List of Servers
| Name | Alias | Hardware | Operating System | |
|---|---|---|---|---|
| linuxg5 | linux19 | 1 x AMD EPYC 7453 28-core with 128GB RAM 2 x NVIDIA A100 GPU |
Rocky Linux 8 | |
| linuxg4 | linux18 | 1 x AMD EPYC 7662 64-Core Processor 128GB RAM 1 x NVIDIA A100 GPU |
Rocky Linux 8 | GPU set in multi-instance mode. See here for more info. |
| linuxg3 | linux17 | 1 x AMD EPYC 16-core with 64GB RAM 2 x NVIDIA Tesla V100S GPU |
Rocky Linux 8 | |
| linuxg1 | linux15 | 1 x Intel Xeon E5-2683 16-core (2.1GHz) with 128GB RAM 1 x NVIDIA Tesla P100 GPU |
Rocky Linux 8 | linuxe4 | linux10 | 2 x Intel Xeon E5-2630 Six core (2.6GHz) with 64GB RAM | Rocky Linux 8 |
| linuxe3 | linux9 | 2 x Intel Xeon E5-2630 Six core (2.6GHz) with 64GB RAM | Rocky Linux 8 | |
| linuxe2 | linux8 | 2 x Intel Xeon E5-2630 Six core (2.6GHz) with 64GB RAM | Rocky Linux 8 | |
| linuxe1 | linux7 | 2 x Intel Xeon E5-2630 Six core (2.6GHz) with 64GB RAM | Rocky Linux 8 | |
| linuxb1 | linux6 | 1 x AMD EPYC 7351 16 Core (2.4GHz) with 128GB RAM | Rocky Linux 8 | |
| linuxa5 | linux5 | 2 x Intel Xeon Fourteen Core (2.4GHz) with 256GB RAM | Rocky Linux 8 | |
| linuxa4 | linux4 | 2 x Intel Xeon Fourteen Core (2.4GHz) with 256GB RAM | Rocky Linux 8 | |
| linuxa3 | linux3 | 2 x Intel Xeon Fourteen Core (2.4GHz) with 256GB RAM | Rocky Linux 8 | |
| linuxa2 | linux2 | 2 x Intel Xeon Fourteen Core (2.4GHz) with 256GB RAM | Rocky Linux 8 | |
| linuxa1 | linux1 | 2 x Intel Xeon Fourteen Core (2.4GHz) with 256GB RAM | Rocky Linux 8 |
Software
Server programs are installed at the request of Faculty and Staff. Some general software is available such as:
- ANSYS
- MATLAB
- Common GNU compilers such as c/c++ and g77.
- Anaconda and Python 2.7 and 3.X
Most of the software can be found in the usual directory locations and /usr/local, but contact us for more info or for more information on specific program availability if needed.
Multi-Instance GPU
The Multi-Instance GPU (MIG) feature allows GPUs be partitioned into separate instances. To identify and choose the instance you’d like to use:
1. Identify the instances available:
nvidia-smi -L
2. Set your CUDA_VISIBLE_DEVICES environment variable to the instance you’d like to run on.
If using tcsh
setenv CUDA_VISIBLE_DEVICES MIG-xxx-xxx-xxx-xxx-xxx
If using bash
export CUDA_VISIBLE_DEVICES=MIG-xxx-xxx-xxx-xxx-xxx
Then run your code as normal. For more information, see here.