You have two options for developing CUDA applications for Jetson TK1:
- native compilation (compiling code onboard the Jetson TK1)
- cross-compilation (compiling code on an x86 desktop in a special way so it can execute on the Jetson TK1 target device).
Native compilation is generally the easiest option, but takes longer to compile, whereas cross-compilation is typically more complex to configure and debug, but for large projects it will be noticeably faster at compiling. The CUDA Toolkit currently only supports cross-compilation from an Ubuntu 12.04 or 14.04 Linux desktop. In comparison, native compilation happens onboard the Jetson device and thus is the same no matter which OS or desktop you have.
Installing the CUDA Toolkit onto your device for native CUDA development
Download the .deb file for the CUDA Toolkit for L4T either using a web browser on the device, or download on your PC then copy the file to your device using a USB flash stick or across the network. (Make sure you download the Toolkit for L4T and not the Toolkit for Ubuntu since that is for cross-compilation instead of native compilation). A more direct link: CUDA 6.5 Toolkit for L4T Rel 21.2.
On the device, install the .deb file and the CUDA Toolkit. eg:
cd ~/Downloads # Install the CUDA repo metadata that you downloaded manually for L4T sudo dpkg -i cuda-repo-l4t-r19.2_6.0-42_armhf.deb # Download & install the actual CUDA Toolkit including the OpenGL toolkit from NVIDIA. (It only downloads around 15MB) sudo apt-get update # Install "cuda-toolkit-6-0" if you downloaded CUDA 6.0, or "cuda-toolkit-6-5" if you downloaded CUDA 6.5, etc. sudo apt-get install cuda-toolkit-6-5 # Add yourself to the "video" group to allow access to the GPU sudo usermod -a -G video $USER
Add the 32-bit CUDA paths to your .bashrc login script, and start using it in your current console:
echo "# Add CUDA bin & library paths:" >> ~/.bashrc echo "export PATH=/usr/local/cuda/bin:$PATH" >> ~/.bashrc echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib:$LD_LIBRARY_PATH" >> ~/.bashrc source ~/.bashrc
Verify that the CUDA Toolkit is installed on your device:
(note that the above flag is a capital "V" not lower-case "v").
Installing & running the CUDA samples (optional)
If you think you will write your own CUDA code or you want to see what CUDA can do, then follow this section to build & run all of the CUDA samples.
Install writeable copies of the CUDA samples to your device's home directory (it will create a "NVIDIA_CUDA-6.5_Samples" folder):
Build the CUDA samples (takes around 15 minutes on Jetson TK1):
cd ~/NVIDIA_CUDA-6.5_Samples make
Run some CUDA samples:
cd 0_Simple/matrixMul ./matrixMulCUBLAS cd ../..
cd 0_Simple/simpleTexture ./simpleTexture cd ../..
cd 3_Imaging/convolutionSeparable ./convolutionSeparable cd ../..
cd 3_Imaging/convolutionTexture ./convolutionTexture cd ../..
Note: Many of the CUDA samples use OpenGL GLX and open graphical windows. If you are running these programs through an SSH remote terminal, you can remotely display the windows on your desktop by typing "export DISPLAY=:0" and then executing the program. (This will only work if you are using a Linux/Unix machine or you run an X server such as the free "Xming" for Windows). eg:
export DISPLAY=:0 cd ~/NVIDIA_CUDA-6.5_Samples/2_Graphics/simpleGL ./simpleGL cd ~/NVIDIA_CUDA-6.5_Samples/3_Imaging/bicubicTexture ./bicubicTexture cd ~/NVIDIA_CUDA-6.5_Samples/3_Imaging/bilateralFilter ./bilateralFilter
Note: the Optical Flow sample (HSOpticalFlow) and 3D stereo sample (stereoDisparity) take rouglhy 1 minute each to execute since they compare results with CPU code.
Some of the CUDA samples use other libraries such as OpenMP or MPI or OpenGL.
If you want to compile those samples then you'll need to install these toolkits like this:
(to be added)