INF5063 - nVIDIA GeForce GPU Resources & FAQ
Page for resources and frequently asked questions for the GPU machines. If you have any other questions, please send an email to inf5063@ifi.uio.no
The following talbe gives an overview of the status of the nVIDIA GPU machines in the lab at Simula. All nVIDIA GPU's from the GeForce 8/9/200/400/500/600 generation, both for desktop and laptop supports CUDA, and it is therefore possible to use your own hardware.
Remember that you have to SSH into oslo.simula.no to access the lab-net here at Simula. Username and password has been provided to all groups:
| Computer | GPU | GPU Core | Multiprocessors (CUDA Cores) | Compute Capability | Status |
| kennedy.ndlab.net | GeForce GTX 650* | GK107 | 2 (384) | 3.0 | Operational |
| clinton.ndlab.net | GeForce GTX 650* | GK107 | 2 (384) | 3.0 | Operational |
| bush.ndlab.net | GeForce GTX 650* | GK107 | 2 (384) | 3.0 | Down |
| gpu-5.ndlab.net | Quadro 600 | GF108 | 2 (96) | 2.1 | Operational |
| gpu-6.ndlab.net | Quadro 600 | GF108 | 2 (96) | 2.1 | Operational |
| gpu-7.ndlab.net | Quadro 600 | GF108 | 2 (96) | 2.1 | Operational |
| gpu-8.ndlab.net | Quadro 600 | GF108 | 2 (96) | 2.1 | Operational |
* clinton.ndlab.net, bush.ndlab.net and kennedy.ndlab.net have two GPU's (supports cuda-gdb). Use "Device 0" for computing.
Please note that only one person in a group may reserve a timeslot (max 4 hours) at a time! The reason for this is to allow other user access to the GPU machines as well. We have a reservation system in place for the GPU machines here at Simula:
The login to open the page is the same you use to access the Home Exams. Remember to log into the calendar with the username and password provided to your group.
GPU Programming Resources
Paper on optimizing the Motion JPEG encoder for Cell and GPU
nVIDIA CUDA Toolkit 4.0 Download & Documentation
nVIDIA OpenCL 1.0 Download & Documentation
Simple Motion JPEG Encoder with 2D-DCT offloaded to CUDA (2009-Edition)
Frequently Asked Questions
Q: Can I use my own GPU?
A: Yes, you can! CUDA works on all GeForce 8/9/100/200/400/500/600-series GPU's from nVIDIA. OpenCL should work on any nVIDIA or AMD GPU with an OpenCL driver, make sure that your program works on lab-machines! If you use functions that requires a special "Compute Version", you have to state this in your documentation.
Q: What software do I need if I want to run on my own GPU?
A: Here at Simula, we are running Ubuntu 12.04 LTS (64-bit) with CUDA 5.0 from nVIDIA. You have to download both a CUDA-certified driver, and CUDA 5.0 toolkit from nVIDIA. The CUDA SDK is optional, but it contains several usefull functions.
Q: Can you give us an example of a GPU program?
A: Yes. We can! We have made availible a simple version of last years Motion JPEG encoder (Week Assignment 2) with only DCT offloaded to the GPU. Note that this implementation uses the same DCT algorithm found the first home exam (2D-DCT). This is different from the 1D-DCT found the precode of Home Exam 2. You can find the encoder HERE.
Q: Do OpenCL and CUDA give the same performance?
A: There are some differences. Have a look at this detailed comparison.