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 a University benefactor. This collection allowed students and staff to build a new specialized server named

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


  • Computation
    • Two NVIDIA Titan X Pascal GPU cards (~7000 CUDA cores)
    • Two Intel – Xeon E5-2620 v4 x86-64 V4 2.1GHz 8-Core Processors (16 cores)
  • Motherboard – Supermicro MBD-X10DAX EATX Dual-CPU LGA2011-3
  • Storage
    • 3 TB 7200 RPM Standard HDD (/media/storage)
    • 120 GB SSD (OS/applications; 64 GB swap partition)
  • Memory
    • system: 64 GB registered DDR4-2133 system
    • Titan X Pascal:12 GB RAM per card (24 total)
  • Power – EVGA SuperNOVA G2 1300W 80+ Gold Fully-Modular ATX PSU
  • Cooling
    • Corsair – H100i Liquid CPU Cooler
    • Corsair – H80i 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)
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