR4 @ 2133 MHz
  • Disk memory: 354GB SSD for /home, 1TB HDD for /data (please, store here your datasets), 100GB SSD for /
  • GPUs:
    • 2 x NVIDIA RTX2080 - 2944 cores (46 SMs x 64 SPs) @ 1.85Ghz, 8 GBytes GDDR5 (provided by our R&D project MABICAP)
    • CUDA 11.3
  • Operating system: Linux CentOS 7.9 64 bits
  • Software libraries through CernVM-FS


Mulhacen:

  • Even Server Xeon
  • CPU: 4 cores (with Hyperthreading, up to 8 "virtual" cores) Intel i5 Xeon E3-1230V3 @ 3.30GHz
  • Main memory: 32 GBytes DDR3 @ 2400Mhz
  • Disk memory: 840GB HDD for /home, 100GB SSD for /
  • GPUs:
    • 1 x NVIDIA Tesla K40c - 2880 cores (15 SMXs x 192 SPs) @ 0.88Ghz, 12 GBytes GDDR3 (provided by NVIDIA under the CUDA Research Center program)
    • 1 x NVIDIA GeForce GTX 780 Ti - 2880 cores (15 SMXs x 192 SPs) @ 0.93Ghz, 3 GBytes GDDR3 (provided by our R&D projects).
    • CUDA 10.1
  • Operating system: Linux CentOS 7.9 64 bits
  • Software libraries through CernVM-FS


Aneto (login node):

  • Supermicro Server 7046GT-TRF
  • CPU: 8 cores (2-socket 4-core) Intel i5 Nehalem E5504 @ 2.00GHz
  • Main memory: 12 GBytes DDR3 @ 1333Mhz
  • Disk memory: 175GB HDD for /home, 230GB HDD for /data (please, store here your datasets), 56GB HDD for /
  • GPUs:
    • 3 x NVIDIA Tesla C1060 - 240 cores (30 SMs x 8 SPs) @ 1.30Ghz, 4 GBytes GDDR3 (provided by our R&D projects)
    • 1 x NVIDIA GeForce GTX 550 Ti - 192 cores (4 SMs x 48 SPs) @ 1.90Ghz, 1 GBytes GDDR3 (provided by Manuel García). Sorry, this GPU was removed temporally to be used in other PC.
    • CUDA 6.5
  • Operating system: Linux CentOS 7.9 64 bits
  • Software libraries through CernVM-FS

Linux

CentOS

Ubuntu

NVIDIA CUDA

CernVM File System

Slurm




2. GPU for mobility hardware


Jetson TX2 Development Kit:

  • GPU: NVIDIA Pascal GPU with 256 CUDA cores
  • CPUs: 64-bit NVIDIA Denver and ARM Cortex-A57
  • Main memory: 8GB LPDDR4




3. Funding and Acknowledgments

The Teide GPU server at RGNC was provided by MABICAP project: FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación/ _Proyecto (TIN2017-89842-P).

The Mulhacen GPU server at RGNC was provided by the R&D project TIN2012-37434 (funded by Ministerio de Economía y Competitividad of Gobierno de España), co-financed by the European FEDER funds. The Tesla K40c GPU was a donation by NVIDIA under the CUDA Research Center program.

The Aneto GPU server at RGNC was provided by the R&D projects P08-TIC4200 (funded by Consejería de Economía, Innovación y Ciencia of Junta de Andalucía) and TIN2009-13192 (funded by Ministerio de Ciencia e Innovación of Gobierno de España), both co-financed by the European FEDER funds.

Please, consider to acknowledge our R&D projects in your papers if you are using our GPU servers and hardware for your research (also notify us about this).