GPU Computing Introduction
Request a GPU Virtual Machine
Change your default password
Openstack GPU Virtual Machine Images
Accessing the GPU server
Backing up files
When you are finished
The SCS GPU allow you to program using its CUDA (Compute Unified Device Architecture) cores. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. The CUDA platform is accessible to software developers through CUDA-accelerated libraries, compiler directives, and extensions to industry-standard programming languages.
GPU programming has been widely used in the area of Deep learning and have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
The School has the following GPU hardware (Jan. 2022):
- NVIDIA RTX 3060 Ti, 8 GB memory, 4,864 CUDA, 152 Tensor and 38 RT cores.
- NVIDIA RTX 3060, 12 GB memory, 3,584 CUDA, 112 Tensor and 28 RT cores.
- NVIDIA Titan V, 12 GB memory, 5,120 CUDA cores, 640 Tensor cores
- GeForce RTX 2080 SUPER, 8 GB memory, 3,072 CUDA , 384 Tensor and 48 RT cores
- GeForce GTX 1080 Ti, 11 GB memory, 3,584 CUDA cores
The school has various VM flavors that assign the number of vCPU’s, memory and disk space. Virtual machines are provisioned using the school’s Openstack cloud.
Students currently taking a course that requires a GPU account can fill out this form to request a GPU VM:
You will need the following accounts to use the SCS GPU VM’s:
- MyCarletonOne account account (university wide or ITS account) – needed to VPN
- GPU VM account – created by the SCS Tech Staff
Please note the GPU account is a local account and you are responsible for your own backups!
More Account information:
Please change your default GPU VM password once you login. The simplest way to do that is to login and then in an xterminal type ‘passwd’ and follow the instructions.
Specific help for an Open Stack image:
Jan. 21, 2021
Note that you require VPN for remote accessing your instances! – Remote Access – VPN
You can access the GPU’s as follows:
- Using a remote desktop
- using a command line ssh client
Logging in using x2go (remote desktop)
The preferred way to access the GPU server is to VPN to Carleton or use a lab on campus such as the SCS Computer labs and then remote desktop (x2go) directly to the server (see diagram). The x2go session will give you a full GUI desktop. Detailed x2go instructions are here:
Logging in using ssh (command line)
Once the VM is shut down it no longer exists so its important to backup your files especially once you have completed your experiments.
Every GPU VM has an expiry date. Once you completed your GPU experiments and you have backed up your files your VM can be terminated. Please note once your VM is terminated it cannot be recovered.
If you finish your experiments before your VM expiry date please let us know! This will allow another student the opportunity to use your GPU.
To help us improve our service, please fill out this exit questionnaire once you are finished: