In 2017 NVIDIA’s Educator GPU Grant program made specialized hardware available to several groups at the University.  Two cards, one directly given by NVIDIA to CIS and one provided by the same program and generously transferred from Electrical and Computer Engineering, were combined with assets provided by centralized IT and Steve Vittitoe, a local University benefactor. This collection allowed students and staff to build a new specialized server named

It provides MST/CIS faculty and other collaborators with a locally administered asset for complex numerical work.


  • Computation
    • Two NVIDIA Titan X Pascal GPUs (~7000 CUDA cores)
    • Two Intel – Xeon E5-2620 v4 x86-64 V4 2.1GHz 8-Core Processors (16 cores)
    • recently added: NVIDIA SLI Bridge unit to link the GPU cards [‘4 slot’]
  • Motherboard – Supermicro MBD-X10DAX EATX Dual-CPU LGA2011-3
  • Storage
    • 120 GB SSD (OS/application storage plus 64 GB swap partition)
    • 120 GB SSD (/media/faststorage)
    • 3 TB 7200 RPM Standard HDD (/media/storage)
    • 3 TB Variable-speed Standard HDD [Western Digital ‘green’] (/media/storage2)
  • Memory
    • System: 128 GB Registered DDR4-2133 SDRAM (upgrade from 64 GB initial configuration on 7/10/2018)
    • Titan X Pascal:12 GB RAM per card (24 total)
  • Power – EVGA SuperNOVA G2 1300W 80+ Gold Fully-Modular ATX PSU, attached to an external UPS unit – a CyberPower Systems LX1500GU (1500VA AVR)
  • Cooling
    • Corsair – H100i Liquid CPU Cooler
    • Corsair – H80i Liquid CPU Cooler
    • Large case and power supply fans
  • Current software:
    • OS: GNU/Linux Ubuntu 16.04.3 LTS
    • Libraries: CUDA suites v8, v9 (/usr/local/CUDA)
    • Mathematical tools: R, Maple, Matlab, Netlogo (/opt/NetLogo-6.0.2)
    • Languages: Oracle/Sun Java 9, GNU Fortran, GNU C, GNU C++, Python v2 (python); Python v3 (python3)

Particular thanks to Charles Morris, the first principal student sysadmin, who helped build the system.

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