INF5063 - nVIDIA GeForce GPU Resources & FAQ
Page for resources and frequently asked questions for the Jetson TX1 machines. If you have any other questions, please send an email to inf5063@ifi.uio.no
The following table gives an overview of the status of the ARM/CUDA machines in the lab at Simula.
Remember that you have to SSH into oslo.mlab.no (or oslo.simula.no) to access the lab-net at Simula. From here you can ssh to "tegra-x.mlab.no"
Username and password has been provided to all groups:
| Computer | GPU | GPU Core | Memory | Multiprocessors (CUDA Cores) | Compute Capability | Status |
| tegra-1.mlab.no | Tegra X1 | GM20B | 4 GB | 2 (256) | 5.3 (CUDA8) | Reserved |
| tegra-2.mlab.no | Tegra X1 | GM20B | 4 GB | 2 (256) | 5.3 | Operational |
| tegra-3.mlab.no | Tegra X1 | GM20B | 4 GB | 2 (256) | 5.3 | Operational |
| tegra-4.mlab.no | Tegra X1 | GM20B | 4 GB | 2 (256) | 5.3 | Operational |
| tegra-5.mlab.no | Tegra X1 | GM20B | 4 GB | 2 (256) | 5.3 | Down |
| tegra-6.mlab.no | Tegra X1 | GM20B | 4 GB | 2 (256) | 5.3 | Operational |
GPU Programming Resources
Paper on optimizing the Motion JPEG encoder for Cell and GPU
nVIDIA CUDA Toolkit 7.0 Documentation
Frequently Asked Questions
Q: Can I use my own GPU?
A: Yes, you can However, we do not recommend this. The program have to compile and run on the Jetson TX1 Development Kits. Your CPU code should be optimised for 64-bit ARMv8 and the GPU code should be optimised for the Maxwell architecture (Compute 5.x).
Q: What software do I need if I want to run on my own GPU?
A: Here at Simula, we are running Ubuntu 14.04 LTS (ARM 64-bit) with CUDA 7.0 from nVIDIA. You have to download both a CUDA-certified driver, and CUDA 7.0 toolkit from nVIDIA. The CUDA SDK is optional, but it contains several useful functions.